saimm 201410 oct

VOLUME 114 NO. 10 OCTOBER 2014

The MSA

explorand esolutions t

MSA Johannesb+27 (0)11 880 [email protected]

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ii OCTOBER 2014 The Journal of The Southern African Institute of Mining and Metallurgy

OFFICE BEARERS AND COUNCIL FOR THE2014/2015 SESSION

Honorary PresidentMike TekePresident, Chamber of Mines of South Africa

Honorary Vice-PresidentsNgoako RamatlhodiMinister of Mineral Resources, South AfricaRob DaviesMinister of Trade and Industry, South AfricaNaledi PandorMinister of Science and Technology, South Africa

PresidentJ.L. Porter

President ElectR.T. Jones

Vice-PresidentsC. MusingwiniS. Ndlovu

Immediate Past PresidentM. Dworzanowski

Honorary TreasurerC. Musingwini

Ordinary Members on Council

V.G. Duke T. PegramM.F. Handley S. RupprechtA.S. Macfarlane N. SearleM. Motuku A.G. SmithM. Mthenjane M.H. SolomonD.D. Munro D. TudorG. Njowa D.J. van Niekerk

Past Presidents Serving on CouncilN.A. Barcza J.C. NgomaR.D. Beck S.J. Ramokgopa J.A. Cruise M.H. RogersJ.R. Dixon G.L. SmithF.M.G. Egerton J.N. van der MerweG.V.R. Landman W.H. van NiekerkR.P. Mohring

Branch ChairmenDRC S. MalebaJohannesburg I. AshmoleNamibia N. NamatePretoria N. NaudeWestern Cape C. DorflingZambia H. ZimbaZimbabwe E. MatindeZululand C. Mienie

Corresponding Members of CouncilAustralia: I.J. Corrans, R.J. Dippenaar, A. Croll,

C. Workman-DaviesAustria: H. WagnerBotswana: S.D. WilliamsBrazil: F.M.C. da Cruz VieiraChina: R. OppermannUnited Kingdom: J.J.L. Cilliers, N.A. Barcza, H. PotgieterUSA: J-M.M. Rendu, P.C. PistoriusZambia: J.A. van Huyssteen

The Southern African Institute of Mining and Metallurgy

PAST PRESIDENTS*Deceased

* W. Bettel (1894–1895)* A.F. Crosse (1895–1896)* W.R. Feldtmann (1896–1897)* C. Butters (1897–1898)* J. Loevy (1898–1899)* J.R. Williams (1899–1903)* S.H. Pearce (1903–1904)* W.A. Caldecott (1904–1905)* W. Cullen (1905–1906)* E.H. Johnson (1906–1907)* J. Yates (1907–1908)* R.G. Bevington (1908–1909)* A. McA. Johnston (1909–1910)* J. Moir (1910–1911)* C.B. Saner (1911–1912)* W.R. Dowling (1912–1913)* A. Richardson (1913–1914)* G.H. Stanley (1914–1915)* J.E. Thomas (1915–1916)* J.A. Wilkinson (1916–1917)* G. Hildick-Smith (1917–1918)* H.S. Meyer (1918–1919)* J. Gray (1919–1920)* J. Chilton (1920–1921)* F. Wartenweiler (1921–1922)* G.A. Watermeyer (1922–1923)* F.W. Watson (1923–1924)* C.J. Gray (1924–1925)* H.A. White (1925–1926)* H.R. Adam (1926–1927)* Sir Robert Kotze (1927–1928)* J.A. Woodburn (1928–1929)* H. Pirow (1929–1930)* J. Henderson (1930–1931)* A. King (1931–1932)* V. Nimmo-Dewar (1932–1933)* P.N. Lategan (1933–1934)* E.C. Ranson (1934–1935)* R.A. Flugge-De-Smidt

(1935–1936)* T.K. Prentice (1936–1937)* R.S.G. Stokes (1937–1938)* P.E. Hall (1938–1939)* E.H.A. Joseph (1939–1940)* J.H. Dobson (1940–1941)* Theo Meyer (1941–1942)* John V. Muller (1942–1943)* C. Biccard Jeppe (1943–1944)* P.J. Louis Bok (1944–1945)* J.T. McIntyre (1945–1946)* M. Falcon (1946–1947)* A. Clemens (1947–1948)* F.G. Hill (1948–1949)* O.A.E. Jackson (1949–1950)* W.E. Gooday (1950–1951)* C.J. Irving (1951–1952)* D.D. Stitt (1952–1953)* M.C.G. Meyer (1953–1954)

* L.A. Bushell (1954–1955)* H. Britten (1955–1956)* Wm. Bleloch (1956–1957)* H. Simon (1957–1958)* M. Barcza (1958–1959)* R.J. Adamson (1959–1960)* W.S. Findlay (1960–1961)

D.G. Maxwell (1961–1962)* J. de V. Lambrechts (1962–1963)* J.F. Reid (1963–1964)* D.M. Jamieson (1964–1965)* H.E. Cross (1965–1966)* D. Gordon Jones (1966–1967)* P. Lambooy (1967–1968)* R.C.J. Goode (1968–1969)* J.K.E. Douglas (1969–1970)* V.C. Robinson (1970–1971)* D.D. Howat (1971–1972)

J.P. Hugo (1972–1973)* P.W.J. van Rensburg (1973–1974)* R.P. Plewman (1974–1975)

R.E. Robinson (1975–1976)* M.D.G. Salamon (1976–1977)* P.A. Von Wielligh (1977–1978)* M.G. Atmore (1978–1979)* D.A. Viljoen (1979–1980)* P.R. Jochens (1980–1981)

G.Y. Nisbet (1981–1982)A.N. Brown (1982–1983)

* R.P. King (1983–1984)J.D. Austin (1984–1985)H.E. James (1985–1986)H. Wagner (1986–1987)

* B.C. Alberts (1987–1988)C.E. Fivaz (1988–1989)O.K.H. Steffen (1989–1990)

* H.G. Mosenthal (1990–1991)R.D. Beck (1991–1992)J.P. Hoffman (1992–1993)

* H. Scott-Russell (1993–1994)J.A. Cruise (1994–1995)D.A.J. Ross-Watt (1995–1996)N.A. Barcza (1996–1997)R.P. Mohring (1997–1998)J.R. Dixon (1998–1999)M.H. Rogers (1999–2000)L.A. Cramer (2000–2001)

* A.A.B. Douglas (2001–2002)S.J. Ramokgopa (2002-2003)T.R. Stacey (2003–2004)F.M.G. Egerton (2004–2005)W.H. van Niekerk (2005–2006)R.P.H. Willis (2006–2007)R.G.B. Pickering (2007–2008)A.M. Garbers-Craig (2008–2009)J.C. Ngoma (2009–2010)G.V.R. Landman (2010–2011)J.N. van der Merwe (2011–2012)G.L. Smith (2012–2013)

Honorary Legal AdvisersVan Hulsteyns Attorneys

AuditorsMessrs R.H. Kitching

Secretaries

The Southern African Institute of Mining and MetallurgyFifth Floor, Chamber of Mines Building5 Hollard Street, Johannesburg 2001P.O. Box 61127, Marshalltown 2107Telephone (011) 834-1273/7Fax (011) 838-5923 or (011) 833-8156E-mail: [email protected]

Contentsby W. Joughin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPresident’s Corner by J.L. Porter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Extending empirical evidence through numerical modelling in rock engineering designby G.S. Esterhuizen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755Time-dependent tensile strengths of Bushveld Complex rocks and implications for rock failurearound mining excavationsby D. Nyungu and T.R. Stacey. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765Fan-structure shear rupture mechanism as a source of shear rupture rockburstsby B.G. Tarasov. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773Unique fall-of-ground prevention strategy implemented at Two Rivers Platinum Mineby A. Esterhuizen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785In situ monitoring of primary roofbolts at underground coal mines in the USAby A.J.S. Spearing and A. Hyett. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791Pillar behaviour and seismicity in platinum minesby S.M. Spottiswoode and M. Drummond . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 801Management of the Nkomati Mine crusher slope failureby R. Armstrong and K. Moletsane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811Grid-based analysis of seismic databy J. Wesseloo, K. Woodward, and J. Pereira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815Evaluation of the spatial variation of b-valueby J. Wesseloo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823Testing tendon support units under a combination loading scenarioby N.L. Ayres and L.J. Gardner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829Estimation of future ground vibration levels in Malmberget town due to mining-induced seismic activityby T. Wettainen and J. Martinsson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 835

Outsourcing in the mining industry: decision-making framework and critical success factorsby C.J.H. Steenkamp and E. van der Lingen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845Focal depths of South African earthquakes and mine eventsby M.B.C. Brandt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855Design and positive financial impact of crush pillars on mechanized deep-level mining at South Deep Gold Mineby B.P. Watson, W. Pretorius, P. Mpunzi, M. du Plooy, K. Matthysen, and J.S. Kuijpers . . . . . . 863The application of geophysics in South African coal mining and explorationby M. van Schoor and C.J.S. Fourie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 875

International Advisory Board

R. Dimitrakopoulos, McGill University, CanadaD. Dreisinger, University of British Columbia, CanadaE. Esterhuizen, NIOSH Research Organization, USAH. Mitri, McGill University, CanadaM.J. Nicol, Murdoch University, AustraliaH. Potgieter, Manchester Metropolitan University, United KingdomE. Topal, Curtin University, Australia

The Journal of The Southern African Institute of Mining and Metallurgy OCTOBER 2014


▲iii

Editorial BoardR.D. BeckJ. Beukes

P. den HoedM. Dworzanowski

M.F. HandleyR.T. Jones

W.C. JoughinJ.A. LuckmannC. MusingwiniR.E. Robinson

T.R. StaceyR.J. Stewart

Editorial ConsultantD. Tudor

Typeset and Published byThe Southern African Instituteof Mining and MetallurgyP.O. Box 61127Marshalltown 2107Telephone (011) 834-1273/7Fax (011) 838-5923E-mail: [email protected]

Printed by Camera Press, Johannesburg

AdvertisingRepresentativeBarbara SpenceAvenue AdvertisingTelephone (011) 463-7940E-mail: [email protected] SecretariatThe Southern AfricanInstitute of Mining andMetallurgyISSN 2225-6253

SSEESSSS

Institute of Mining and Metallurgy. Allrights reserved. Multiple copying of thecontents of this publication or partsthereof without permission is in breach ofcopyright, but permission is hereby givenfor the copying of titles and abstracts ofpapers and names of authors. Permissionto copy illustrations and short extractsfrom the text of individual contributions isusually given upon written application tothe Institute, provided that the source (andwhere appropriate, the copyright) isacknowledged. Apart from any fair dealingfor the purposes of review or criticismunder The Copyright Act no. 98, 1978,Section 12, of the Republic of SouthAfrica, a single copy of an article may besupplied by a library for the purposes ofresearch or private study. No part of thispublication may be reproduced, stored ina retrieval system, or transmitted in anyform or by any means without the priorpermission of the publishers. Multiplecopying of the contents of the publicationwithout permission is always illegal.

U.S. Copyright Law applicable to users Inthe U.S.A.The appearance of the statement ofcopyright at the bottom of the first page ofan article appearing in this journalindicates that the copyright holderconsents to the making of copies of thearticle for personal or internal use. Thisconsent is given on condition that thecopier pays the stated fee for each copy ofa paper beyond that permitted by Section107 or 108 of the U.S. Copyright Law. Thefee is to be paid through the CopyrightClearance Center, Inc., Operations Center,P.O. Box 765, Schenectady, New York12301, U.S.A. This consent does notextend to other kinds of copying, such ascopying for general distribution, foradvertising or promotional purposes, forcreating new collective works, or forresale.


6th Southern African Rock Engineering Symposium

General Papers

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iv OCTOBER 2014 The Journal of The Southern African Institute of Mining and Metallurgy

Journal Comment

The Journal of The Southern African Institute of Mining and Metallurgy OCTOBER 2014 ▲v

Presidentʼs

Corner

PAPERS IN THIS EDITIONThese papers have been refereed and edited according to internationally accepted standards and

are accredited for rating purposes by the South African Department of Higher Education andTraining

These papers will be available on the SAIMM websitehttp://www.saimm.co.za

6th Southern African Rock Engineering Symposium 2014by G.S. Esterhuizen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755The use of numerical models to answer questions about specific aspects of excavation stability that existing empirical models were unable to provide is illustrated in two case studies. It is shown that the combined application of numerical and empirical models can help to improve understanding of causes of stability and instability in excavations, resulting in efficient design and increased safety.

by D. Nyungu and T.R. Stacey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 765Numerical modelling has shown that large zones of extension strain can occur around excavations in the Bushveld Complex, and that the magnitudes of the extension strain can substantially exceed the critical values determined fromlaboratory testing. These zones may thus be prone to time-dependent spalling, and perhaps more significant failure.

by B.G. Tarasov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773Physical and mathematical models have shown that shear ruptures can propagate through a highly confined intact rock mass at shear stresses significantly less than the frictional strength. The failure process is inevitably spontaneous and violent. The fan mechanism allows a novel point of view for understanding the nature of spontaneous failure processes, including shear rupture rockbursts.

by A. Esterhuizen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785Since 2005, Two Rivers Platinum Mine has actively monitored and controlled ground conditions on a daily basis bymaking use of borehole cameras and day-to-day observations of hangingwall conditions. The strategy has greatly reduced the mine’s fall- of-ground frequency and size, while effectively controlling support costs.

by A.J.S. Spearing and A. Hyett . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791A study was conducted to assess the performance of primary roofbolts in three underground coal mines in the USA. The results showed that there was no evidence to indicate a difference in performance of active primary roof bolts compared with passive primary roofbolts.

by S.M. Spottiswoode and M. Drummond. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 801Violent failure of crush pillars is said to be the main cause of seismicity associated with mining of the Merensky Reef. This paper reports the first results from a new suite of programs to model pillars in platinum mines. The ultimate aim is to provide a tool for on-mine rock engineers to interpret current and planned mining geometry by extrapolating comparisons of historical modelled and observed seismicity into the future for better and safer mining.

by R. Armstrong and K. Moletsane. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811This paper describes the events leading up to a minor slope failure above the crusher at Nkomati nickel mine. Real-time monitoring was deployed, and the monitoring data determined a management plan for the failure that resulted in minimal shutdowns of the primary crusher.

by J. Wesseloo, K. Woodward, and J. Pereira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815A grid- based interpretation of seismic data and some examples of results obtained with the method are presented.Grid- based interpretation allows the spatial variation of seismic source parameters to be evaluated without predetermined analysis volumes. As such, it reduces interpretation bias.

PAPERS IN THIS EDITIONThese papers have been refereed and edited according to internationally accepted standards and

are accredited for rating purposes by the South African Department of Higher Education andTraining

These papers will be available on the SAIMM websitehttp://www.saimm.co.za

6th Southern African Rock Engineering Symposium 2014Evaluation of the spatial variation of b-valueby J. Wesseloo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823The interpretation of b-value of the Gutenberg-Richter relationship is important for both the general interpretation of the mechanism of rock mass response and seismic hazard assessment. This paper discusses the algorithm for spatially sub-sampling the data as well as the algorithm for obtaining the magnitude of completeness mmin and b-value for every spatial sub-sample.Testing tendon support units under a combination loading scenarioby N.L. Ayres and L.J. Gardner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829Tendon support units were tested at different installation angles to establish the performance, mechanical behaviour, and load capacity under different combinations of tensile and shear forces. The results are aimed at improving understanding of how tendons perform under these conditions.Estimation of future ground vibration levels in Malmberget town due to mining-induced seismic activityby T. Wettainen and J. Martinsson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 835This paper describes investigations into how seismicity will change as production increases at the Malmberget underground iron-ore mine in northern Sweden, and what possible measures could be taken to reduce inconvenience to the residents of the town, which partly overlies the orebodies. Relations between historical seismic events and measured ground vibrations in the town were established, and future ground vibrations caused by expected seismic events were estimated using a probabilistic approach.

General PapersOutsourcing in the mining industry: decision-making framework and critical success factorsby C.J.H. Steenkamp and E. van der Lingen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845A study was conducted to determine whether mining is truly a core competency for a mid-tier commodity specialist mining company. A decision-making framework for mining operations outsourcing was developed and the critical success factors were determined. It is shown that the most important tools at the disposal of a mine owner’s team to manage a contract miner are the social and output control mechanisms.Focal depths of South African earthquakes and mine eventsby M.B.C. Brandt. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855The focal depths of 15 tectonic earthquakes and 9 mine-related events were determined using data recorded by the South African National Seismograph Network. Focal depths were estimated for nine stations by visually comparing synthetic waveform phases with recorded waveforms. The results confirm the assumption that focal depths of South African earthquakes and mine-related events are shallow — within the upper third (0 km to 10 km) of the crust.Design and positive financial impact of crush pillars on mechanized deep-level mining at South Deep Gold Mineby B.P. Watson, W. Pretorius, P. Mpunzi, M. du Plooy, K. Matthysen, and J.S. Kuijpers. . . . . . . . . . . . . . . . . . . . . . . . . . 863Crush pillars have been incorporated into a mechanized, low-profile trackless system at South Deep Gold Mine. The introduction of these pillars has improved the rockmass conditions because of the active nature of the support, compared to the previous passive backfill method. Importantly, the pillars have increased mining efficiencies and improved face availability.The application of geophysics in South African coal mining and explorationby M. van Schoor and C.J.S. Fourie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 875Geophysics can play a significant role in addressing a wide range of problems in coal mining and exploration. This paper provides a brief overview of a textbook compiled by Coaltech to guide the application of geophysics to coal mining problems in South Africa, using key sections and selected examples to highlight the value of geophysical techniques.

IntroductionThe design of excavations in rock is extremelychallenging because of the variability of therock materials, uncertainty about the loadingconditions, and the need for cost-effectivesolutions. Empirical models such as pillarstrength equations, excavation stability charts,and classification-based support design chartsare widely used in design. These models arepopular because they reduce the complexity ofthe systems into simple charts or equationsthat can readily be applied. Numerical models,on the other hand, are built upon themechanics of materials and can be used toconduct experiments in which unknownsituations are studied. However, numericalmodels need to be grounded in reality, whichcan be achieved only through calibration andvvalidation against empirical evidence. The

synergy between empirical and numericalmodels has helped to improve the effectivenessof both types of models.

This paper discusses modelling inengineering design and examines the varioustypes of models that are used in rockengineering. The synergy between empiricaland numerical models is discussed, and twocase studies are presented in which empiricalmodels were used to validate numerical modelsand the numerical model outcomes were usedto supplement empirically based designmethods.

Models as tools in engineering designModels of various types are used by engineersand scientists to represent reality in a logicaland objective way (Frigg and Hartmann,2006). Models allow us to investigate thebehaviour and attributes of a system and canbe useful for predicting the response of thesystem under a different set of conditions. Amodel can be a physical representation ofreality, usually on a reduced scale; or anabstraction such as a set of equations thatreplicate the system behaviour. Hammah andCurran (2009) state that although models aresimplified reflections of reality, they are usefulfor:

➤ Developing an understanding➤ Proper formulation of questions➤ Providing an approximation of

behaviour➤ Providing meaningful predictions➤ Aiding in design and decision-making.

Extending empirical evidence throughnumerical modelling in rock engineeringdesignby G.S. Esterhuizen*

SynopsisModels are used in engineering to reproduce reality as faithfully as possibleso that the expected response of a system for given actions or inputs can bedetermined. In the field of rock engineering, both empirically based andnumerical models are widely used to determine the likely response of therock surrounding excavations. Many of the empirical models are developedfrom statistical analysis of case histories or from direct observation;however, empirical models are limited because they should be used onlywithin the range of conditions of the observational database. Synergyexists between empirical and numerical models, because empirical modelscan be used to calibrate and validate numerical models. The empiricalapproach can benefit from the capability of numerical models to investigatespecific mechanisms, which would not be possible using observationsalone. Two cases are presented in which the synergy between empirical andnumerical models is demonstrated. The first case examines the analysis ofdiscontinuity effects on the strength of slender pillars in limestone mines,and the second case evaluates the effects of stress orientation on coal mineentry stability. It is concluded that numerical model calibration and verifi-cation comprises an important first stage in the successful application ofmodels in rock engineering design. Application of numerical models allowsmechanisms and interactions of various parameters to be analysed, greatlyimproving the understanding of the system. The improved understandingcan be used to extend the application of empirical design methods,resulting in improved safety and efficiency of rock engineering designs.

Keywordsrock engineering, empirical design, numerical modelling.

* National Institute for Occupational Safety andHealth, Pennsylvania.

© The Southern African Institute of Mining andMetallurgy, 2014. ISSN 2225-6253. This paperwas first presented at the, 6th Southern AfricanRock Engineering Symposium SARES 2014,12–14 May 2014, Misty Hills Country Hotel andConference Centre, Cradle of Humankind,Muldersdrift.

755The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 114 OCTOBER 2014 ▲

Extending empirical evidence through numerical modelling in rock engineering design

fThe insight that comes from evaluating model responsesgreatly helps us in understanding the problem and thedevelopment of engineering solutions. Often, the act ofsimply creating a model can lead to greater insight into theproblem and point towards a solution.

AAccuracy of modelsWWhen creating a model it is always necessary to make someassumptions and simplifications. The process of modelcreation requires judgments about which factors areimportant and should be included in the model and whichfactors can be excluded. The result is that models are asimplification of the real system and thus the model resultswwill be approximate. With ever-increasing computationalcapabilities, the temptation is to build increasingly complexmodels that include as many mechanisms as possible.However, added complexity does not necessarily result inimproved accuracy. Every new parameter included in a modelintroduces new uncertainty (Curran and Hammah, 2006).Thequote attributed to Einstein comes to mind: ‘Make things assimple as possible, but no simpler’. A model might be overlysimplistic if it does not simulate an important mechanism thatdetermines system response. For example, if post-failuredeformation of rock plays an important role in supportdesign, a model that does not simulate post-failure mechanicswwill be insufficient for the intended application. A modelshould therefore endeavour to capture the essence of thesystem under consideration without unnecessary compli-cation. Overly complex models can become difficult tounderstand and costly or impractical to use.

Calibration and verificationOnce a model has been created it is necessary to conductcalibration and validation studies. The calibration processinvolves improving the agreement of the model with respectto a chosen set of benchmarks through the adjustment ofparameters in the model (Trucano et al., 2006). In rockengineering practice, a model is often calibrated against fieldmonitoring data. During the calibration process some of theless well-defined inputs may be modified to achieve greateragreement with the field-measured deformations or otherresponses. A well-known calibration parameter that needs tobe applied in almost every rock engineering model is a factorto account for the reduction in rock strength with increasingscale (Heuze, 1980; Hoek and Brown, 1980).

Validation addresses the question of whether a modelproduces correct results for its intended application (Thackeret al., 2004; Trucano et al., 2006). Validation involvescomparing calibrated model outputs to experimental or otherempirical outcomes. During validation, the range ofconditions in which the model can provide accuratepredictions can be established. In rock engineering, calibratedmodels can be validated against field monitoring data fromalternative experimental sites that was not used in thecalibration stage. However, there is a general lack of largenumbers of field experimental results, because of the cost anddifficulty of conducting such experiments. An alternativeapproach is to compare model results to empirically derivedrelationships that describe the average response of anexcavation or structure. For example, empirically derivedpillar strength equations can be used to determine the validity

fof a pillar model (Martin and Maybee, 2000; Lunder, 1994;Esterhuizen, 2006; Roberts et al., 2007).

Modelling approach with limited dataIn rock engineering, numerical modelling is usuallyconducted with limited data. The field stresses, materialproperties, and discontinuities in the rock are poorlydescribed and are variable. When data is available, it usuallyconsists of point representations conducted on a very smallvolume of the problem domain and the natural variability ofthe parameters is not known. Starfield and Cundall, (1988)discuss an approach for modelling of data-limited problems.Under such circumstances, there is no point in constructinglarge and complicated models. Models should rather besimple and should be used to educate the design engineer byproviding insight into the possible mechanisms. The modelscan be used as an experimental test bench, to aid in designand decision-making, rather than being expected to provideabsolute design data.

Models in rock engineeringModels have been extensively used since the inception of therock engineering discipline in the 1960s. For example, photo-elastic models and centrifuge models were widely used tounderstand the distribution of stress and deformationsaround excavations and to model failure under elevatedloading conditions (Bieniawski and van Tonder, 1969;Hudson et al., 1972) In coal mining, large physical modelswere created to investigate the response of coal measurerocks to mining excavations (Hucke et al., 2006; Yeats et al.,1983), while large physical models of rock flow have beenused for the design of caving mines (Kvapil, 1992;Laubscher, 2001; Power, 2004). With the development ofhigh-speed computers, physical models have largely beenreplaced by numerical models of increasing complexity.Currently, numerical models are used in every area of rockengineering. Software products are available that are able tomodel all kinds of rock structures, including rock at the grainscale (Potyondy and Cundall, 2004; Cho et al., 2007) and upto large-scale rock masses that include multiple disconti-nuities and intact rock fragments (Elmo and Stead, 2010;Mas Ivars et al., 2007).

Empirically based modelsA special type of model that is sometimes called an ‘empiricalmodel’ has been widely used in rock engineering. Thesemodels are usually relatively simple mathematical equationsthat are based on a conceptual understanding of the systemunder consideration supplemented by statistical analysis ofrecorded performance of excavations or structures in rock.The success of empirical models requires a goodunderstanding of the problem at hand, usually through yearsof trial-and-error experimentation during which theunderlying relationships between variables are discovered.Alternatively, statistical analyses of large numbers of casehistories can be used to determine how the parameters arerelated.

Examples of statistically based empirical models are thewidely used pillar strength equations that were developedduring the 1960s (Salamon and Munro, 1966; Bieniawski,1968). These strength equations are based on the

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756 OCTOBER 2014 VOLUME 114 The Journal of The Southern African Institute of Mining and Metallurgy

funderstanding that the strength of rock pillars depends onthe strength of the intact rock and the width-to-height ratioof the pillar. Empirical evidence was collected from casehistories in the field or by physical experimentation.Statistical techniques were used to determine the best fit ofthe proposed strength equation to the empirical data. Otherexamples of empirically based models are excavation stabilitycharts (Mathews et al., 1981) to support design charts(Barton, 2002). In coal mining in the USA, two empiricallybased models are currently used in the design and layout ofpillar and panels for longwalls and retreat mining sections.These models are called ‘Analysis of Longwall Pillar Stability’(ALPS) (Mark, 1992) and ‘Analysis of Retreat Mining PillarStability’ (ARMPS) (Mark and Chase, 1997). The models arebased on very large databases of case histories that areupdated as new information becomes available. Theseempirically developed models are very powerful because theyare rooted in the actual performance of real excavations.

The disadvantages of empirically based models are thatthey cannot easily be extended beyond their originaldatabases and they do not necessarily capture the mechanicsof the system being modelled (Mark, 1999). The need forsufficient case histories to develop the model necessitatesthat the model output represents the ‘average’ response of allthe cases evaluated. It becomes difficult to use these methodswwhen site-specific conditions are not well represented by theaveraged conditions.

NNumerical models in rock engineeringThe attractiveness of numerical models for rock engineeringapplications can be explained by the problem of the scaleeffect of rock strength. Laboratory-scale rock samples arestronger than larger field-scale rocks, while the rock mass,wwhich can contain many discontinuities, is much weaker thanthe laboratory-scale rocks (Hoek and Brown, 1980). Physicalmodel tests on small-scale rock samples bear littleresemblance to the response of a full-scale rock masscontaining many natural discontinuities. It is hardly practicalto recreate a rock mass in the laboratory that is sufficientlylarge to capture its response to external loading. Even if thiscould be done, the required loads to simulate the stresschanges associated with mining or tectonics would be wellbeyond current laboratory testing capabilities. However,numerical models that simulate large volumes of rock withembedded discontinuities can readily be created, allowingnumerical experiments to be conducted on the full-scale rockmass. The recent development of discrete fracture networkmodels (Dershowitz et al., 2004) combined with syntheticrock mass models has allowed investigations into large-scalerock mass properties to be conducted (Cundall et al., 2008) –an endeavour that could not be undertaken in a laboratory.

Synergy between empirical and numerical modelsThere is considerable potential for synergy between empiricaland numerical models. All numerical models need to bevvalidated against empirical evidence. Since empirical modelsusually encapsulate a large database of experience, they canbe effectively used to test the validity of numerical models.Similarly, validated numerical models can be used toinvestigate specific aspects of a system that are difficult toestablish by empirical observations alone. The fact that

fnumerical models are based on the mechanics of materialsallows the engineer to study interactions and combinations ofvariables that are not regularly encountered in the field, butmay be important from a safety or hazard assessment pointof view. In these cases, numerical models can extend theempirical evidence, enabling improved engineering designand excavation safety.

Case studiesTwo case studies are described in which numerical modelswere validated against empirical evidence, followed byinvestigation of a particular parameter on excavation stabilityusing the models. The first case considers the question of theimpact of large angular discontinuities on the stability ofpillars in underground limestone mines. The second casestudy describes how numerical models were used to quantifythe relationship between coal mine entry stability and theratio of horizontal to vertical stress in the surrounding rock.

Case study 1: discontinuity effects on slender pillarstrengthThe room-and-pillar method is used in the undergroundlimestone mines in the USA. The room dimensions aretypically in the region of 12 to 15 m while pillars have similardimensions. The mining height is about 8 to 10 m for single-cut mining and up to 20 m when multiple benches are mined.The width-to-height ratio of pillars varies between 0.5 andabout 4.0. Wide area collapses of a panel of pillars are rare inthe limestone mines. One or two cases of panel collapse areknown to have occurred, and only one has been adequatelydescribed in the literature (Phillipson, 2010). A survey ofpillar stability issues on 34 different limestone minesidentified 18 cases in which single pillars had failed orshowed signs of being overloaded (Esterhuizen et al., 2008).The failed pillars exhibited one or more of the followingcharacteristics:

1. Collapse of the entire pillar2. Rib spalling to a rounded hourglass shape with open

joints and fractures, shown in Figure 1



Figure 1—Partially benched pillar failing under elevated stress at thebench mining front. Typical hourglass formation indicating overloadedpillar


3. Shearing along large angular discontinuities (dip 30°to 70°) resulting in loss of pillar integrity, shown inFigure 2.

The failed pillars were typically surrounded by pillars thatappeared to be stable, showing minimal signs of disturbance.The observations led to the conclusion that the failed pillarsrepresent the low end of the distribution of possible pillarstrengths, and not the average pillar strength.

Of particular concern was that the failed pillars wereimpacted by large angular discontinuities. It was estimatedthat these pillars failed when the average pillar stress wasonly about 5% of the uniaxial compressive strength of thelimestone rock material. Discontinuities are not alwaysreadily visible to production staff when developing a pillar,but become apparent only when the pillar becomes fullyloaded or when bench mining is carried out around thepillars. Particularly hazardous conditions can result if largeangular discontinuities cause unstable blocks to slide ortopple from the pillar ribs. Of the eighteen failed pillarsidentified in the above studies, seven were associated withlarge angular discontinuities. It was also observed that widelyspaced angular discontinuities were present in the majority oflimestone mines. Clearly, the effect of large discontinuitieshas to be taken into account in the design of pillar systems inlimestone mines.

Empirical design approachThe empirically developed design approach of Roberts et al.(2007) was adopted for estimating the strength of pillars inlimestone mines. Using this approach, the strength of a pillarthat is square in plan can be expressed as follows:

[1]

wwhere w and h are the pillar width and height in metres. Thevvalue of k can be expressed in terms of the uniaxialcompressive strength (UCS) as follows:

[2]

This equation predicts the average or expected strength ofa pillar and does not explicitly account for large angulardiscontinuities. For example, a 15 m wide by 15 m high pillar

fcan be expected to have a strength that is 29.6% of the UCSof the rock material. However, field data shows that such apillar may fail when the stress is only about 5% of the UCS ifit is intersected by a large angular discontinuity. The concernwas that a collapse can occur if angular discontinuitiesweaken a large proportion of the pillars in a panel. Therefore,it was decided to introduce an adjustment to the pillarstrength equation that accounts for the weakening effect ofangular discontinuities. However, the lack of sufficient fieldcases made it impossible to conduct a statistical analysis ofthe failed cases to estimate the impact of the angular discon-tinuities. Numerical models were therefore used to investigatethe likely effect of discontinuities on pillar strength.

Modelling analysisThe numerical models were designed specifically to determinehow the inclination and frequency of large, roof-to-floordiscontinuities would affect the strength of the slender pillarsfound in limestone mines. The two-dimensional finitedifference software UDEC (Itasca Consulting Group, 2006)was used to model the pillars.

Model calibration and validation was carried out againstthe empirically developed pillar design method of Roberts etal. (2007) as well as the Lunder and Pakalnis (1997) pillarstrength equation for hard rock pillars (Esterhuizen et al.,2008). Models were created to simulate pillars with width-to-height ratios of 0.5 to 2.0 and the results compared to theempirically developed equations. These models did notcontain any explicitly modelled angular discontinuities. Therock mass properties were selected to simulate a good qualityrock mass representative of the rock found in limestonemines. Figure 3 shows the comparison between empirical andmodel results. It was concluded that the modelling methodprovides a realistic representation of pillar strength over therange of width-to-height ratios shown.

Further modelling was conducted by introducing largeangular discontinuities into the pillar models. Variousanalyses were carried out in which the dip of the discon-tinuity was varied from 30° to 90° and the strength of thepillar was determined by simulating the gradual compressionof the pillar until it reached its peak resistance and started toshed load. Figure 4 shows one of the models, indicating thelocation of the angular discontinuity and associated rock

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Figure 2—Example of a pillar bisected by a large angular discontinuityFigure 3—Validation of numerical model of a pillar against theempirically derived pillar strength equation of Roberts et al. (2007)

failure. A series of curves was fitted to the peak resistance ofeach modelled pillar (Figure 5). The results show firstly thatas the discontinuity dip increases from 30° to about 60°, itsimpact on the pillar strength increases. When the discon-tinuity dip is greater than 70°, the effect starts to diminish. Avvertical joint through the centre of a pillar is seen to have arelatively small impact on pillar strength. These trends in therelationship between pillar strength and discontinuity dip aresimilar to the results obtained when testing laboratoryspecimens with inclined planes of weakness.

The width-to-height ratio is also shown to be asignificant factor affecting the impact of large discontinuities.Figure 5 shows, for example, that a pillar with a width-to-height ratio of 0.5 will suffer a 93% reduction in strength if itis intersected by a 60° dipping joint, while a pillar with awwidth-to-height ratio of 1.0 would only suffer a 34%reduction in strength. The observation that slender pillarsintersected by large angular discontinuities can fail when theaverage stress is only about 5% of the intact rock strengthconfirms that these large strength reductions do occur in thefield and are similar to those predicted by the numericalmodels.

AApplication to designThe recommended pillar strength equation for limestonemines in the USA includes a large discontinuity factor (LDF)to account for the impact of large discontinuities on pillarstrength (Esterhuizen et al., 2008). The LDF accounts forboth the dip angle of discontinuities and the frequency ofdiscontinuities, based on the results shown in Figure 5.Figure 6 shows an application of the modified designequation, in which the impact of a single large discontinuitydipping at 60° on the strength of a 14 m wide pillar isestimated. The discontinuity causes a much greater reductionin pillar strength as the width-to-height ratio drops below1.0. This case demonstrates how numerical models were usedto develop an understanding of the issue of large angulardiscontinuity impact on pillar strength in the absence ofsufficient field cases to conduct a statistical analysis. Theoutcome of this work was used to explain the unusually lowstrength of pillars that collapsed in a marble mine in 2010(Phillipson, 2010).

Case study 2: stress impacts on coal mine entrystability

Effects of horizontal stress on roof stabilityHorizontal stress has long been known to be a significantfactor contributing to roof instability in bedded rocks in bothcoal and hard rock mines (Aggson and Curran, 1978; Herget,1987; Mark and Mucho, 1994; Iannacchione et al., 2003). Incoal mines in the USA, horizontal stresses are normallygreater than vertical stresses. They can cause compressive-type failures in the bedded roof rocks (Mark and Barczak,2000) and are a critical factor in the stability of miningexcavations. The horizontal stresses are closely related to theglobal tectonic forces (Mark and Gadde, 2008) in the NorthAmerican plate. One feature of the horizontal stress field isthat the magnitude of the major horizontal stress is about50% greater than the minor horizontal stress. As a result,excavation performance can be highly dependent on theorientation of the long axis of the entry relative to the majorhorizontal stress (Mark and Mucho, 1994). This case studyhas the objective of better quantifying the effect of entryorientation on entry stability.

Roof instability of bedded rocks subject to high horizontalstress can take many forms. Failure is often observed to bepreceded by ‘delamination’ of the bedded rock into thin


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Figure 4—Model showing damage to a pillar with a width-to-heightratio of 1.0 when loaded beyond its peak strength. The pillar is partiallyintersected by an angular discontinuity, with rock damage indicated bycoloured zones. Loading is simulated by gradually moving the upperplaten downward

Figure 5—Impact of large angular discontinuities on the strength ofpillars, based on numerical model results

Figure 6—Expected impact of a large angular discontinuity dipping at60° on the strength of a 14 m wide pillar at varying width-to-heightratios using the modified empirical pillar strength equation


f f fbeams in the roof or floor of an excavation (Colwell and Frith,2010). The thickness of these delaminated beams isdetermined by the geological composition of the rock, and canvvary from tens of centimetres to less than 1 cm. Theseindividual beds are much weaker than the original combinedbeam and have an important impact on the strength andfailure development within the roof. A second commonlyobserved failure mode is known as ‘cutter’ or ‘kink’ failure,in which crushing and local buckling of thinly laminated roofbeds occurs near the corners of an excavation. The kink bandor cutter is progressive and typically forms a near-verticalzone of failed rock (Hill, 1986). Figure 7 shows an exampleof cutter or kink failure in thinly laminated strata. The near-vvertical zone of crushed rock that results from this failuremode can lead to the progressive collapse of the entire roof,as shown in Figure 8.

The stability of critically stressed excavations can behighly dependent on their orientation relative to the majorhorizontal stress. Many cases have been reported where theground conditions in a mine were significantly improved bysimply orienting the main development direction near-parallelto the major horizontal stress (Mark and Mucho, 1994).

Empirical design approachModern support practices using rock reinforcement haveevolved in US coal mines since the 1960s. Extensive

f fhistorical experience exists with successful and unsuccessfulsupport systems in a wide variety of ground conditions.Based on this experience, NIOSH developed an empiricalsupport design procedure, called ‘Analysis of Roof BoltSystems’ (ARBS) (Mark et al., 2001). The procedure is basedon a statistical analysis of roof falls and support performanceat 37 coal mines across the USA. The ARBS makes use of theCoal Mine Roof Rating (CMRR) (Molinda and Mark, 1996) toquantify the stability of the roof rocks. The support intensityis expressed by an index parameter called PRSUP whichcombines the bolt length, spacing, capacity and, entry widthinto a single parameter as follows:

[3]

where L is the bolt length in metres, N is the number of boltsper support row, C is the bolt capacity in kilonewton, S is thebolt row spacing in metres and WeWW is the width of theexcavation in metres. A discriminant line, which defines therequired PRSUP to achieve acceptable entry stability for agiven CMRR, was determined by statistical analysis of thecase histories of ‘acceptable’ and ‘unacceptable’ supportperformance. The required support intensity is given by:

[4]

where H is the depth of cover in metres. The horizontal stressis not explicitly included in the calculation of either the CMRRor the PRSUP, but is indirectly related to the depth of cover.Consequently, the empirical design approach cannot be usedto investigate the impact of various orientations of the entryrelative to the major horizontal stress.

Modelling analysis of horizontal stress effectsA numerical modelling study was conducted in whichcalibrated numerical models were used to investigate theimpact of horizontal stress on entry stability. The firstobjective of the study was to develop a modelling method thatallows the stability of an excavation to be quantified inmeaningful manner. Model outputs can be expressed in termsof degree of deformation or volume of failure, but theseoutputs are indirectly related to stability. The operatingengineer is interested in the degree of stability of theexcavation. The concept of a factor of safety, which is widelyused and accepted in engineering practice (Harr, 1987), wasselected to express the stability of the modelled excavations.

For this modelling study, the strength reduction method(SRM) (Zienkiewicz et al., 1975) was selected to calculate thefactor of safety of supported coal mines entries (Esterhuizen,2012). The SRM was originally developed to provide analternative method of calculating the stability of rock slopesusing numerical models. It has since found acceptance in rockslope design (Lorig and Varona, 2000; Diederichs et al.,2007), but has not been widely used for undergroundexcavation stability analysis. The SRM is uniquely suited tothe objective of expressing entry stability in a format that ismeaningful to operating engineers.

The SRM safety factor is determined by first conducting astability analysis of an excavation using average rockstrength properties. Depending on the outcome, the analysisis repeated using either a decreased or increased strength

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Figure 8—Roof cavity created by progressive failure of alternating shaleand siltstone beds affected by horizontal stress. (Photo: Greg Molinda,NIOSH)

Figure 7—Failure of bedding laminations in the roof of an entry subjectto high horizontal stress. This type of failure is commonly called a‘cutter’ or ‘kink’. (Photo: Chris Mark, NIOSH)

f f f f funtil the point of failure is satisfactorily defined. The safetyfactor of the system is simply calculated as the inverse of thestrength adjustment factor at the point of collapse of themodelled excavation. For example, if collapse occurs whenthe strength is reduced by a factor of 0.8, the safety factorwwould be 1.25.

The state of ‘failure’ being investigated must be clearlydefined when using the SRM. For this study, an entry isconsidered to have failed if roof collapse in the model exceedsthe bolt length. The term stability factor is used in this paperbecause the stability is expressed as a ratio of strengths ofthe rock mass, rather than the ratio of strength to load, asused in the classic safety factor calculation. The interpretationof stability factors is similar to the interpretation of safetyfactors. A stability factor of 1.0 indicates a system that is atthe point of failure, while less than 1.0 indicates an increasedlikelihood of failure and greater than 1.0 indicates increasedlikelihood of stability.

The FLAC3D (Itasca Consulting Group, 2011) finite-difference code was used to conduct the strength reductionanalyses. Figure 9 shows one of the models, which simulatesa vertical slice through a 6 m wide coal mine entry. Thethickness of the slice is equal to the support row spacing,typically 1.2 m. Strata layering is modelled with explicitinterfaces between the different lithologies. Rockbolts aremodelled using the built-in structural elements available inFLAC3D. The bolts are located along the centre line of theslice. The bedded strata are modelled using the strain-softening ubiquitous joint constitutive model available inFLAC3D. The strength parameters of the rock matrix and thebedding planes are specified separately in the model. It wasfound that modelling of the anisotropic strength of thebedded rock was a requirement to achieve realistic rock massresponse (Esterhuizen and Bajpayee, 2012).

The model inputs and results were initially calibratedagainst field instrumentation studies. The calibration studiesincluded rock deformation, bolt loads, and entry stabilityanalysis in a variety of geological conditions encountered inthe US coal regions. During the calibration stage, a

f f fsystematic procedure for obtaining model inputs from fielddata was developed based on the CMRR (Esterhuizen et al.,2013). Using this approach, the rock mass is divided intounits, each unit having similar strength properties. Theprocedures can be used by support designers to createnumerical models in the absence of detailed laboratory testresults.

The SRM-calculated stability factors were validated bycomparing them to ARBS-calculated stability factors. For thisstudy, a total of 15 different cases were evaluated consistingof entries located at three different depths of overburden withfive different roof compositions (Esterhuizen et al., 2013).The support system consisted of five fully grouted rockboltsin a 6 m wide entry. The bolt row spacing was 1.2 m. Thestability factors of the 15 cases were first evaluated using theARBS method. For the SRM analyses, the 15 cases wereevaluated for three different stress scenarios. The stressscenarios were selected to represent the range of likelyhorizontal stress conditions in US coal mines. The SRMresults for the three different horizontal stress scenarios wereaveraged to allow comparison to the ARBS stability factors.This was done because the ARBS stability factor is based ona discriminant line that represents the entry performanceunder averaged horizontal stress conditions. Figure 10presents the correlation between the stability factors of thetwo methods at 100 m, 200 m, and 300 m depth of cover.The excellent correlation between the results of the statis-tically based ARBS method and the SRM confirms the validityof SRM results.

Horizontal stress effects quantified by numerical modelsA series of SRM analyses was conducted in which thestability factors of entries in various geological settings andfield stress conditions were varied. The entries were locatedat depths of cover of 100, 200, and 300 m. The ratio of thehorizontal to vertical stress in the plane perpendicular to theaxis of the entry was used to quantify the horizontal stresseffect. This ratio is commonly known as the k-ratio in rockengineering. The resulting stability factors were normalizedby the depth in metres, the CMRR, and the strength of theimmediate roof layer in MPa. Figure 11 shows the results fora 6 m wide entry supported by four fully-grouted 1.8 m longbolts, with rows 1.2 m apart. There is an inverse relationshipbetween the stability factor and the k-ratio. The relationship



Figure 9—Three-dimensional slice model of an entry supported withfour 2.4 m long fully grouted rockbolts. Dark shading indicates severityof rock damage; light red shading indicates dome-shaped roof that ismoving downwards. Fully grouted bolts are providing anchorage wherecoloured red

Figure 10—Correlation between stability factors calculated by theempirical ARBS method and the numerical model-based SRM


can be expressed as follows:

[5]

Using this relationship, the change in the stability factorwwith changes in the k-ratio (k) can be estimated as follows:

[6]

Consider a 6 m wide entry at 200 m depth in the easternUSA in which the major horizontal stress is 2.5 times thevvertical stress and the minor horizontal stress is 1.5 times thevvertical stress. Assume the stability factor of an entry isestimated to be 1.6 when calculated by the empirical ARBSmethod. This stability factor is representative of the entryperformance under the average stress conditions, which is ak-ratio of 2.0. The stability factor in the ‘poor direction’, thatis, if the major horizontal stress is perpendicular to the axisof the entry (k-ratio is 2.5), can be calculated to be 1.30using Equation [6]. The stability factor in the ‘good direction’(k-ratio is 1.5) is calculated to be 2.08. In this example, thereduced stability factor in the ‘poor direction’ indicates thataction will be required to ensure a stable, safe entry, whileentries developed in the ‘good direction’ can be expected to beadequately supported. The above relationship can beincorporated in empirically based support design equations tobetter represent the impact of horizontal stresses in theempirical model.

DiscussionThe two case studies show how specific stability issuesidentified during empirical observations can be evaluatedusing numerical models. In the case of the limestone minepillars, the impact of large angular discontinuities wasidentified as a potential safety hazard. However, the lack ofsufficient case histories in the field made it impossible toconduct a statistical analysis to quantify their effect on pillarstability. Numerical models were used to investigate a largenumber of scenarios to develop an understanding of theproblem and determine an adjustment to the pillar strengthequation that accounts for these structures.

In the second case, empirical observations identified theimpact of the k-ratio on excavation stability; however, the

empirically developed design method did not explicitlyaccount for horizontal stress. Numerical models were used toinvestigate the impact of the k-ratio on entry stability andproduced a relationship that can be used to quantify the k-ratio effect on stability.

In both cases, the numerical model outputs were validatedagainst empirical observations before detailed analyses of thespecific issues were conducted. The numerical model resultswere then used to extend the usefulness of the empiricalmodels, demonstrating the synergy between the twoapproaches.

ConclusionsModels of various types are used in rock engineering todetermine the likely response of the rock mass aroundexcavations. Empirical models, based on the analysis of largenumbers of case histories, have found wide acceptance as atool for engineering design. The application of empiricalmodels is limited by the restriction that they should not beused beyond the limits of the empirical base from which theywere developed. Numerical models are based on themechanics of rock behaviour and can be used to answerquestions about rock mass response under given loadingconditions and to extend the empirical models.

Numerical models must be validated against empiricaldata. The process of first calibrating numerical modelsagainst specific case histories is required because of theuncertainty associated with rock mass strength parameters.The validity of the models can then be tested againstempirically based models.

Synergy exists between the empirical and numericalmodelling approaches. Empirical models can be used tovalidate numerical models, and numerical models can be usedto extend the utility of empirical models.

The two case studies presented show how numericalmodels were applied to answer questions about specificaspects of excavation stability that existing empirical modelswere unable to provide.

The combined application of numerical and empiricalmodels can help to improve understanding of causes ofstability and instability in excavations, resulting in efficientdesign and increased safety.

AcknowledgementsThe contributions of my colleagues at the NIOSH Office ofMine Safety and Health Research in the research presentedhere are gratefully acknowledged. The willingness of minestaff to share their experience and allow NIOSH researchersto conduct mine site surveys and measurements haveprovided the much-needed empirical data for calibration andverification of numerical model results.

DisclaimerThe findings and conclusions in this report are those of theauthors and do not necessarily represent the views of theNational Institute for Occupational Safety and Health.

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Figure 11—Effect of varying the horizontal-to-vertical stress ratio onthe stability of modelled entries in various geological settings anddepths of cover

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IntroductionSouth Africa is a major mining country and ishost to a significant proportion of the world’smineral resources. Gold resources in SouthAfrica occur mainly in the WitwatersrandBasin, and platinum group metals (PGMs) inthe Bushveld Complex (BC). The nation hostsmost of the world’s mineral reserves ofplatinum and palladium, about 75% and 50%respectively (Cawthorn, 1999), and futureproduction potential is estimated to be morethan a hundred years. Furthermore, Cawthorn(1999) suggests that, since mining of PGMshas progressed only to an average depth of2000 m below surface, the proven reservesmay easily double as deeper exploration andmining take place.

The 2060 Ma Bushveld Complex is anirregular, saucer-shaped massive layeredigneous intrusion (Figure 1), with outcrop

textremities of approximately 450 km east–westand 300 km north–south (Simmat et al.,2006). The platinum reserves occur in threehorizons: the Merensky Reef, the Upper Group2 (UG2) Reef, and the Platreef (Cawthorn,1999; Cawthorn and Boerst, 2006). Belowthese reef horizons lies the Upper Group 1(UG1) Reef, the platinum content of which hasnot yet been widely proven to be economicallyviable. The continuity of the Merensky andUG2 reefs has been confirmed to 3000 mbelow surface (Cawthorn, 1999). Potholes,faults, and dykes in the Merensky and UG2reefs disrupt the otherwise uniformly shallowdipping and narrow tabular reef characteristicspeculiar to the BC mines.

The main rock types associated with theMerensky and UG2 reefs are gabbro, norite,anorthosite, and pyroxenite. ‘The [Merensky]reef in its most common form is a pegmatoidal(coarse-grained) feldspathic pyroxenite,generally bounded by thin (approx. 20 mm)chromitite layers. The immediate hangingwallis pyroxenite, 1–5 m thick, which gradesupwards through norites to anorthosites. Thefootwall generally consists of various types ofnorite and anorthosite, and less commonlyfeldspathic pyroxenite or harzburgite, whichhowever often forms the immediate footwall ofpothole reefs’ (Rangasamy, 2010). Anorthositeis an important rock type in the BC. Asindicated by Barnes and Maier (2002),

Time-dependent tensile strengths of BushveldComplex rocks and implications for rockfailure around mining excavationsby D. Nyungu* and T.R. Stacey

SynopsisDespite observations of spalling and damage of mine excavation wall rock inthe Bushveld Complex (BC) over the passage of time, there have been veryfew time-dependent or creep tests carried out in South Africa on rock, partic-ularly on BC rock types. The research described in this paper deals with theinvestigation of stress and strain conditions influencing spalling of wall rockin BC mine excavations, and the influence of time on the tensile strength ofseveral BC rock types.

Time-dependent laboratory testing of BC rocks was carried out in indirecttension. The results show that the magnitude of the tensile strength of BCrock types is approximately 5% of their uniaxial compressive strengthmagnitudes. The average long-term uniaxial compressive strength of the BCrocks, interpreted from the axial stress-volumetric strain graphs, is 56% of theUCS value. The long-term tensile strength is shown to be less than 70% of thenormal tensile strength. Extension strains at tensile strength failure rangedbetween 0.16 and 0.21 millistrain. Values corresponding with the long-termtensile strength are less than 70% of this range, namely, 0.11 to 0.15millistrain. These results represent new knowledge, since such rock testingand analysis does not appear to have been carried out previously on BC rocktypes.

Elastic numerical modelling was carried out to illustrate the extents oftensile stress zones and extension zones around of typical BC mineexcavations. The models showed that large zones of extension strain canoccur around BC excavations, and that the magnitudes of the extension straincan substantially exceed the critical values determined from the laboratorytesting. The implication of this is that there are substantial zones surroundingBC mine excavations that will be prone to time-dependent spalling conditionsand perhaps more significant failure.

Keywordstensile strength, creep, time-dependent failure, rock strength, BushveldComplex.

* Anglo American Platinum Ltd and School ofMining Engineering, University of theWitwatersrand.

† School of Mining Engineering, University of theWitwatersrand.



Time-dependent tensile strengths of Bushveld Complex rocks

anorthosite is a brittle rock, and can therefore be expected tobe prone to failure under tensile stress and extensionconditions. They state, ‘Mottled anorthosite refers toanorthosite in which large areas of inter-cumulus ortho-pyroxene and/or augite (from 10 mm diameter up to thediameter of tennis balls) form dark mottles in a matrix ofpure white or pale grey anorthosite. Spotted anorthosite isanorthosite in which a small percentage of cumulus ortho-pyroxene gives the effects of dark spots in the paleanorthosite matrix.’

In situ stress conditions are very important influences onthe behaviour of mining excavations in the BC. Relevantinformation from a database of in situ stress measurementsacross southern Africa (Stacey and Wesseloo, 2004) aresummarized in Figure 2. High horizontal to vertical stressratios (2.5–4 in the pseudo-strike direction) are commonlyexperienced at shallow depths in BC mines. At greater depthsof about 1000 m, the stress ratios are lower, in the region of1 to 1.5.

Mining operations in the Bushveld Complex Underground mines contribute most of the PGM production inSouth Africa, the bulk of the ore currently being mined atdepths between 500 m and 2000 m below surface. Primaryand secondary excavations are mined to access the mineralreserves, and these have to remain open and stable for thelife of the mine. In shallow BC mines, the compressivestrength of the rock (UCS) is usually much greater than thecompressive stress in the excavation walls. In these stressconditions, failure of rock would not be expected. However,stress-induced spalling of rock from walls of miningexcavations is frequently observed (Ryder and Jager, 2002).Figure 3 illustrates this behaviour in a haulage tunnel at adepth of approximately 400 m below surface.

The walls of the haulage were observed to scale with thepassage of time, implying time-dependent behaviour of thewwallrock. This has been observed in the BC mines, betweenmonths and years after excavation, due to fracture initiationand propagation in intact rock. Time-dependent stope closurebehaviour in BC stopes has also occurred (Malan et al.,

2007). Damage in excavation walls in BC mines isexacerbated by the intersection of fractures with naturally-occurring, shallow-dipping discontinuities and layered rock,resulting in the formation of blocks of rock with high falloutand unravelling potential. Instability problems in inclinedshafts and in dip-oriented tunnels have occurred in somemines in the Rustenburg mining environment, as evidencedby observed roof failures and resulting ‘gothic arches’ intunnels oriented on-dip (see Figure 4).

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Figure 1—Geological map of the Bushveld Complex (after Viljoen andSchürmann, 1998)

Figure 2—In situ stress data for South African mines (Stacey andWesseloo, 2004)

Figure 3—Spalling in a haulage tunnel in a platinum mine, western limb,Bushveld Complex

Figure 4—Stress-induced failure in the roof of a dip-oriented tunnel in aplatinum mine (Stacey and Wesseloo, 2004)

Since rock strengths appear to be considerably greaterthan rock stresses, the observations indicate that rock failuremight be considered to be somewhat unexpected. However,in other situations, rock in the walls of excavations has beenobserved to fracture or fail at stress levels well below theUCS, as found by Stacey and Yathavan (2003). Theyreviewed published information on the development offractures at low stress levels in rock (Grimstad and Bhasin,1997; Myrvang et al., 2000). This review showed that stress-induced failure can occur even when the maximum inducedstresses are as low as one-quarter to one-half of the rockstrength.

Ortlepp (1997) observed a rockburst in a sandstone roofof a shallow coal mine about 20 m below surface. Thesefindings point to a different failure mechanism than themechanisms commonly assumed in the Mohr-Coulomb andHoek-Brown failure criteria. Observations of face-parallelslabbing or spalling at low confining stress suggest extensionas a fracturing mechanism.

Stress and strain distributions around typical miningexcavations Since failure of rock around mining excavations at shallowdepth is commonly observed, it was considered appropriate tocarry out stress analyses to evaluate the magnitudes of stressand strain that might theoretically be expected. Numericalanalyses of typical mining excavations, a stope and a stopepillar, were therefore carried out using k-ratios of 1 and 2,characteristic of the deeper and shallow mining respectively.Two mining depths were considered, 500 m and 1000 mbelow surface. Examples of computed minimum principalstresses (σ3), including orientations, and minimum principalstrains, around a typical in-stope pillar are shown in Figures5 and 6.

Distributions of minimum principal stresses and strainsaround a typical stope excavation were also determined.Examples of the minimum principal stress and minimumprincipal strain distributions are shown in Figures 7 and 8.

fTensile stresses occur in the immediate sidewalls of thepillar, and in the stope hangingwall. The immediateperipheries of the excavations experience substantial zonesof extension. Extension strains that exceed a criticalextension strain value can indicate the initiation of fracturingin rock (Stacey, 1981). These fractures form in planes normalto the direction of extension strain, which corresponds withthe direction of minimum principal stress (the leastcompressive principal stress). Importantly, extension canoccur in an environment in which all three principal stresses



Figure 5—Distribution of minor principal stress (σσ33) around a pillar at adepth of 500 m, k-ratio of 2

Figure 6—Distribution of minimum principal strain around a stope pillarat a depth of 500 m, k-ratio of 2

Figure 7—Minimum principal stress around a stope at a depth of 500 m,k-ratio of 2

Figure 8—Minimum principal strain around a stope at a depth of 500 m,k-ratio of 2


fare compressive. It is to be noted that, in the calculation ofthe minimum principal strain, the value of σ2 used was basedon the assumption of plane strain.

Observed hangingwall delamination has been attributedto the layered nature of the rocks. However, the effect of lowconfinement, as well as tensile and extension conditions,could be a direct or a contributory cause. The models indicatezones of extension strain and principal stress orientationsthat are compatible with the geometry of spalling observed inthe excavation wall rock.

The photograph in Figure 9 shows a platinum mine stopethat had been standing for at least 6 months. Thehangingwall of the stope delaminated and face-parallelfractures, corresponding with the modelled principal stressorientations, developed slowly at stresses lower than thecompressive or tensile strengths of the host rock types. Looseblocks form when the fractures propagate and intersectnatural discontinuities, resulting in unravelling aroundsupport. Installed support in these conditions curbs thepropagation of fractures, and slows down the manifestationof excavation wall damage.

Time-dependent behaviour of rockWWhen an excavation is mined, stress redistribution occursaround the opening, and a change in the stress field canresult in significant deformation, or creep, occurring over arelatively long period of time. Creep is defined as increasingstrain while the stress is held constant (Rinne, 2008) and isobserved mainly in soft rocks, for example salt. However, alltypes of hard rock also exhibit creep characteristics over longenough time intervals (Critescu and Hunsche, 1998).

Creep commonly consists of three stages: after an instan-taneous elastic strain when a constant load is applied,primary (transient) creep occurs; then, with time, secondaryor steady state creep occurs; finally, tertiary or acceleratingcreep will occur, leading to eventual failure. The tertiary stagealways terminates in fracture and establishes the link withthe phenomenon of time-dependent failure (Wawersik,1972). Drescher and Handley (2003) observed these creepstages when they carried out uniaxial compression creep testson Ventersdorp lava and Elsburg quartzite.

According to Ryder and Jager (2002), the long-termstrength of rock can be as low as 70% of the UCS. However,

tests on granite and anorthosite by Schmidtke and Lajtai(1985) showed that stresses as low as 50% of the short-termstrength almost certainly caused time-dependent stress-corrosion cracking in brittle rocks, severe enough to causedelayed failure. Investigations of crack growth in loadedgranite using a scanning electron microscope indicated thatnew cracks developed continuously under constant load(Kranz, 1976). These findings point towards the importanceof including time-dependent behaviour in the design ofexcavations in rock.

Ryder and Jager (2002) state that the creep rate in rock isdependent on the magnitude of the deviatoric stress (σ1 - σ3)and not the individual magnitudes of σ1 and σ3, (where σ1and σ3 are the major and minor principal stresses respec-tively). However, orientations of fracturing due to deviatoricstresses would not correspond with the observedunderground spalling or slabbing behaviour. In contrast,observations of failure, and the results of the numericalanalyses described above, which show that large zones ofrock around excavations are likely to be in a condition ofextension, indicate that extension fracturing is a more likelymechanism. An investigation into the time-dependent charac-teristics of BC rock types was therefore considered to bejustified.

Laboratory tests on Bushveld Complex rocksFew studies have been conducted on the time-dependentbehaviour of strong brittle rocks. Of significance for SouthAfrican mining conditions is the testing described byBieniawski (1967c; 1970), Kovács (1971), Drescher (2002),Drescher and Handley (2003), and Watson et al. (2009).However, these publications provide very little information onthe time-dependent properties of BC rock types. Appropriatetesting was therefore necessary to provide data forcomparison with the results of the numerical analyses.

A range of laboratory rock strength tests was carried outon several BC rock types: UCS tests to establish the elasticproperties and failure characteristics under uniaxialcompression and, from the UCS test results, interpretation oflong-term strength and stress and strain values observed atfailure; Brazilian indirect tensile (BIT) tests (ISRM, 2007) todetermine normal tensile strengths; and time-dependent BITtests.

The following methodology was used to achieve theobjectives outlined above:

➤ UCS tests on cylindrical specimens of several BC rocktypes prepared from different depth sections along asingle vertical drill-hole core

➤ Normal BIT tests on several BC rock types➤ Time-dependent constant hold-load BIT tests on several

BC rock types stressed to pre-determined hold-loadlevels. The times-to-failure for the different testcategories at constant load were recorded for thedetermination of time-dependent characteristics.

The UCS and BIT test specimens were prepared from thecore of a single exploration drill-hole. The vertical boreholegives a good cross-sectional representation of the BClayering. Drill-hole core samples were taken from a zone upto 10 m above and below the hangingwall (HW) and footwall(FW) contacts of the Merensky (MR), Upper Group 1 (UG1).and Upper Group 2 (UG2) reef horizons.

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Figure 9—Delamination of hangingwall in a platinum mine stope

fNine test specimen categories were identified: spottedanorthosite, mottled anorthosite (A, D, and I), pyroxenite,norite, anorthositic norite, and spotted anothositic norite (Fand H).

UCS and BIT test specimens were cut alternately from thecore to provide an unbiased sample representation for thetwo test methods. No two test specimens for the same testmethod were cut adjacent to one another. In total, 334specimens were tested. The majority of test specimens wereprepared for the BIT tests, and most of these were used in thetime-dependent tests. For each test type, the numbers ofspecimens that were tested successfully with valid resultsvvaried.

All specimens were prepared and tested according to theISRM Suggested Methods for rock testing (ISRM, 2007). Thecores were BQ size with a diameter D = 36.3 mm. Averagelength to diameter ratios of 2.2 and 0.5 were achieved for theUCS and BIT test specimens respectively. Inspection showedthat the specimens did not have visible pre-existingdeformities. Circumferential and axial strain gauges wereattached to the UCS test specimens to measure strains duringthe tests. All tests were carried out in anhydrous conditionsat room temperature and pressure.

The data from the test results was processed and used todetermine UCS values, and to plot stress-strain curves,similar to that shown in Figure 10. The average values ofelastic properties of mottled anorthosite (A) are indicated onthe plot. After initial nonlinear behaviour (possibly crackclosure, Bieniawski, 1967a; 1967b), the plot shows largelylinear behaviour up to the peak strength of the specimen.

The specimens failed in the typical fashion observed forbrittle rock failure – initial axial extension fractures andultimately shear, resulting in some cases, in conical end-pieces and a completely fractured or crushed middle portion.

Axial stress-volumetric strain plots were used to evaluatethe ‘long-term strength’ of the rock types. According toBieniawski (1967a), the ‘nose’ of the stress-volumetric strainplot marks the ‘long-term strength’ of the specimen. Long-term strengths were determined for each rock type andaverage values calculated. Variations in the test results areattributed to inherent variability in the rock specimens. A

fsummary of the UCS test results, together with the ‘long-termstrength’ values, is given in Table I.

The average value of the ‘long-term strength’ for the ninecategories of rock types was 78 MPa, which is 56.4% of theUCS value of the rock types tested. The lowest long-termstrength values were recorded for pyroxenite and mottledanorthosite at 44% of their respective UCS values.

Brazilian indirect tensile (BIT) testsA servo-controlled rock testing machine was used to carryout normal and time-dependent BIT tests. A constant loadingrate of 2 kN/min was used to load specimens, targetingfailure in 3 to 4 minutes, depending on the sample’s tensilestrength. A summary of the results of the normal Braziliantensile strength tests is presented in Table II (with elasticmodulus values obtained from the UCS tests). On average,the tensile strength magnitude was found to be 5% (1/20) ofthe UCS of the same rock type.

Typical strain values at failure for each rock type werecalculated based on the average elastic modulus of the rocktype, and ranged from 0.16 to 0.21 millistrain, with anaverage value of 0.18 millistrain. This range agrees withpublished data; for comparison, the value obtained for noritewas 0.173 millistrain (Stacey, 1981).



Table I

Summary of average results from UCS tests

Rock type/ Mottled Spotted Pyroxenite (C) Mottled Norite (E) Spotted Anorthositic Spotted Mottled Code anorthosite anorthositic norite anorthosite anorthositic norite anorthosite anorthosite

(A) (B) (D) norite (F) (G) (H) (I)

Sample diameter, D (mm) 36.30 36.30 36.30 36.30 36.30 36.30 36.30 36.30 36.30Sample length, L (mm) 80.74 84.79 81.66 81.13 83.71 82.87 80.99 81.03 80.98L/D ratio 2.23 2.34 2.25 2.24 2.29 2.28 2.23 2.23 2.23Sample mass, M (g) 231.41 254.20 270.30 230.93 261.32 248.10 253.48 237.80 232.32Sample density, ρ (kg/m3) 2769.46 2898.71 3198.41 2750.49 3016.40 2892.39 2990.22 2835.12 2772.17Failure load, (kN) 180.60 139.40 129.80 140.50 96.00 154.60 114.00 159.60 182.20UCS, σcσσ (MPa) 174.51 134.70 125.42 135.76 92.76 149.38 110.15 154.22 176.05Elastic modulus, E (GPa) 44.60 33.32 35.49 39.01 30.90 40.65 37.90 42.64 45.31Poisson’s ratio, v 0.20 0.21 0.17 0.28 0.19 0.21 0.15 0.22 0.19Long-term strength (MPa) 90.2 61.8 56.5 59.75 53.5 75.6 83.6 103.33 125.75% of UCS 57 46 44 44 57 51.4 72.4 67 68.7557 46 44 44 57 51.4 72.4 67

Figure 10—Example of a stress-strain graph for mottled anorthosite


Time-dependent Brazilian indirect tensile (BIT)sstrength tests

Pre-determined load levels, derived from the tensile strengthvvalues determined in the normal BIT tests, were used in thetime-dependent BIT strength tests. The load levels usedrepresented 70%, 75%, 80%, 85%, and 90% of thecorresponding tensile strength for each rock type: At eachload level, tests were conducted on sets of five specimens foreach test category. Loading of each test specimen wasincreased at a rate of 2 kN/min up to the required hold-load.The time-to-failure, T(s), was recorded for complete test runsTTwwhere specimen failure was observed. Owing to limitedavailability of the testing machine, the time-dependent testswwere limited to a maximum of three days. In a few cases,particularly in the low load tests at 70%, failure did not occurwwithin three days, and in some cases the testing machinetripped due to overheating. Valid test results were thereforerecorded only if the initial loading build-up was completed tothe hold stage, the machine did not trip, and the test wascompleted within three days.

Some of the tests, particularly those at 90% of tensilestrength, resulted in failure during the load application stage,i.e. before reaching the constant load phase. Other test runswwith similar premature failure results were attributed to rockmaterial variability.

fThe time-dependent test results for the nine rock typesare summarized in Table III.

The individual test results showed scatter in the time-to-failure. A logarithmic trend line was fitted to the results foreach type category, as shown in Figure 11. The minimumvalue indicated by the curve may be taken as the long-termtensile strength of the rock type. Similar graphs wereproduced for all the rock types to determine the time–to-failure trends.

It may be estimated from these results that the long-termtensile strengths of BC rocks are likely to be between 60%and 70% of the short-term tensile strength normally reportedfor laboratory tests. Since all mining excavations are long-term in the context of the duration of testing carried out,these lower strength values should be taken into account indesign.

From these results the equivalent extension strains atfailure were calculated, and these indicate that an averagevalue for the critical extension strain is likely to be approxi-mately 0.12 millistrain.

Summary of laboratory test resultsFrom the range of strength tests carried out, the followingkey outputs have been summarized:

a) Average UCS values obtained for the BC rocks testedvaried between 93 MPa and 176 MPa

▲


Table II

Summary of normal BIT test results

Rock type Sample Sample Sample t/D Sample Average load Average BIT Average elastic Average strain Average time-ID diameter thickness, ratio mass, at failure, strength, modulus, at failure, to-failure

D (mm) t (mm) M (g) P (kN) σσtσσtσσ (MPa) E (GPa) (millistrain) (s)

Mottled anorthosite (A) DBA 36.30 19.27 0.53 54.26 8.19 7.46 46.72 0.16 220.71Spotted anorthositic norite (B) DBB 36.30 18.88 0.52 55.46 6.84 6.35 33.32 0.19 205.72Pyroxenite (C) DBC 36.30 18.97 0.52 62.67 7.43 6.89 35.40 0.19 206.77Mottled anorthosite (D) DBD 36.30 18.77 0.52 53.84 6.83 6.38 39.01 0.16 138.02Norite (E) DBE 36.30 18.36 0.51 57.67 6.92 6.62 30.90 0.21 160.35Spotted anorthositic norite (F) DBF 36.30 18.61 0.51 55.24 8.27 7.76 40.65 0.19 138.17Anorthositic norite (G) DBG 36.30 17.65 0.49 56.31 7.71 7.65 37.90 0.20 203.85Spotted anorthosite (H) DBH 36.30 17.59 0.48 51.29 7.11 7.10 42.64 0.17 213.83Mottled anorthosite (I) DBI 36.30 17.06 0.47 48.93 6.82 7.04 45.31 0.16 214.03

Table III

Time-dependent test results

Rock type . Mean BIT strength, Static BIT test load, x% of Pmean (kN) and time-to-failure, T (s)

specimen I.D Pmean (kN) 90% Time, T (s) 85% Time, T (s) 80% Time, T (s) 75% Time, T (s) 70% Time, T (s)

A 8.19 7.37 268 6.96 972 6.55 11658 6.14 39831 5.73 111480B 6.84 6.16 1587 5.81 2200 5.47 4578 5.13 19837 4.79 62226C 7.43 6.69 16830 6.32 38695 5.94 33705 5.57 207423 5.20 23605D 6.83 6.15 229 5.81 16328 5.46 82700 5.12 36306 4.78 151889E 6.92 6.23 1652 5.88 375 5.54 10327 5.19 1679 4.84 60706F 8.27 7.44 - 7.03 375 6.62 1705 6.20 2698 5.79 60709G 7.71 6.94 643 6.55 147 6.17 - 5.78 2483 5.40 805H 7.11 6.40 4214 6.04 12291 5.69 8168 5.33 67109 4.98 62350I 6.82 6.14 6213 5.80 45191 5.46 33038 5.12 38993 4.77 39151

7.116.82

A Mottled anorthosite; B Spotted anorthositic norite; C Pyroxenite; D Mottled anorthosite; E Norite; F Spotted anorthositic norite; G Anorthositic norite; HSpotted anorthosite; I Mottled anorthosite

fb) The average long-term strength of the rocks(Bieniawski, 1967a), interpreted from the volumetricstrain curves in the UCS tests, was 78 MPa, which is56% of the UCS. The lowest long-term strength valueobtained was 44% of the corresponding UCS value

c) Tensile strength magnitudes of the rocks were foundto be between 4% and 7% of the UCS magnitudes (i.e.the tensile strength magnitude is about 1/20 of theUCS magnitude)

d) The minimum long-term tensile strengths of the rockscould not be determined owing to the limited testingduration of three days, but are certainly less than 70%of the short-term tensile strength normally reportedfor laboratory tests

e) Extension strain magnitudes at strength failurecalculated from the normal tensile strength testsindicate a range of between 0.16 and 0.21 millistrain.Values corresponding with the long-term tensilestrength would therefore be less than 70% of thisrange, i.e. 0.11 to 0.15 millistrain.

The extension strain magnitudes determined in thenumerical modelling are significantly greater than thoseindicated in (e) above, the implication being that extensionstrain may be a suitable criterion for prediction of fractureand failure around BC mining excavations. Observationsmade in actual BC mine excavations revealed that fracturingof intact rock occurs over a protracted time, possibly due tothe development and propagation of extension fractures, andthat the manifestation of such fracturing was curbed byinstalled support.

ConclusionsThe research described in this paper has dealt with theinvestigation of stress and strain conditions influencing thespalling of wallrock in mine excavations in the BushveldComplex (BC). This involved laboratory testing of BC rocks inuniaxial compression and in indirect tension, including time-dependent indirect tension, as well as numerical modelling oftypical mine excavations. The following conclusions aredrawn:

➤ Observations made in BC mine excavations revealedthat fracturing of intact rock occurs over a protractedtime period, and that its manifestation is curbed byinstalled support

➤ There have been very few time-dependent or creep

ftests carried out in South Africa on BC rock types. Thelaboratory testing reported in this paper has providednew data in this regard

➤ The laboratory tests have shown that tensile strengthmagnitudes of BC rock types are approximately 5% oftheir compressive strength magnitudes

➤ The long-term uniaxial compressive strength of the BCrocks, interpreted from the axial stress-volumetricstrain graph from the UCS test, is on average 78 MPaor 56% of the average UCS value

➤ The tensile strength of the BC rock types was found tobe time-dependent. Failure times for individual testspecimens showed large variability, but the generalindication is that the time-dependent tensile strengthsare between 60% and 70% of the tensile strengthnormally reported from laboratory testing, and possiblymay even be less than 60%

➤ Extension strains calculated at tensile strength failureranged between 0.16 and 0.21 millistrain. Valuescorresponding with the long-term tensile strength areless than 70% of this range, namely, less than 0.11 to0.15 millistrain

➤ Numerical analyses of BC excavations were carried out,using elastic models and assuming homogeneity ofmaterial, to investigate the possible occurrence of zonesin which tensile stresses and extension strains occur.The models showed that large zones of extension strainmay occur around BC excavations, and that themagnitudes of the extension strain exceed the criticalvalues determined from the laboratory testing.Predicted orientations of fracturing from these modelscorrespond with observed geometry of spalling inexcavations. The implication is that there are likely tobe substantial zones surrounding BC mine excavationsthat will be prone to spalling conditions and perhapsmore significant failure.

AcknowledgementsImpala Platinum is thanked for provision of the borehole coreand associated information on which the research describedin this paper was based. Joseph Muaka is thanked forassistance with numerical analyses specifically for the paper.The input of the second author is based on researchsupported in part by the National Research Foundation ofSouth Africa (Grant-specific unique reference number (UID)85971). The Grantholder acknowledges that opinions,



Figure 11—Time-to-failure plot for mottled anorthosite


ffindings, and conclusions or recommendations expressed inany publication generated by the NRF-supported research arethat of the author(s), and that the NRF accepts no liabilitywwhatsoever in this regard.

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IntroductionIt has been observed in field and laboratoryconditions that failure of intact hard rocks athighly confined compression can beaccompanied by abnormal violence. Underboth of these conditions the failure process isassociated with shear rupture development.David Ortlepp, who acquired more than 40yyears of experience in the study of shearrupture rockbursts in deep and ultra-deepSouth African mines, emphasized thisphenomenon (Ortlepp, 1997; Ortlepp et al.,2005): ‘All rockbursts, by definition, involvesudden and often violent displacement of rock.Occasionally however, larger incidents causedamage of such intense violence that it seemsthat our knowledge of the mechanism ofdamage is completely inadequate.’ Special fieldstudies (Gay and Ortlepp, 1979; McGarr et al.,1979) have revealed that shear rupturescausing abnormally violent rockbursts arecreated in intact rock mass. An important

f ffeature is that they nucleate in zones of highlyconfined compression that are some distanceaway from excavation (on the excavationsurface the minor stress is equal to zero). Itwas shown that these mine tremors andearthquakes share the apparent paradox offailure at low shear stresses, while laboratorymeasurements indicate high material strengths(McGarr et al., 1979).

Recent laboratory studies of post-peakfailure of hard rocks (characterized by uniaxialcompressive strength above 250 MPa) athighly confined compression (σ1 > σ2 = σ3when σ3 > 50 MPa) support Ortlepp’s ideaabout inadequate understanding of the failuremechanism at these loading conditions(Tarasov, 2008, 2010; Tarasov and Randolph,2008, 2011). Some observed abnormalitiesthat cannot be explained on the basis ofconventional approach are presented inFigures 1 and 2.

Figure 1 shows two sets of generic stress-strain curves for different levels of confiningpressure σ3. Figure 1a represents the conven-tional (well-studied) rock behaviour associatedwith increasing post-peak ductility with risingσ3. For clarity, the variation of the post-peakcurves is indicated by dotted lines. Figure 1brepresents the unconventional type of rockbehaviour. Here, increasing σ3 can lead to acontradictory variation of post-peak properties.In fact, rock behaviour can be changed fromClass I to extreme Class II and then to Class Iagain. Class I is characterized by a negativepost-peak modulus M = dσdd /d∈dd , and Class II bypositive (Wawersik and Fairhurst, 1970). Atextreme Class II behaviour, values of post-peak modulus M and elastic modulus E =dσdd /d∈dd can be very close, indicating extremelysmall post-peak rupture energy (compare

Fan-structure shear rupture mechanism as asource of shear rupture rockburstsby B.G. Tarasov*

SynopsisThis paper proposes the further development of a recently identified shearrupture mechanism (fan mechanism) that elucidates a paradoxical featureof hard rocks – the possibility of shear rupture propagation through ahighly confined intact rock mass at shear stresses that can be significantlyless than frictional strength. In the fan mechanism, failure is associatedwith consecutive creation of small slabs (known as ‘domino blocks’) fromthe intact rock in the rupture tip, driven by a fan-shaped domino structurerepresenting the rupture head. The fan head combines such unique featuresas extremely low shear resistance, self-sustaining stress intensification,and self-unbalancing conditions. Consequently, the failure process causedby the mechanism is inevitably spontaneous and violent. Physical andmathematical models explain unique and paradoxical features of themechanism, which can be generated in primary ruptures and segmentedfaults. The fan mechanism provides a novel point of view forunderstanding the nature of spontaneous failure processes, including shearrupture rockbursts. The process explains, in particular, features of shearrupture rockbursts such as activation at great depths, generation of newshear ruptures in intact rock mass, nucleation of hypocentres at significantdistances from the excavation, shear rupture development at low shearstresses, and abnormal rupture violence.

Keywordsrock strength, failure at confined compression, shear rupture mechanism,structure of shear rupture, conditions of instability, physical model,mathematical model, shear rupture rockburst.

* University of Western Australia.© The Southern African Institute of Mining and

Metallurgy, 2014. ISSN 2225-6253. This paperwas first presented at the, 6th Southern AfricanRock Engineering Symposium SARES 2014,12–14 May 2014, Misty Hills Country Hotel andConference Centre, Cradle of Humankind,Muldersdrift.


Fan-structure shear rupture mechanism as a source of shear rupture rockbursts

shaded areas in Figures 1a and 1b for σ3 = σ3(4)).A small post-peak rupture energy in turn indicates high

post-peak brittleness. A special brittleness index wasdeveloped to characterize unambiguously the post-peakbrittleness at any type of rock behaviour (see details inTarasov and Potvin, 2013). The index K = dWrWW /rr dWeWW = (M(( -EE)/EE M// is based on the ratio between the post-peak ruptureenergy dWrWW and elastic energy dWeWW withdrawn from thematerial during the failure process. The index K characterizesthe capability of the rock for self-sustaining failure due to theelastic energy available from the failing material. Figure 1cshows variation of the brittleness index K with risingconfining pressure σ3 for rocks exhibiting conventional andunconventional behaviour. In contrast to the conventionalbehaviour, where increasing σ3 is accompanied by amonotonic decrease in post-peak brittleness, the brittlenessvvariation for unconventional behaviour follows a typicalpattern of initially increasing, reaching a maximum, and thenultimately decreasing. The harder the rock, the greater theeffect of embrittlement. Experiments (Tarasov, 2010) showedthat some rocks at high confinement became hundreds oftimes more brittle compared to their behaviour under uniaxialcompression.

Figure 2 illustrates the abnormal violence of hard rockfailure at extreme Class II behaviour. The experiments wereconducted on an extremely stiff servo-controlled testingmachine based upon the loading principles described inStavrogin and Tarasov (2001). Figure 2a shows a set ofstress-strain curves for dolerite (uniaxial compressivestrength 300 MPa) obtained at different levels of σ3. At σ3 <60 MPa the total post-peak control was provided for bothClass I and Class II behaviour. Dotted lines here indicategeneral orientation of post-peak curves. At σ3 ≥ 60 MPacontrol was possible only at the start of the post-peak stage,after which spontaneous and violent failure took place.Dotted lines indicate orientation of post-peak curves at themoment that instability starts. In this case M is close to E,EEpost-peak rupture energy is vanishingly small, and post-peakbrittleness approaches absolute brittleness (extreme Class II).

ffTo demonstrate the difference in violence at spontaneousfailure for Class II at low σ3 (where post-peak control ispossible) and for extreme Class II at high σ3 (where post-peak control is impossible), some special experiments wereconducted. At low σ3 the spontaneous failure was generatedat the peak stress due to the absence of post-peak servo-controlling. During failure at all levels (low and high) of σ3the differential stress variation with time was recorded by a load cell adjoining the tested specimens (Figure 2b).Two different modes of rock behaviour were distinguished.Figure 2c shows a stress-time curve typical for σ3 < 60 MPa,while Figure 1d shows a stress-time curve typical for σ3 ≥60 MPa.

It should be emphasized that for the two curves obtainedat σ3 = 30 and 60 MPa the stress drop ΔΔσσ (the differencebetween the stress of the instability start σAσ and the residualstrength σf) was practically the same:f ΔΔσσ(30) = 310 MPa andΔΔσσ(60) = 340 MPa. Points of instability are marked byasterisks on the stress-strain curves in Figure 2a and stress-time curves in Figures 2c and 2d. Despite the fact that theelastic energy available from the specimen-loading machineis comparable for both experiments, the shapes of the curvesdiffer dramatically. For σ3 = 30 MPa the failure was followedby a conventional stress oscillation around the residualstrength. For σ3 = 60 MPa an extraordinary post-failurestress shock was generated, after which the equilibriumcondition was reached at a stress level significantly below theresidual (frictional) strength σf. Identical stress shocks wereobserved in all experiments conducted at σ3 ≥ 60 MPa(Tarasov and Randolph, 2008).

The observed features of hard rocks, such as the dramaticpost-peak embrittlement with rising confining pressure σ3;the abnormal failure violence within a certain range of σ3;and the huge after-failure stress-shock cannot be explainedaccording to the basics of common understanding of shearrupture mechanisms. This paper shows that the observedabnormalities are generated by a recently identified shearrupture mechanism (fan mechanism) that is activated in hardrocks (uniaxial compressive strength above 250 MPa) athighly confined compression. In the fan mechanism, the rock

▲


Figure 1—Two sets of generic stress-strain curves for different levels ofconfining pressure σσ33 illustrating (a) conventional and (b) unconven-tional rock behaviour. (c) Typical variation of the post-peak brittlenessindex K with rising σσ33 for rocks exhibiting the conventional andunconventional behaviour

Figure 2— (a) Stress-strain curves for dolerite specimens tested atdifferent levels of confining pressure σσ33, (b) schema of a specimen withadjoining load cell, (c) and (d) typical stress-time curves recorded bythe load cell during spontaneous failure of rock specimens at low andhigh levels of σσ33

f ffailure is associated with consecutive creation of slabs(known as ‘domino blocks’) from the intact rock in therupture tip and is driven by a fan-shaped domino structurerepresenting the rupture head. The fan head combines suchunique features as extremely low shear resistance, self-sustaining stress intensification, and self-unbalancingconditions. Consequently the shear rupture can propagatethrough the medium with negligible resistance, resulting inabnormal violence. Physical and mathematical models of themechanism presented in this paper explain the unique andparadoxical features of the mechanism. This mechanism canoperate in small laboratory specimens and in field conditions,causing shear rupture rockbursts and earthquakes. Naturalfaults normally have a very complicated multi-hierarchicalsegmented structure. We will firstly discuss features of thefan mechanism operation in primary ruptures (thincontinuous formations), and then in complex faults (Ortleppshears).

Fan mechanism in primary ruptures

FFrictional and fan-hinged shearThis section discusses the interrelation between well-knownfailure mechanisms for rocks at confined compression andthe fan mechanism. It is known that rock failure mechanismsare dependent on the level of confining pressure σ3 (e.g.Kirby and McCormick, 1984). Figures 3a and 3b showvvariations in failure mechanisms, with σ3 rising from left toright, for rocks exhibiting conventional and unconventionalbehaviour. Rectangles represent rock specimens exhibitingdifferent failure mechanisms. In brittle rocks, pre-existingdefects at loading generate tensile cracks, the ultimate lengthl of which is a function of σ3, as shown symbolically bydotted lines: the higher the σ3 the shorter l. The length l oftensile cracks in turn determines the macroscopic failuremechanism and the failure pattern.

At confining pressures σ3 < σ3min(shear), shear rupturecannot propagate in its own plane due to the creation in therupture tip of relatively long tensile cracks preventing theshear rupture development. The tensile cracks grow along themajor stress. Two failure mechanisms distinguished at thesestress conditions are: (1) splitting by long tensile cracks and(2) failure due to coalescence of distributed micro-cracksaccumulated within the material body during loading.

At σ3 ≥ σ3min(shear) fthe failure mode is localized shear.Due to high confinement, micro-tensile cracks becomesufficiently short to cause shear rupture to propagate in itsown plane. Here the dilation of one short micro-crack inducesthe dilation of a closely spaced neighbouring crack (Rechesand Lockner, 1994). Due to the consecutive creation of shorttensile cracks in front of the rupture tip, the advancing faultitself induces organized damage that is restricted to its ownplane. It is important to note that micro-cracks are generatedalong the major stress, which is at angle αo ≈ (30°–40°) tothe shear rupture plane (Reches and Lockner, 1994; Horiiand Nemat-Nasser, 1985). This micro-cracking processcreates inclined intercrack blocks (known as domino blocks)which are subjected to rotation at shear displacement of therupture interfaces (Peng and Johnson, 1972; King andSammis, 1992; Reches and Lockner, 1994). Two specificshear rupture mechanisms have been distinguished here.

Frictional shearThe development of a frictional shear rupture can becontrolled on stiff servo-controlled testing machines. It hasbeen observed that intercrack domino blocks generated in therupture tip are subjected to collapse at rotation caused byshear displacement of the rupture faces, creating frictionalstructureless medium (gouge) in the shear rupture interface(Peng and Johnson, 1972; King and Sammis, 1992; Rechesand Lockner, 1994). Increasing confining pressure (σ3 in thiscase) increases friction within the total rupture zone(including the rupture head), which causes the increase inpost-peak rupture energy according to the conventional rockbehaviour shown in Figure 1a.

Fan-hinged shearIt should be noted that the three mechanisms discussed areactivated in practically all types of rock. The fourthmechanism is generated in hard rocks (characterized byuniaxial compressive strength above 250 MPa) and isresponsible for the unconventional behaviour (Tarasov,2008, 2010). Further increases of the confining pressureabove σ3 = σ3min(shear) will continue reducing the length l oftensile cracks (dotted curve in Figure 3b) and, consequently,the length of domino blocks composing the fault structure.Due to the very strong material and proper geometry of shortdomino blocks within the range σ3min(hinge) < σ3 < σ3max(hinge)they can withstand rotation caused by the shear displacementof the rupture faces without collapse. In this case the dominoblocks behave as hinges, and due to consecutive generationand rotation they create a fan structure representing theshear rupture head. The fan structure has several extraor-dinary features which will be discussed further.

It should be emphasized that the efficiency of the fanmechanism is variable and determined by how perfect thefault structure is. The solid curve in Figure 3b shows symbol-ically the variation of the fan mechanism efficiency versusconfining pressure, which determines the identical variationof post-peak brittleness of rocks exhibiting unconventionalbehaviour (see Figure 1c). At the low end of the hingepressure range, when the relative length (length/thickness)of the rotating blocks is still large, the domino blocks aresubjected to partial destruction (buckling) as they rotate (therole of block geometry is discussed in more detail later). Athigher σ3, with shorter blocks, this imperfection decreases,rendering the fan mechanism more efficient. The optimal



Figure 3—Variation in failure mechanisms and failure patterns with σσ33

for rocks exhibiting (a) conventional and (b) unconventional behaviour


ff fefficiency takes place at a confining pressure at which theblocks rotate with minimum destruction. At greater σ3 theefficiency reduces because shorter blocks gradually lose anypotential to operate as hinges. Finally, very short blocks losethis capability completely and the rock behaviour returns tothe commonly accepted frictional mode.

PPhysical model of the fan mechanismSelf-balancing fan structureAs discussed above, primary shear ruptures propagatethrough rocks due to the consecutive formation of identicaldomino blocks from the intact material in the rupture tip.Further rotation of the blocks between the shear rupture facescan lead to frictional shear (at block collapse) or to fan-hinged shear (at block rotation without collapse). Themechanism responsible for the creation of identical dominoblocks is not considered in the physical and mathematicalmodels presented in this paper. The models discuss theinfluence of the fan structure formed on the basis of rotatingblocks on the rupture process. In the models the dominoblocks are considered as ‘predetermined’ and operating withoptimal efficiency (without collapse at rotation).

Photographs of the physical model in Figure 4a showdifferent stages of fan formation. At the initial condition(Figure 4a-I) a row of identical domino blocks inclined atangle α0 represents an implicit horizontal shear rupture(fault). Surfaces of neighbouring domino blocks are in fullcontact, providing a very compact ‘monolithic’ material. Tosimulate the resistance of domino blocks to tearing-off fromthe monolithic material (which takes place in real materials)the blocks are bonded to each other. The row of dominoblocks is located between two layers of elastic material(elastic connectors) representing the fault interfaces. Theupper and lower elastic connectors are fixed to correspondingends of each domino block. Contact areas between the endsof domino blocks and the interfaces we will call joints. Assuch, a version of the model with bonded blocks can betreated as representing an intact material. Evenly distributedwweight located on the upper layer creates normal stressapplied to the simulated fault σn = σp.

Propagation of shear rupture along the potential fault can be initiated by application of a local force F to the elasticconnector fixed to the top of the first domino block (Figure4a-II). The applied force will be transmitted to the next blocksby stretching elastic connectors between them at theconsecutive separation of blocks and rotation of them againstjjoints. Due to this, the domino blocks will be sequentially(one by one) torn off from the ‘monolithic’ material, forminga fan-shaped structure. The fan has completed when the frontblock rotates on the total angle βtot = 180° - 2α0 at sheardisplacement ΔΔ of the rupture faces. The total number ofdomino blocks involved in the fan structure of the physicalmodel is about 30, while in real materials it can be thousands(Tarasov and Guzev, 2013). The fan structure with smallnumber of domino blocks in the physical model will allowsome features of the fan mechanism to be illustrated morelegibly.

Figure 4b reflects experimentally determined variation ofthe force F applied to the first domino block during fanformation and, consequently, the variation of shearresistance of the fan structure at different stages of itsformation. Such variation of the shear resistance is

f fdetermined by the fact that elementary forces N (representingnormal stress σn) applied to each domino block createhorizontal components fs Nff of opposite directions in the firstand the second half of the fan structure (Figure 4c). Due tothis, the maximum resistance is attained at the completion ofthe first half of the fan, while the totally completed fanstructure represents the self-balancing structure with shearresistance equal to zero (details are given in the mathematicalmodel presented later).

Shear resistance below the frictional strength – pulse-likerupture modeTo make the fan structure self-unbalancing, a distributedshear stress τ should be applied to the whole domino row.The simplest way to apply the distributed shear stress for thephysical model is to incline the row by angle γ as shown inFigure 5a. The distributed weight σp in this situation createsshear stress τ = σpsinγ along the whole structure. Under theeffect of distributed shear stress the fan propagates along thewhole row, sequentially moving the loaded upper face againstthe lower one by distance ΔΔ.

Experiments on the physical model show that theminimum angle γ at which the fan becomes unstable is about4°. The distributed shear stress generated at this angle is justsufficient to overcome the shear resistance of the fanstructure, which is determined by (1) friction in joints ofrotating domino blocks and (2) the reduced resistanceassociated with the tearing off of each front domino blockfrom the intact material. It was established that the resistanceto shear provided by the fan structure is very low. To movethe same fault faces at common frictional resistance (withoutfan structure) the angle γ should be about 40°. Thisexperiment clearly demonstrates that shear resistance of thefan structure τfan can be significantly less than the commonfrictional strength τf. For this particular model, shearresistance (strength) of the fan structure τfan is less than thefrictional strength τf: τfan ≈ 0.1τf by a factor of ten.

It should be emphasized that low shear resistance isencountered within the zone of the moving fan head only. Infront of the fan the material is in an intact condition. Behindthe fan, shear resistance is equal to friction. Due to this thefan mechanism provides the pulse-like rupture mode: at anygiven time during rupture propagation, slip occurs over only anarrow band (fan head) along the fault and the fault relocksbehind the rupture head. This slip pulse propagates forwardas the fault proceeds. Pulse-like rupture mode was observedfor earthquakes (Healton, 1990) and in laboratory (Ohnakaet al., 1986; Lykotrafitis et al., 2006).

▲


Figure 4—Physical model of the fan structure formation from dominoblocks

Conditions of fan nucleation and propagationFigure 5b illustrates schematically the most important featureof the fan mechanism. Horizontal lines show differentstrength levels along a hypothetical fault zone: τu – strengthof intact material (fracture strength); τf – frictional strength ofpre-existing fault; τfan – strength (shear resistance) of the fanstructure. The graph on the left reflects the procedure of fanmechanism activation as discussed in Figure 4a. To create theinitial fan structure a local stress equal to the fracturestrength τu should be applied. The development of the initialfan structure is a stable process. After completion of the fanhead, further dynamic propagation of the fault through theintact material can occur at any distributed shear stressesexceeding the fan structure strength τfan. New faults in intactmaterials can thus be produced by the fan mechanism even atdistributed shear stresses that are significantly below thefrictional strength τf (this will be discussed in more detailfurther). At the same time, the higher the distributed shearstress applied the higher the rupture speed and rupturevviolence.

MMathematical model of the fan mechanism

Interaction of domino blocks and self-balancingconditionsFurther elucidation of the fan mechanism will be provided inthis section on the basis of the mathematical model (furtherdevelopment of the model by Tarasov and Guzev, 2013). Themathematical model here represents the simplest descriptionof the fan mechanism and reflects the static balance of forcesaffecting the domino structure before instability. This modelwwas designed to demonstrate the essence of the fanmechanism, including some extraordinary features (e.g. self-balancing principle, extremely low shear resistance, self-sustaining stress intensification, self-unbalancingconditions). All simplifications made in the model do notdistort the essence of the fan mechanism.

fThe domino structure of the mathematical model has thesame features discussed for the physical model. For simplicitywe will discuss the domino blocks as shown in Figure 6a. Allblocks here are represented by beams of length r inclined atthe initial angle α0 and distanced from each other by s. Thedistance is s = w/sinα0, where w is the block width. Bothends of each beam are connected to the upper and lowerlayers by joints. For ease we assume that the upper layer isrepresented by an elastic material, while the lower layer andthe beams (domino blocks) are stiff. As such, a version of themodel with blocks bonded together can be treated asrepresenting the intact material.

Evenly distributed normal σn and shear τ stresses areapplied to this construction. The value of τ is less than theshear stress necessary for displacement of the upper layeragainst the lower. Each beam is loaded by the same normal,N, and horizontal (shear), fNN τff , elementary forces representingthe evenly distributed normal σn and shear τ stresses appliedto the whole structure. Elementary forces N (and theirhorizontal components) are shown by black arrows.Elementary forces fs τff are shown by red arrows. The meaningof the elementary forces is as follows: N = σns1; fτff = τs1,where depth (or thickness) of the model is equal to unity. Itshould be noted that in the completed fan structure, due tostretching of the interface the distance between any twoneighbouring blocks on one side of the fan increases(compared to the initial distance s) and the interface acquiresa wave-like shape, which should affect the values of stresses(σn, τ) and elementary forces (N,NN f,, τff ). To simplify the modelwe will consider the applied stresses and elementary forcesinvariable. This simplification does not affect the equilibriumcondition of the completed fan and the extraordinary featuresof the fan-structure, which we shall demonstrate, thanks tothe symmetrical configuration of the fan structure.

The fan structure can be formed if, in addition to theexisting evenly distributed stresses, a local supplementaryforce (or stress) is applied. Let us apply a supplementaryhorizontal force F to the upper end of the leftmost beam.Figure 6b shows an intermediate stage of the fan formation.Equation [1] describes a variation of shear resistance (F(( )FFduring formation of the fan structure (see details in Tarasovand Guzev, 2013).

[1]

wherefcff is the reduced resistance of tearing off the front beam

from the basis (the elementary force fcff is applied to thefront beam only and shown by a green arrow inFigures 6b)

k is the number of activated beamsδ is the average angle between two neighbouring beams

described by Equation [2] (Tarasov and Guzev, 2013).



Figure 5—(a) Physical model of the fan propagation along the inclinedsimulating fault, (b) schema illustrating shear resistance of the fan-structure at the stage of initial formation and at the stage ofspontaneous propagation

Figure 6—Interaction of domino blocks in the fan mechanism


[2]

wwhereEE is the modulus of elasticity of the elastic connectorAA is the cross-sectional area of the elastic connector.

An essentially important feature of the fan structure isthe fact that the resistance fNff = N/NN tgαgαggαgαgαgg (see Figure 6b) of eachbeam decreases with increasing angle αα from αα0. It reacheszero at αα = 90° and becomes negative at αα > 90°. This meansthat the formation of the first half of the fan structure isaccompanied by the increase in shear resistance F; it reachesFFa maximum when the half of the fan has completed and thendecreases to zero, similar to the experimental graph inFigure 4b.

Self-sustaining stress intensification and conditions ofiinstabilityFigure 7 illustrates some features of the completed fanstructure. In Figure 7a, elementary forces resistingdisplacement of the top rupture face against the bottom oneare directed to the right, while forces assisting displacementare directed to the left. Shear resistance of relativedisplacement of the rupture faces due to the fan mechanismis determined by the sum of all horizontal forces applied to allbeams involved in the completed fan, which is represented byEquation [3].

[3]

The first term of Equation [3] reflects the effect ofelementary forces fNff = N/NN tgαgαggαgαgαgg and elastic forces associatedwwith stretching of the elastic connectors on shear resistanceof the fan structure. The elastic forces are represented hereby angle δ. At self-balancing conditions the elastic forces areneutralized by forces fNff . For the completed fan structure αα0+ ktotkk δ = - αα0, which means that the first term of Equation [3]is equal to zero.

The second term of Equation [3] reflects the reducedresistance fcff of tearing off the front beam (domino block)from the intact material. The elementary force fcff (green) isapplied to the front beam only. It will be shown that thecontribution of fcff to the total resistance of the fan structure inreal rocks is negligibly small.

The third term of Equation [3] represents an active unitedforce assisting displacement. It is caused by the evenlydistributed shear stress τ applied to the material. It should beemphasized that, unlike fcff , the elementary forces fτff (red) areapplied to all ktotkk beams of the fan structure.

When τ = 0 the formation of completed fan structure canbe conducted in a stable regime using stiff +servo-controlledloading principles. Dotted beams in Figure 7a correspond topositions of them for the self-balancing fan. The dotted blackcurve in Figure 7b illustrates the variation of the fan headresistance Fk at the fan structure development for the self-balancing condition. The horizontal axis represents the fanstructure length l = sk.

With distributed shear stress (τ > 0) the fan headbecomes self-unbalancing and starts propagating through theintact material. Separation (tearing-off) of the front domino

f fblocks sequentially from the intact material is the essence ofthe failure process created by the fan mechanism. It shouldbe emphasized that the fan structure in this case represents anatural stress intensifier that magnifies stresses in therupture tip providing the tearing-off process. Elementaryhorizontal forces fτff (red) applied to each domino block of thefan as shown in Figure 7a are transmitted via the elasticconnector (interface) to the rupture tip by the principle shownin Figure 7c. Hence, the separation of each front dominoblock with the reduced resistance fcff (green arrow) from theintact material is caused by the united active force ktotkk ftt τffwhere ktotkk is the total number of domino blocks in the fanhead. Calculations by Tarasov and Guzev (2013) show thatthe fan structure in natural materials can incorporatethousands of domino blocks Hence separation of the frontdomino block from the intact material can be caused by verylow shear stress applied fτff = fcff /ktotkk . The contribution of fcff c toshear resistance of the fan structure is therefore negligibledue to intensive stress magnification in the rupture tip causedby the fan mechanism.

The solid curve in Figure 7b shows features of the fanformation for the case when τ > 0. At the initial stage of thefan formation Fk increases; it reaches a maximum Fk = Fmax,then decreases to zero Fk = 0, and finally becomes negativeFk < 0. At the last stage (after point A) the fan formation isinevitably unstable because internal forces within the fanstructure are not equalized and the fan head starts movingspontaneously as a wave. Depending on the value ofdistributed shear stress applied the extreme situation, Fk = 0can be reached at different stages before the completion ofthe fan structure.

Effect of friction in joints and block collapse on shearresistance of the fan structureThe analysis of the idealized fan model (without friction injoints of rotating domino blocks) shows that the shearresistance of the completed fan structure propagating throughthe intact material is negligibly small. If we take into accountfriction in joints, we can estimate the real shear resistance ofthe fan structure. Estimations by Tarasov and Guzev (2013)show that despite friction in joints, shear resistance between

▲


Figure 7—(a) Structure of the completed fan head with elementaryforces applied to domino blocks, (b) variation in shear resistance of thedeveloping fan structure for self-balancing (shaded curve) and self-unbalancing conditions, (c) principle of the stress intensificationprovided by the fan mechanism

f f ftwo interfaces separated by the fan structure of dominoblocks operating as hinges is lower than the frictionalstrength of pre-existing faults with common frictionalinterfaces by the ratio w/r. Here, w is the width and rk is thelength of domino blocks (see Figure 6a). The shear resistanceassociated with friction in joints represents the totalresistance of the fan head propagating through the intactmaterial:

[4]

Equation [4] shows that increasing the ratio r/wdecreases the effect of friction in joints on the fan shearresistance: the higher the r/w the greater the efficiency of thefan mechanism. As discussed previously, the length ofdomino blocks is a function of confining pressure σ3 (dottedline l versus σ3 in Figure 3b). By analogy, the dotted line inFigure 8a shows symbolically the variation of the ratio r/wversus σ3 within the pressure range between σ3min(shear) andσ3max(hinge). This line indicates the hinge efficiency variationwwith confining pressure if domino blocks are not collapsed atrotation.

It should be noted that each block in the fan functionssimilarly to a beam with rotation-free end conditions loadedalong the beam axis. Figure 8b illustrates features of theaxial loading of a block at the initial (α0 = 300) and verticalpositions. It shows that the value of axial force applied to thefront domino-block exceeds the elementary force N(associated with the evenly distributed normal stress σn) bymore than two times. This force is determined by forces Nand fcff . It means that any block at the front position is in themost stressed conditions. If the block does not collapse at thestart of rotation it will be capable of bearing identical stressesat any stage of its rotation, including the vertical positionwwhen it deforms the rupture faces by the value μ. All blocksμof the fan in combination create additional normal stressesmoving apart the fault faces (wedge effect). We can supposethat during rotation from the initial to the final position, eachdomino block of the completed fan structure is under approx-imately the same axial stresses.

In rocks under confined compression, domino blocks aresubjected to high loads that can lead to buckling and collapseof the blocks. On the basis of information presented byMegahid et al., (1993) we assume that domino blocks withslenderness ratio r/w ≤ 10 are stable at axial loading. Dominoblocks with slenderness ratio r/w > 10 will be subjected todifferent degrees of destruction, depending on the ratio r/w.The solid graph in Figure 8a illustrates a possible variation ofthe fan mechanism efficiency versus confining pressure σ3,taking into account the block destruction. At values r/w < 10the efficiency varies in accordance with Equation [4]. Withinthe range 10 < r/w < 20, due to different degrees ofdestruction (depending on r/w) only a part of each block canmaintain stability operating as a hinge. Very long blocks withslenderness ratio r/w > 20 completely collapse at rotation,creating gouge and common friction between the interfaces.This is a preliminary explanation for the variable fanmechanism efficiency. Further experimental and theoreticalstudies will allow better understanding of this phenomenon.

The important point is that the fan mechanism is activeonly within the range of confining pressure betweenσ3min(hinge) and σ3max(hinge), with optimal efficiency at

σ3opt(hinge) f f ff. Such variation of the fan mechanism efficiencycauses corresponding variation of the unconventional rockbehaviour (e.g. brittleness variation shown in Figure 1c). Inaccordance with Equation [4], the shear resistance (strength)of the fan head for domino blocks characterized by the ratiow/r = 0.1 is one-tenth of the frictional strength: τfan ≈ 0.1τf.This estimation is consistent with the result obtained on thephysical model.

Uncontrollable failure and abnormal violence causedby the fan mechanismIn this section we discuss an energy balance at spontaneousfailure of rock specimens caused by the fan mechanism. Itshould be noted that loading conditions to generate the fanmechanism in rock specimens are different from those in thephysical model discussed previously. In the physical modelthe fan structure was generated by a stress applied locally.Rock specimens tested at highly confined compression aresmall. In order to generate the fan structure in this situationthe whole specimen has to be loaded axially to high stressesthat correspond to the material strength. Let us analysefeatures of the failure process caused by the fan mechanismat such stress conditions.

We will do this on the basis of experimental resultsobtained on the dolerite specimen tested at confiningpressure σ3 = 60 MPa (Figure 2). The same stress-displacement curve is shown in Figures 9a and 9b. Point Abeyond the peak stress on these graphs corresponds to thestart of instability. Figure 9c shows an enlarged portion ofthe post-peak stress-strain curve at stress degradation fromultimate stress σu till σAσ (this is replicated four times). At thispost-peak stage the rupture was easily controllled. However,below σAσ (points A) control became impossible. To analysethe reason for that we divided the post-peak curves into fourstages with equal intervals of differential stress. Each stage ischaracterized by average values of elastic modulus E (solidlines) and post-peak modulus M (dotted lines). Areas locatedbetween the E and M lines indicate the current post-peakrupture energy dWrWW :

Here σo and σe are differential stresses at the onset andthe end of each stage.

We can see that the current post-peak rupture energydecreases dramatically with the rupture development fromstage 1 to stage 4. The variation of dWrWW with stress



Figure 8—(a) Variation of the fan mechanism efficiency versus confiningpressure affected by friction in joints and block collapse, (b) loadingconditions of the same domino block at the initial and vertical positions


fdegradation from σu to σAσ is illustrated in Figure 9d. At stage4 the rupture energy becomes extremely small becausemodulus M approaches modulus E. Such variation in theEEpost-peak rupture energy can be caused by the formation ofthe second half of the fan structure as was shown for thephysical model on the graph in Figure 4b. After completion ofthe fan structure the uncontrollable spontaneous failure startsdue to the high distributed shear stress (corresponding topoint A on the diagrams in Figures 9a and 9b).

Figure 9e illustrates features of the shear rupturepropagation through the specimen after the fan structure hascompletely formed. Figure 9e (1) shows the situationcorresponding to point A, after which the fan head startedpropagating spontaneously along the dotted line. Figure 9e(2) shows an intermediate situation where the rupture ispropagating in the pulse-like mode. Shear resistance anddisplacement along the fault during the failure process arevvery irregular. Three specific zones can be distinguished: (1)the fan zone where the failure process and domino blockrotation is in progress; (2) the frictional zone located behindthe fan head where the blocks have completed their rotationand the full friction is mobilized; and (3) the intact zone infront of the fan head. A load cell and an axial gauge in Figure9e (1) mounted on the specimen as is commonly used inexperiments can measure only the average load-bearingcapacity and displacement of the specimen during the failureprocess. Results obtained on the basis of these gauges do notallow estimating the real energy balance of the failureprocess. However, the new knowledge about the fanmechanism gives us a chance to derive this inaccessibleinformation.

The red area in Figure 9a represents elastic energyaccumulated within the specimen and the machine at point Aat the moment of the instability start (AD is the loadingstiffness). At point A the bearing capacity of the specimenmeasured by the load cell (axial stress σA) is generallyprovided by the intact material located in front of the fanhead. The contribution of the fan head to the bearing capacityof the specimen is very small. Starting from this moment, thefan head with resistance σfan crosses the specimen. Axialdisplacement dfand of the specimen caused by this processis associated with rotation of domino blocks as shown inFigure 9f. For the fault thickness h = 0.1 mm and α0 = 30°the displacement dfand ≈ 0.3 mm. Due to a very small shearresistance of the fan head (assume σfan ≈ 0.1 σf) the post-fpeak rupture energy Wr(fan)WW associated with failure anddisplacement by the distance dfand ≈ 0.3 mm is also very small.Wr(fan)WW is represented by the grey area in Figure 9b. After thefan head has crossed the specimen, displacement along thewwhole fault becomes possible. This displacement isaccompanied by violent dynamics caused by a large amountof released energy WaWW (yellow area in Figure 9b). Shearresistance of the fault at this stage is determined by frictionalstrength σf.

Due to the inertia associated with high dynamics, thetotal displacement along the fault dtotdd will exceed thecoordinate of point B corresponding to frictional (residual)strength σf. The equilibrium conditions will be reached belowσf at point B1 (see Figures 9b and 2d). The violentdisplacement along the fault will pulverize the initial dominostructure and create gouge between the fault interface at thefinal stage of the failure process (Figure 9e (4)).

fIt should be emphasized that during spontaneous failureafter point A, self-unbalancing conditions exist within the fanhead at any level of stresses above σfan. This means thatstable and controllable failure beyond point A is in principleimpossible for the fan mechanism, even on an absolutely stiffand servo-controlled testing machine. This explains theabsence of experimental post-peak data for hard rocks athighly confined conditions (Mogi, 2007; Shimada, 2000;Tarasov, 2010). High rupture speed and pulverization of theinitial fault structure during the spontaneous failure processmake it impossible to directly observe and study the fanmechanism, and this explains why the mechanism has notbeen detected before.

Figure 10 explains the post-failure stress-shock discussedin Figure 2d by comparing different failure regimes (frictionalshear and fan-hinged shear) with sliding a board along ahillside. When friction is constant during the dynamic eventsas shown in Figure 10a, the failure and sliding processes arenot accompanied by stress shock. However, if low friction issuddenly substituted by high friction, the stress shockbecomes inevitable (Figure 10b).

Fan mechanism in natural faults

Structure and evolution of Ortlepp shearsNatural faults normally have very complicated structure.Before elucidating the role of the fan mechanism in theformation of dynamic natural faults, we will discuss featuresof complex fault development. Different hypotheses of faultevolution have been proposed (Segall and Pollard, 1980;Sibson, 1982; Scholz, 2002; De Joussineau and Aydin,2009), but there is still no consensus regarding this process.This section introduces a possible version of fault evolutionbased upon studies of: (a) faulting process at highly confinedcompression in laboratory experiments on calcareoussiltstone (Otsuki and Dilov, 2005) and (b) structure ofdynamic faults in rockbursts generated by shear rupture inbrittle quartzite in deep South African mines (Ortlepp, 1997).

Experiments conducted by Otsuki and Dilov (2005)demonstrated the following features of complex faults:

(1) Faults are multi-hierarchical segmented formations(2) Segmentation as a mechanism of fault propagation

acts on all hierarchical ranks of complex faults▲


Figure 9—Features of the failure process governed by the fanmechanism in the dolerite specimen

(3) Segmentation is a result of advanced triggering of anew bilaterally propagating rupture (new segment)in front of the propagating current rupture (currentsegment)

(4) The current and new segments propagating towardeach other form a jog (step) where they meet

(5) Jogs of a compression type are very common infault zones regardless of their size

(6) Once a number of segments of a given hierarchicalrank coalesce, they behave as a single new andlonger segment of one higher rank

(7) Segment of higher rank can trigger a new segment(rupture) at greater distance

(8) The new triggered segment starts as a primaryrupture.

All these features are illustrated and discussed brieflybelow. The series of photographs in Figure 11a showsprinciples of the fault evolution by the advanced triggering ofnew segments (modified from Otsuki and Dilov, 2005).Segments are represented here by white lines. The faultpropagates from left to right. The segments are generated oneby one and propagate bilaterally. Neighbouring segments areconnected by a compressive jog where they meet. Overlapzones of the jogs are subjected to significant irreversibledeformation. Figure 11b shows features of compressive jogsformed in brittle quartzite (photographs from Ortlepp, 1997).The overlap zones of these jogs are represented by a row ofdomino blocks.

Figure 11c proposes a possible mechanism of the dominostructure formation in compressive jogs (Tarasov andOrtlepp, 2007). It shows four steps of linkage between twosegments (bold arrows) propagating towards each other inintact rock. Figure 11c (1) indicates the directions of theapplied stresses against the propagating segments. In Figure11c (2) the rock mass surrounding the approaching segmentsis theoretically divided into two massive blocks (A and B)pressed against each other by the applied stresses. Thepropagation of the segments decreases shear resistancebetween the massive blocks along these segments, which

foverstresses an area located between the tips of theapproaching segments (Segall and Pollard, 1980). Withfurther propagation of the segments they confine a zone thatis now overstressed (Figure 11c (3)). When the extendingoverlap zone in hard brittle rocks reaches a critical length itfractures dynamically into an echelon of domino blocks(slabs) due to the shear of the overlap zone between theparallel faces of the massive blocks A and B (Figure 11c (4)).This process is accompanied by the release of some portion ofelastic energy. After that the segments stop propagating. Theinitial orientation of the tensile cracks separating the overlapzone into domino blocks is parallel to the major stress σ1. Byanalogy with primary ruptures angle αo ≈ (30°–40°). Incontrast to primary ruptures where the domino blocks aregenerated sequentially in the rupture tip, this mechanismgenerates a row of parallel domino blocks simultaneouslywithin the overlap zone of compressive jogs.

This mechanism can create cascades of compressive jogswhich in combination can represent a fault segment of higherhierarchical rank. Figure 12 illustrates the principle offormation of a multi-segmented fault, a photograph of whichis shown on the right. The fault propagates upwards. Openarrows indicate the direction of applied shear stress. At stageI a dynamically propagating primary fracture triggers anadvanced fracture. Asterisks indicate centres of initiation ofadvanced triggered fractures. This new fracture (as well as allfurther triggered fractures) propagates bilaterally towards thecurrent fracture and in the opposite direction. This fracture inturn triggers the next advanced fracture shown at stage II. Atthis stage the overlap zone between the two bottom fractureshas reached the critical length and divided into a row ofdomino blocks. Further fault development occurs throughrepetition of similar stages. In Figure 12 new compressivejogs adjoining the tip of propagating fault are shown in red.

Figure 13 shows the evolution of a multi-hierarchicalsegmented fault. The fault nucleates as a primary rupture(bottom left corner) because the fan mechanism mobilized invery thin primary ruptures is the most energy-efficient shearrupture mechanism. A new primary rupture can be triggeredin front of the current one at a distance xI due to stresstransfer. Primary ruptures represent segments of rank I. Oncea number of segments of a given hierarchical rank coalesce,they behave as a single new and longer segment of one



Figure 10—Explanation of the post-failure stress-shock phenomenonby comparing different failure regimes (frictional shear and fan-hingedshear) with sliding a board along a hillside

Figure 11—Fault segmentation due to advanced triggering of newsegments and creation of domino structure on the basis ofcompressive jogs


higher rank. The rank II segmented rupture can trigger aseries of new primary ruptures at different distances withmaximum remoteness of xII. The key feature of fault segmen-tation is the fact that a new segment triggered by the currentsegment of any rank nucleates as a primary rupture. At itspropagation towards the current segment (and in the oppositedirection) the new segment will be subjected to similarevolution as the current segment. After linkage of a numberof rank II segments the next rank III segment will be formed(shown on a smaller scale ≈ 1:5). Further development of thisfault will be accompanied by creation of higher ranksegments. Where they meet, segments of each hierarchicalrank form compressive jogs and domino structures ofcorresponding rank.

Formation of the fan structure in segmented faultsThe photographs in Figure 14a (from Ortlepp, 1997)demonstrate that domino blocks involved in complex faultscan be subjected to rotation by angle ββ due to sheardisplacements of the fault faces. Observations show thatangle ββ can be relatively high, exceeding 90°. Taking this factinto account we can suppose that during the faultpropagation, domino blocks within a set of compressive jogsrepresenting the fault head can created a fan-shape structureas shown symbolically in Figure 14b due to rotation ofdomino blocks through different angles ββ caused by thevvariable shear displacement along the fault. In the front jog

the block orientation is αα0 f. The final block position at the endof the fan head is (-αα0). The segmented fan structure has thesame remarkable features as discussed for primary ruptures.

It should be emphasized that the fan structure can beformed if shear displacement between the fault faces issufficient for the completed block rotation. Figure 14c showsthe initial and final position of domino blocks for two shearruptures of thickness h1 and h2. The thick rupture requiressignificantly greater displacement ΔΔ to complete the blockrotation. Equation [5] allows estimation of the faultdisplacement ΔΔfault necessary for creation of the fan structure:

[5]

fTaking this into account, we can analyse the possibility ofthe fan mechanism activation in a complex fault shown inFigure 15 (photograph from Ortlepp, 1997). The structure ofthis fault is shown symbolically on the left. It includesprimary ruptures and higher rank segments formed on thebasis of compressive jogs (rank II and rank III). Ortlepp(1997) indicates that the magnitude of the jog (or step) ofrank III is hIII = 260 mm. The magnitude of sheardisplacement along the fault is less than 100 mm. This meansthat the fan structure cannot be formed on the basis ofdomino blocks of rank III. However, the development ofprimary ruptures (rank I) and ruptures of rank II can begoverned by the fan mechanism because for these Δfault ≥Δfan.

Due to this, the self-unbalancing fan structure (red zonesin Figure 15) is created predominantly in segments of lowerranks. The fan mechanism generated here is responsible forhigh dynamics of the failure process. Relatively thin localizedzones of very intense destruction can be observed in eachdynamic fault. The initial domino structure of these segmentsis completely destroyed by extensive and violent shear andrepresented by pulverized gouge or even by pseudo-tachylytes. This explains why the fan structure has neverbeen seen in nature, unlike domino structures of high-ranksegments observed in a myriad of different faults. In high-rank segments, domino blocks rotate through low angleswithout destruction and serve as a dampening mechanism.

Nucleation and propagation of dynamic natural faultsThe analysis of the physical and mathematical models showsthat the initial formation of the fan structure requires a high

▲


Figure 12—Cascade-like combination of compressive jogs formed dueto advanced triggering of new segments (photograph from Ortlepp,1997)

Figure 13—Evolution of a multi-hierarchical segmented fault

Figure 14—Principle of fan structure formation in segmented faults

local shear stress corresponding to the material’s fracturestrength τu. After that the fan head can propagatedynamically through intact rock mass at shear stresses belowthe frictional strength. It is known that the field stress in thelithosphere cannot exceed the frictional strength. However,local stresses in the intact rock mass in the vicinity of pre-existing discontinuities (e.g. boundaries between tectonicplates, faults, deep mines, etc.) can reach the fracturestrength levels τu. According to the new approach, pre-existing discontinuities act as stress concentrators creatingthe starting conditions for the fan mechanism, but instability(e.g. earthquakes) occurs due to the development of newfaults in the intact rock mass.

Figure 16 illustrates this feature. Figure 16a shows afragment of the rock mass with the local zone of high shearstress adjoining a pre-existing discontinuity where the fanstructure is generated and a large zone of lower stress wherethe fan head can easily propagate. In Figure 16b the redgraph illustrates shear resistance of the fan head at twostages: nucleation (length of fan head fracture lfanll , strengthττu) and propagation (length of created shear fracture L >>>lfanll , strength τfan). The horizontal dotted line shows the levelof frictional strength τf. The horizontal bold line correspondsto the field stress level τ.

Paradoxically, the low strength of intact rock provided bythe fan mechanism favours the generation of new faults in anintact rock mass over reactivation of pre-existing faults(Tarasov, 2013). This unique feature of the fan mechanismallows the supposition that the majority of dynamic events inthe Earth’s crust result from generation of new faults.However, the proximity of pre-existing discontinuities to thearea of instability caused by the fan mechanism creates theillusion of stick-slip instability on pre-existing faults, thusconcealing the real situation.

Generation of shear rupture rockbursts by the fanmechanismThe fan mechanism could be responsible for some types ofman-made earthquakes. Special studies conducted in SouthAfrican mines (Gay and Ortlepp, 1979; McGarr et al., 1979)show that shear rupture rockbursts, which are seismicallyindistinguishable from natural earthquakes, are generated inintact hard rock (dry quartzite) in zones of highly confinedcompression. It was shown that these mine tremors and

fearthquakes share the apparent paradox of undergoingfailure at low shear stresses, while laboratory measurementsindicate high material strengths.

Figure 17 shows a cross-section of the Earth’s crustinvolving an opening. The graph on the left illustratesvariation of minor stress σ3 with depth. The fan mechanismcan be activated below a critical depth corresponding to thecritical level of minor stress σ3min(hinge) in Figure 3b. Thezone of fan mechanism activity in Figure 17 is shown by thegrey area. The efficiency of the fan mechanism increases withincreasing brittleness of the rock with depth. However,around the opening the minor stress is below the critical levelσ3 < σ3min(hinge). The dotted areas in Figure 17 show zoneswhere the fan mechanism cannot be generated.

Deep openings similar to any pre-existing discontinuityrepresent stress concentrators. If locally elevated shearstresses in the grey area reach the level of ultimate stress τu(see graph in Figure 16) the fan head will be generated in theintact rock mass distanced away from the opening. After thatthe fan head can propagate further spontaneously throughthe zone of lower shear stress, creating new shear ruptureand resulting in a shear rupture rockburst.

The fan mechanism is generally activated in intact rocks.However, pre-existing faults can also be reactivated by thefan mechanism under special conditions. Seismic data and



Figure 15—The fan mechanism is activated predominantly in faultsegments of lower hierarchical ranks where ΔΔfaultf ≥ ΔΔfanf

Figure 16—Fan mechanism operation in vicinity of discontinuities

Figure 17—Nucleation of shear rupture rockbursts by the fanmechanism


fgeological observations suggest that faults strengthen (heal)during the inter-seismic period of the earthquake cycle due tometamorphism and intruding igneous rocks. When thestructure of the healed fault becomes strong enough for thecreation of domino blocks capable of rotating withoutcollapse, it can be reactivated by the fan mechanism. The fanmechanism cannot be generated within fresh faults withunconsolidated layers of weak gouge between the fault faces.

ConclusionsThis paper presents physical rationales for the recentlyidentified fan mechanism generated in hard rocks at highlyconfined compression. In the fan mechanism, the rock failureassociated with consecutive creation of small slabs (known as‘domino blocks’) from the intact rock in the rupture tip isdriven by a fan-shaped domino structure representing therupture head. The fan head combines such unique featuresas: extremely low shear resistance, self-sustaining stressintensification, and self-unbalancing conditions. Due to thisthe failure process caused by the mechanism is inevitablyspontaneous and violent. The mechanism is generated inprimary ruptures and in segmented faults.

The physical and mathematical models presentedhighlight a paradoxical feature of the fan mechanismassociated with the possibility of creating new shear rupturesin intact rock masses at shear stress levels that are signifi-cantly less than the frictional strength. The fan mechanism isthe most energy-efficient shear rupture mechanism for rocksat confined compression. This mechanism causes theunconventional rock behaviour associated with drastic rockembrittlement at highly confined compression. The newmechanism provides a novel point of view for understandingthe nature of spontaneous failure processes, includingearthquakes and shear rupture rockbursts.

AAcknowledgementsThe author acknowledges the support provided by the Centrefor Offshore Foundation Systems (COFS) at the University ofWWestern Australia, which was established under theAustralian Research Council’s Special Research Centrescheme and is currently supported as a node of theAustralian Research Council Centre of Excellence forGeotechnical Science and Engineering and in partnership withThe Lloyd’s Register Educational Trust.

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revisited. Pure and Applied Geophysics, vol. 166. pp. 1575–1594.GAY, N.C. AND ORTLEPP, W.D. 1979. Anatomy of a mining induced fault zone.

Geological Society of America Bulletin, Part 1, vol. 90. pp. 47–58.HEATON, T.H. 1990. Evidence for and implications of self-healing pulses of slip

in earthquake rupture. Physics of the Earth and Planetary Interiors,vol. 64, no. 1. pp. 1–20.

HORII, H. and NEMAT-NASSER, S. 1985. Compression-induced micro-crack growthin brittle solids: axial splitting and shear failure. Journal of GeophysicalResearch, vol. 90. pp. 3105–25.

KINGKK , G.C.P. and SAMMIS, C.G. 1992. The mechanisms of finite brittle strain.PAGEOPH, vol. 138, no. 4. pp. 611–640.HH

KIRBYKK , S.H. and MCCORMICK, J.W. 1984. Inelastic properties of rocks andminerals: strength and rhelolgy. Handbook of Physical Properties ofRocks. Carmichael, R.S. (ed.). CRC Press, Boca Raton, Florida. vol. 3.pp. 139–280.

LYKOTRAFITIS, G., ROSAKIS, A.J., and RAVICHANDRANRR , G. 2006. Self-healing pulse-like shear ruptures in the laboratory. Science, vol. 313. pp. 1765–1768.

MCGARR, A., SPOTTISWOODE, S.M., GAY, N.C., and ORTLEPP, W.D. 1979.Observations relevant to seismic driving stress, stress drop, and efficiency.Journal of Geophysical Research, vol. 84. pp. 2251–2261.

MEGAHID, A.R., SOGHAIR, H., HAGEED, M.A.A., and HAFER, A.M.A.A. 1993.Strength and deformation capacity of slender RC beams. Proceedings ofFracture and Damage of Concrete and Rock – FDCR-2. Rossmanith, H.P.(ed.). Chapman & Hall, London.

MOGI, K. 2007. Experimental Rock Mechanics. Taylor & Francis, London, NewYork.

OHNAKA, M., KUWAHAREKK , Y., YAMAMOTOYY , K., and HIRASAWA, T. 1986. Dynamicbreakdown processes and the generating mechanism for hi-frequencyelastic radiation during stick-slip instabilities. Earthquake SourceMechanics, Geophysics. Monograph Series, vol. 37. Das, S., Boatwright, J.,and Scholz, C.H. (eds.). American Geophysical Union, Washington, DCpp. 13–24.

ORTLEPP, W.D. 1997. Rock Fracture and Rockbursts – an Illustrative Study.South African Institute of Mining and Metallurgy, Johannesburg.

ORTLEPP, W.D., ARMSTRONG, R., RYDERRR , J.A., and O’CONNOR, D. 2005.Fundamental study of micro-fracturing on the slip surface of mine-induced dynamic brittle shear zones. 6th International Symposium onRockburst and Seismicity in Mines, Perth, Western Australia, 9–11 March2005. Potvin, Y. and Hudyma, M. (eds.). Australian Centre forGeomechanics, Perth. pp. 229-237.

OTSUKI, K. and DILOV, T. 2005. Evolution of hierarchical self-similar geometry ofexperimental fault zones: Implications for seismic nucleation andearthquake size. Journal of Geophysical Research, vol. 110, B03303. doi:10.1029/204JB003359

PENG, S. and JOHNSON, A.M. 1972. Crack growth and faulting in cylindricalspecimens of Chelmsford granite. International Journal of Rock Mechanicsand Mining Sciences, vol. 9. pp. 37–86.

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SHIMADA, M. 2000. Mechanical Behaviour of Rocks under High PressureConditions. Balkema, Rotterdam.

SIBSON, R.H. 1982. Fault zone models, heat flow, and the depth distribution ofearthquakes in the continental crust of the United States. Bulletin of theSeismological Society of America, vol. 72. pp. 151–163.

STAVROGIN, A.N. and TARASOV, B.G. 2001. Experimental Physics and RockMechanics. Balkema, Rotterdam.

TARASOV, B.G. 2008. Intersonic shear rupture mechanism. International Journalof Rock Mechanics and Mining Sciences, vol. 45, no. 6. pp. 914–928.

TARASOV, B.G. 2010. Superbrittleness of rocks at high confining pressure.Keynote Address, Fifth International Seminar on Deep and High StressMining, Santiago, Chile. pp. 119–133.gg

TARASOV, B.G. 2013. Depth distribution of lithospheric strength determined bythe self-unbalancing shear rupture mechanism. Proceedings of Eurock2013, Poland. pp. 165-170.

TARASOV, B.G. and ORTLEPP, W.D. 2007. Shock loading-unloading mechanism inrockburst shear fractures in quartzite causing genesis of polyhedral sub-particle in the fault gouge. Proceedings of the 4th International Seminaron Deep and High Stress Mining, Perth, Australia. pp. 183–192.gg

TARASOV, B.G. and RANDOLPHRR , M.F. 2008. Frictionless shear at great depth andother paradoxes of hard rocks. International Journal of Rock Mechanicsand Mining Sciences, vol. 45. pp. 316–328.

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TARASOV, B.G. and POTVIN, Y. 2012. Universal criteria for rock brittlenessestimation under triaxial compression. International Journal of RockMechanics and Mining Sciences, vol. 59. pp. 57–69.

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IntroductionTwo Rivers Platinum Mine, a joint venturebetween African Rainbow Minerals (ARM,55% ownership and management) and ImpalaPlatinum (45% ownership and smelting,refining, marketing), is situated on the easternlimb of the Bushveld Complex near the town ofSteelpoort in Limpopo Province (Figure 1).The mine is underlain by rock formations ofthe Critical Zone (Winterveld Norite-Anorthosite) and the Main Zone(Winnaarshoek Norite-Anorthosite). Twoeconomically significant PGM-bearinghorizons, namely the UG2 chromitite andMerensky Reef, are located in the UpperCritical Zone, separated by approximately 140 m of norites and anorthosites. The UG2chromitite seam is extracted throughmechanized bord and pillar mining at a rate of300 000 t per month at an average ore gradeof 4.10 g/t (PGE + Au). The undergroundwworkings on the UG2 horizon are currentlybetween 40 m and 800 m below surface, at anaverage orebody dip of 9 degrees, and areaccessed through two decline shafts roughly 3km apart. The Merensky Reef is extracted on asimilar bord and pillar mining layout, currentlyapproximately 30 m below surface. TheMerensky Shaft is planned to extract 14 500 tof ore per month in the trial mining phase, andthereafter it will build up to 180 000 t/month.

Regional geologyThe UG2 chromitite layer has been intersectedin over 100 boreholes on the host farmDwarsrivier. The lithological sequence, fromthe base upwards is illustrated in Figure 2. TheUG2 reef horizon lies within a competentpyroxenite band, which extends 2.5 m into thehangingwall and up to 1 m in the footwall. TheFootwall 1 Unit is coarse-grained topegmatoidal pyroxenite/harzburgite and isapproximately 1 m thick.

The UG2 averages approximately 180 cmin thickness, and internal pyroxenite andnorite partings may be present. These in somecases have highly angular margins and appearto have been derived from erosion andtransport within the UG2 of pre-existinglayers. To the south and deep central part ofthe farm, a large area is characterized by thepresence of split reef, whereby a pyroxenite ornorite lens up to 6 m thick is situated approxi-mately two-thirds from the base of the UG2.

Disseminated sulphide mineralization isgenerally present, especially around themargins of internal pyroxenite partings. Thismainly comprises pentlandite and chalcopyrite,with lesser pyrrhotite. There is minimalcohesion between certain layers due to themineral composition of the contacts.

The UG2 is overlain by poikilitic pyroxenitethat hosts up to three chromitite ‘leader’ layers(collectively termed the UG2A chromititelayers). The pyroxenite is intersected by threemajor joint sets; Joint set J1 dips at approxi-mately 85° and has a strike of approximately050° E of N; Joint set J2 dips at approximately85° and strikes 120° E of N. Joint set J3 dips at

Unique fall-of-ground prevention strategyimplemented at Two Rivers Platinum Mineby A. Esterhuizen*

SynopsisSince 2005 Two Rivers Platinum Mine has set out on an initiative toactively monitor and control ground conditions on a daily basis, by makinguse of borehole cameras and pro-actively amending the support and miningstrategies based on day-to-day observations of the hangingwall conditions.Today the borehole camera observations form part of the Rock EngineeringDepartment’s daily function, and the size and frequency of falls of groundand ensuing accident rates have been drastically reduced since implemen-tation of the system. The Two Rivers Platinum fall-of-ground managementsystem aims to support 100% of the possible fallout thickness, based onongoing data gathering and interpretation, thereby ensuring safety andlimiting support cost.

Keywordsstrata control, fall-of-ground management.

* Open House Management Solutions (Pty) Ltd.© The Southern African Institute of Mining and



Unique fall-of-ground prevention strategy implemented at Two Rivers Platinum Mine

70° to 80° and strikes 080° to 100° E of N. The jointingextends through the reef into the footwall, but rarely extendsinto the HW2 anorthosite.

The UG2 reef horizon is divided into two portions, theUG2 proper (1.8 m), and the UG2 Leader reef (30-40 cm) byan internal pyroxenite layer (30-40 cm). The mining propertyis also affected by numerous features related to late-stageBushveld intrusions. These include potholes and Fe- and Mg-enriched replacement pegmatoid bodies emplaced into theBushveld cumulate stratigraphy. Generally, thinning of thereef layer, coupled with the steep and erratic dips around thepotholes, results in a total ground loss during undergroundmining operations. Potholes are present on varying scales,from the large regional potholes that affect the Merenskystratigraphy in the northwestern Bushveld Complex to smallcircular features less than 10 m in diameter. Currentinformation at Two Rivers suggests that potholes affect boththe Merensky pyroxenite and the UG2. These potholes areenvisaged to be similar in their development to thoseencountered in the western Bushveld Complex. However, noindications of regional potholes affecting the Merenskystratigraphy have been identified.

Numerous wide dolerite dykes intersect the miningproperty, striking in NW-SW and NW-SE directions. Thedykes are brittle, but generally easily negotiated with fewstability-related issues.

The pyroxenite is prone to weathering, especially in theshallower workings of the mine where some water inflow isexperienced. The oxidation of specifically the contact betweenthe HW1 pyroxenite and HW 2 anorthosite, referred to as theHW 1/2 contact, is problematic and resulted in numerouslarge collapses during the mine’s early history. It is theweathered nature of this contact, which is located 2.5 mabove top of UG2, and the necessity of observing itscondition, that led to the implementation of the boreholecamera system.

Mine layoutThe mine is laid out in a regular checkerboard bord and pillarlayout, with panel widths ranging between 6 m and 12 m,depending on the rock mass rating. Elastic pillars aredesigned according to the Hedley and Grant pillar formulaand increase in size with increasing depth below surface.Shaft stability is ensured by means of squat pillars.Although the orebody dips at 9-11 degrees, the mine isundercutting a mountain, which results in a rapid increase indepth below surface.

Two Rivers Platinum is a fully mechanized mine, whichperfectly suits the orebody geometry, with nine half-levels(sections) on the Main Shaft and seven half-levels on theNorth Shaft. A section consists of at least eight panels and ismined with a fleet consisting of three load haul dumpers

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Figure 1—Regional setting of Two Rivers Platinum Mine

Figure 2—Simplified diagram of the lithological sequence

excavations as well as the boxcuts and ventilation shafts

Figure 4—Diagram showing a typical section layout and miningsequence

f(LHDs), one roofbolter, one drill rig, one utility vehicle, andone emulsion utility vehicle (UV). Currently the mine isproducing roughly 300 000 t of ore per month through thetwo shafts. Each section is equipped with a strike conveyor,wwhich in turn tips on the main belt through an orepass.

SupportAAs regional support, elastic pillars are left on a checkerboardpattern, designed with a factor of safety 1,6 and width toheight ratio 3. Due to the frequency and size distribution ofthe potholes and other major geological features such asshear zones and dykes that are left unmined, no regional/barrier pillars are purposely designed.

As in-stope support, 1.5 m long × 18 mm diameter fullcolumn grouted resin bolts are installed spaced 1 m × 2 mapart to a diamond pattern. The support is installed using 60-second spin-and-hold resin and is aimed primarily at keyblock suspension and beam building. Where the need tosuspend the entire HW1 beam is identified by means ofborehole camera observation, 4 m long, 18 mm diameter(380 kN) pre-tensioned, full column grouted cable anchorsare installed. The mine currently installs roughly 22 000 boltsand 3 000 cable anchors per month. An average of 3 000 m2

of shotcrete and thin sprayed liners is also applied per monthin friable areas.

Major geological features such as shear zones and dykesare left unmined to prevent off-reef mining and exposure ofemployees to hazardous ground. Where excavations have totraverse wide shear zones, the sheared and weathered HW1pyroxenite makes conventional drilling and installation ofsupport impossible. In these conditions the HW1 pyroxeniteis removed as the HW2 anorthosite behaves significantlybetter under these conditions.

AAnalyses of fall-of-ground data and implementationof a strata control systemShortly after the onset of mining in 2005, massive falls ofground (FOGs) occurred at a rate of 0.8 collapses per month.Some of these collapses were a result of wedge failurebrought about by the interaction of the normal near-verticaljjoints with flatter low-angle or domed joints. The main causeof most of these collapses was found to be the inability of theinexperienced mining crews to identify and timeously supportthe structures. Of concern, though, was the high frequency ofcollapses extending up to the HW1/2 contact, which resultedin fallout thicknesses of up to 2.5 m, which is far above theeffective length of the installed primary support. Thesecollapses were unexpected, as the initial geotechnical studiesand risk assessment did not identify this particular contact asbeing of high potential risk. By the end of 2006, 50% of allcollapses dislodged from this contact.

Examination of the collapses identified early on that theHW1/2 contact and HW1 pyroxenite are prone to oxidation,wwhich reduces the cohesion and self-supporting ability of thebeam. The beam would then collapse between joints andcould extend up to 20 m in strike length.

The following conclusions were made based on FOGanalyses:

➤ As the contact is located mine-wide at a fairlyconsistent distance above the UG2, it was obvious that

f fdetermination of the physical condition of the contact,and not only the location of the contact, was crucial

➤ Due to weathering in certain areas of the mine, beambuilding was not being achieved by the installedsupport, as alteration on joint walls, and subsequentreduction in cohesion, allowed blocks to rotate andslide out between support units

➤ Total suspension of the pyroxenite beam is requiredunder certain conditions

➤ Production personnel were unable to successfullyidentify low-angle/domed-shape joints, whichaccounted for the other 50% of collapses

➤ There was an urgent need to identify areas where theHW1/2 contact was likely to be oxidized

➤ Low-angle joints are scattered randomly across themining property, but joint mapping showed that thesefeatures tend to cluster around large potholes.

Based on the findings of the analyses, a FOGmanagement plan was implemented, based on localexperience and observations.

Based on the identified requirements, two strata controlobservers were employed to assist with daily undergrounddata acquisition and identification of weathered groundconditions and low-angle features. In order to assist with theinspection of the HW1/2 contact, a borehole camera waspurchased. The camera was used on an ad-hoc basis wherethe need was identified and did not form part of the normaldaily routine.



Figure 5—Diagram showing the support layout

Figure 6—FOG heights for 2005 to 2006


fIn addition to the modifications to the Rock EngineeringDepartment structure, the production team was given regularunderground training in the identification and treatment oflow-angle joints. The original risk assessment was updatedand the current support at the time, resin bolts, was supple-mented with additional 4 m cable anchors where deemednecessary. A great deal of attention was given to raisingawareness amongst mining personnel with regard to low-angle features and the problematic HW1/2 contact.

The changes to the strata control system proved to be astep in the right direction. Although the 2 m high collapseswwere not entirely prevented, the overall size of the collapseswwas reduced due to the installation of cable anchors wheredeemed necessary.

In early 2008 the mine suffered another massive collapse.This time the collapse occurred in a previously identified andsupported area, and trapped a LHD. Investigation showedthat the extent of the unstable area was underestimatedduring the initial support recommendation and, in addition,the installed support spacing was not in accordance with theRock Engineering recommendation.

Based on the prior success gained from the addition ofobservers and borehole cameras, it was decided to continueand improve on this system, which at the time was new andnot used on a daily basis in South African mines.

The Rock Engineering Department staff complement wasextended to include an observer and a borehole camera foreach section. The borehole inspections now became part ofthe daily functions of the rock engineering observers, as wellas daily inspections of all available working panels. Thisinformation was then relayed back to the strata control officerand mine overseer on a daily basis for recommendation andcommunication. All installed support was inspected and thesupport spacing measured on a daily basis, with weekly over-inspections of all cable anchors and applied thin-skin liners.The mine’s support standard was amended to ensure that twodedicated camera holes were drilled with every supportround. The holes are 4 m deep and 40 mm in diameter,spaced no more than 4 m apart, and drilled midway betweenthe center line and the sidewalls. After drilling, the holes areclearly demarcated and no support units may be installed inthese holes. In addition, the width of excavations mining in,or advancing through, deteriorated ground conditions wasreduced from 12 m to 6 m, reducing exposure of weaknessplanes.

The success obtained through this system is self-evidentwwhen one considers the mine’s total FOG history from 2005to 2011. There has been a clear and significant improvementin the FOG height and overall size since the introduction ofthe camera system. The fallout size has been reduced to thepoint where, since 2010, most FOGs are barring-relatedincidents.

The system incorporated at Two Rivers Platinum Mine,along with detailed daily over-examination of working panelsby competent persons and proper training of productionpersonnel, resulted in a proactive system where potentiallyunstable ground is identified and assessed on a daily basis.The borehole camera system is quick, simple, and easy tounderstand. Feedback to mining personnel is immediate andvvisual, which helps foster trust and appreciation of thesystem amongst miners, which in turn helps to ensurecompliance to the standard.

f fUsing the information obtained from the borehole cameraobservations and FOG investigations, it became apparent thatthe mine has four different fallout height zones, and if thesezones can be correctly and timeously identified, 100% of thefallout height can be supported 100% of the time, withoutresorting to a mine-wide blanket worst-case scenario supportdesign. The zones include:

A. Stringer collapse (0.5 m height)B. Low-angle joint wedge collapse (1.5 m height)C. HW1/2 beam collapse (2.5 m height)D. Shear zone self-mining (up to 4 m height).Figure 10 indicates that the 2.5 m high collapses related

to the hangingwall anorthosite/pyroxenite contact (zone C)have basically been eradicated. This is a result of the abilityto identify and treat the structure where necessary. Alsoobvious is the continuance of the 1.5 m high collapses (zoneB). These collapses are related to low-angled joints anddomes, which are randomly spaced and oriented, and harderto identify and treat. Approximately 90% of all low-angledjoint/dome collapses on Three Rivers Platinum occur in theunsupported area between the face and the last line ofsupport immediately following or concurrent with the blast.The lack of these collapses in the supported area pays tributeto the ability of the entry examination team, in conjunctionwith the relevant service departments, to identify and treatthe structures.

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Figure 7—Reduction in FOG size from 2005 to 2007 due to theintroduction of observers and a borehole camera

Figure 8—Diagram illustrating how the support strategy is adjustedaccording to the local ground conditions. The green line represents aweathered contact and the yellow bars indicate camera boreholes

fAll designs and assumptions regarding fallout thicknessare based on a detailed actual FOG database. Every incidentis thoroughly inspected, regardless of the size or consequenceof the collapse. Despite the thoroughness, only 75 cases wereinvestigated over a period of eight years, representing lessthan 0.05% of the total mined area. In order to verify theconclusions, and to gain confidence in the assumptions, asynthetic database was created, using the software packageJJBlock. JBlock allows for the creation of a jointed beam basedon given joint set parameters. The resultant key blocks canthen be analysed for stability.

AActual vs. synthetic database The mine’s FOG database shows that the number of collapsesper square metre mined has steadily decreased since theintroduction of the current strata control system. Althoughthis is a desirable result of an effective system, it limitsobservational ability, and with it, the opportunity to learnand gather information. As with any statistical information, alarger FOG database will provide more accurate output andprovide more confidence in the assumptions made during thedesign process.

Jblock was used to create a synthetic database, in order tosupplement the actual database and verify the conclusions.Using Jblock, 10 845 mining steps (advance blasts) and1.17 million square metres were simulated, which resulted inthe analyses of 20 000 key blocks with 5 iterations per block.The results obtained were very similar to the actual database,and confirmed the prediction of four fallout height zones.

The results of the synthetic database, shown in Figure12, were obtained by inserting a hangingwall-parallel contactat the actual location of the anorthosite/pyroxenite interface.The results indicate that the 95% FOG height is 2.95 m, witha maximum expected apex height of 3.58 m. The actualdatabase concluded that the 95% FOG height is 2.7 m, andthat the highest actual collapse was 3.5 m. There were otherhigher collapses in the actual database, but these wererelated to self-mining shear zones, and did not dislodge fromthe hangingwall contact. As the contact location is consistentat 2.5 m above the reef, an associated collapse with a falloutheight of 3.58 m is not deemed probable because the jointingdoes not penetrate above the contact into the anorthosite.

The stringer collapses were analysed in a similar fashion,where the structure was modelled as a hangingwall-parallel



Figure 9—Total mine history (2005 to 2013) for FOG size (tonnage)

Figure 10—Total mine history (2005 to 2013) for FOG height. The analyses clearly indicate four separate fallout zones

Figure 11—Decrease in FOGs per m2 mined


contact, placed at the known location. Here too the resultsclosely resembled the actual database (Figure 13).

The actual database shows that for the stringer layer, a95% fallout thickness of 0.4 m should expected, whereas themaximum experienced fallout height was 0.56 m. Thesynthetic database concluded that the 95% fallout height isexpected to be 0.56 m with a maximum expected wedge apexof 0.77 m. This maximum expected apex height for thestringer layer is deemed more probable than that predictedfor the HW1/2, as the joints are continuous in the pyroxeniteand penetrate through the stringer layer.

Conclusions➤ Based on the study, synthetic databases can be used to

supplement the actual mine fall-of-ground database inorder to build a larger database with more accuratedesign parameters

➤ Based on the results of the study and experience on-mine, support design is divided into four zones, each

fwith a specific support length and spacing in order tosupport 100% of the maximum fallout height expectedfor that specific zone

➤ The strategy greatly reduced the mine’s fall-of-groundfrequency and size, while effectively controllingsupport costs.

AcknowledgementsThe authors wish to thank Mr J.D. Bosman for the technicalreview, and the Management of Two Rivers Platinum Minefor approval of the release of the information published inthis paper. We also thank Mr Q. Grix for his input and dataacquisition.

ReferencesGEOLOGICAL DESCRIPTION – GEOLOGY DEPARTMENT. Two Rivers Platinum Mine public

network, 2013.

FOG DATABASE – ROCK ENGINEERING DEPARTMENT. Two Rivers Platinum Mine -OHMS. ◆

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Figure 12—Actual and synthetic cumulative fallout frequencies for HW1/2 interface

Figure 13—Actual and synthetic cumulative fallout frequencies for the stringer layer

IntroductionSupport in US coal mines is divided into threemain categories:

➤ Primary support that is installed on-cycle, typically resin-grouted rebar,possibly with straps and/or screen

➤ Secondary support that is installed asadditional support after the primarysupport, mainly in intersections, due tothe wider effective span, and consistingtypically of cable bolts

➤ Supplementary support that is installedas additional support after the secondarysupport due to poor localized conditions,and typically consisting of truss bolts,cribs, and standing support.

Primary roof support represents the firstline of defence against rock-related falls ofground in underground mines, and improperutilization and/or understanding of primarysupports applicability and behaviour can becostly and adversely affect rock-related safety.This is a major concern for undergroundmines, as roof support is the single most costlyexpense from a mining operations perspectiveand is the production bottleneck. This isfurther backed by the evidence that, in theUSA, fatalities and hundreds of injuries stilloccur each year because of rib, roof, andmassive roof falls, as shown in Figure 1(Mark, Pappas, and Barczak, 2009).

Additionally, the fully-grouted passiverebar (FGPR), fully-grouted tension rebar(FGTR), and resin-assisted mechanical anchorbolts (RMABs), which constitute a largeportion (89%) of the 68 million bolts installedeach year in underground mines, can vary incost quite dramatically. As a rough estimate,the FGPR is the least costly support, whereasthe FGTR is roughly 10%, and the RMAB isaround 29% greater than the FGPR. (Tadoliniand Mazzoni, 2006; Spearing and Gadde,2011).

To mitigate this concern a study wasconducted in 2010, funded by the NationalInstitute of Occupational Safety and Healthand undertaken by Southern Illinois Universityof Carbondale and Peabody Energy, to assess

In situ monitoring of primary roofbolts atunderground coal mines in the USAby A.J.S. Spearing* and A. Hyett†

SynopsisPrimary roof support represents the first line of defence against rock-related falls of ground in underground mines, and improper utilization ormisunderstanding of the applicability and behaviour of primary supportcan be costly from a safety standpoint. This is a major concern forunderground mines, as roof support is the single most costly expense froma mining operational perspective. This is further backed by the evidencethat, in the USA, hundreds of injuries and fatalities still occur each yearbecause of rib, roof, and massive roof falls. Additionally, the fully-groutedpassive rebar, fully-grouted tension rebar, and resin-assisted mechanicalanchor bolts, which constitute a large portion (89%) of the 68 million boltsinstalled each year in underground mines in the USA can vary in cost quitedramatically. To mitigate this concern a study was conducted in 2010 bythe National Institute of Occupational Safety and Health, in conjunctionwith Southern Illinois University of Carbondale, to assess the performanceof primary roofbolts in underground coal mines for improved safety andcost. This was accomplished using underground roofbolt monitoringsolutions, field data, and numerical modelling to better understand thequasi-static behaviour of underground coal mine roofs and the responsebehaviour of the bolts. In particular, over 170 instrumented extensometers,closure meters, shear meters, fully-grouted passive rebar, fully-groutedtension rebar, and resin-assisted mechanical roofbolts were installed atthree coal mines across the USA. Of these three mines, two used the roomand pillar extraction method and the other used the longwall extractionmethod. There was no evidence to indicate a difference in performance ofthe active primary roofbolts compared with the passive primary roofbolts.Additionally, in the initial loading phase, the active bolts showed nodifference in loading, indicating that tension bleed-off is of more of aconcern than originally thought. Lastly, for the initial computer modellingstudies, challenges still remain in obtaining a good match to the in situ boltmeasurements and replicating the discontinuous roof rock and in situ boltbehaviour over time.

Keywordsprimary support, roofbolts, in situ monitoring.

* Southern Illinois University Carbondale, Illinois,USA.

† Yield Point Inc., Ontario, Canada© The Southern African Institute of Mining and



In situ monitoring of primary roofbolts at underground coal mines in the USA

the performance of primary roofbolts in underground coalmines in the USA for improved safety and cost. This wasaccomplished using underground roofbolt monitoringsolutions, field data, and numerical modelling to betterunderstand the quasi-static behaviour of underground coalmine roofs and the response behaviour of the bolts.

Furthermore, over 170 instrumented extensometers,closure meters, shear meters, fully-grouted passive rebar(FGPR), fully-grouted tension rebar (FGTR), and resin-assisted mechanical roofbolts (RMAB) were installed at threecoal mines across the USA. Of these three mines, two usedthe room and pillar extraction method and the other used thelongwall extraction method.

Mine sitesThree underground coal mines were selected for the project.The sites needed to have similar immediate rock lithology,and mine management needed to provide the equipment andlabour to carefully install the expensive instrumented boltson-cycle, which adversely affected mine productivity. Theactual mine names are not disclosed, but they are referred toas mines A, B, and C hereafter. Mines A and B were bothroom and pillar coal operations located in southwesternIndiana, and Mine C was a longwall operation innorthwestern Colorado (Figure 2).

In the following sections, the layout of the instrumen-tation sites at each mine site, the local geology of the instru-mentation sites, and the instrumentation itself will bediscussed.

Rockbolts and instrumentation

RRockboltsThe rockbolts were installed on-cycle with the productionoperations as primary support at both room-and-pillar mines.At the longwall mine, the rocksbolts were supplementalsupport because primary support had already been installedduring panel development. Reiterating, the three bolt systemscompared in this project were fully-grouted passive rebar(FGPR), fully-grouted tension rebar (FGTR), and resin-assisted mechanical roofbolts (RMAB). The FGPR isconsidered a passive support because it is not tensioned oninstallation, whereas the FGTR and RMAB are consideredactive because they are tensioned on installation. As thenames suggest, FGPR and FGTR support are installed withfull resin encapsulation, while the RMAB utilize an anchor atthe back of the hole with a 1.22 m (4 ft) resin encapsulation.

All three mines used no. 6 (19 mm or 0.75 inch nominaldiameter) Grade 60 fully grouted passive rebar as theirprimary support; however, after slotting of the rebar, which isrequired for instrumenting the rockbolts, the residual yieldcapacity of the bolts was well below the requirements set outby the mines’ ground control plans. Therefore a bolt withhigher yield and ultimate capacity, (20 mm or 0.804 inchGrade 75 rebar) was chosen for the instrumented bolts. Acomparison of the yield and ultimate load capacity of the no.6 Grade 60 and 20 mm (0.804 inch) Grade 75 bolts is shownin Table I. All of the rockbolts, bolt plates, and resin weredonated from the same manufacturer. This was to eliminatevendor-related variability of the materials.

InstrumentationAll past studies related to roofbolt monitoring have beenconducted mainly through the National Institute ofOccupational Safety and Health (NIOSH), by Signer from1984-1997 (Serbousek and Signer, 1984; Signer, 1988;Signer, Franklin, Mark, and Hendon, 1993; Signer, Cox, andJohnston, 1997). For those studies the instrumentedrockbolts were equipped with a short baselength (<25 mm)resistive foil strain gauges. The shortcoming of thistechnology was that only 10% coverage of the bolt wasachieved due to the shortness of gauges. Additionally, theloadings that were obtained were highly localized and theentire axial loading profile of the bolts was not wellrepresented.

In contrast to these past studies, a new technology wasutilized in an attempt to better capture the axial loadingprofile of the rockbolts. For this, rockbolts were fitted with

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Figure 1—Fall-of-ground injuries in US underground coal mines (MineSafety and Health Administration, 2010, 2011; Reisterer, 2011)

Figure 2—Locations of Mines A, B, and C

Table I

Yield load and ultimate load of primary support andinstrumentation (Spearing, et al., 2012)

Bolt type Yield load (kN) Ultimate load (kN)

#6 Grade 60 forged head 119.75 (minimum) 179.62 (minimum)0.804in. Grade 75 threaded 184.16 (actual) 257.31 (actual)0.804in. Grade 75 bar 183.25 (actual) 261.27 (actual)

Yield load (kN)

119.75 (minimum)184.16 (actual)183.25 (actual)

long baselength (200–500 mm) displacement sensorsdeveloped by YieldPoint Inc. This technology utilizes an arrayof sub-micrometre resolution displacement sensors thatmeasure the displacement of the bolt. Collectively, the arrayof gauges provides an axial loading profile of the bolt overtime. A more in-depth discussion of this technology ispresented by Spearing et al. (2012), but overall, a greatercoverage of the bolt is obtained (75%) as well as an averagedand more representative axial loading profile of the rockbolt.

Once the bolt type, capacity, and technology had beendetermined, the bolts were machined by slotting along theirlength for placement of the sensors (Figure 3). YieldPoint Inc.wwas chosen as the manufacturer because of their competitivecosts, their willingness to be present during instrumentationinstallation at the mine sites, and because they were able todevelop all of the rockbolt instrumentation, as well as closuremeters, extensometer, tilt meters, and data loggers.

The bolts were slotted to a depth of 3.2 mm (0.126 inch)for placement of six 45.7 cm (18 inch) displacement sensors(three on each side). Six sensors per bolt were chosen tomitigate the total cost per bolt while still obtaining a compre-hensive coverage of the bolt. The sensors were placed in anend-to-end arrangement within the machined slots and wereheld in place by epoxy.

The electronics of the sensors were housed in anextended steel head at the end of the rockbolt. When datalogging began, Mine A and Mine C utilized the extended bolthead shown in Figure 4. This bolt head protruded somelength below the hangingwall; eventually, due to movingmachinery at the face, mainly the continuous miner holingthrough the crosscuts, several instruments were knocked outand destroyed. Therefore a more adaptive shallow head(shown at the bottom of Figure 4) was used later at Mine Bto eliminate this problem.

The sensors on the bolts were arranged in two configu-rations. Mines B and C utilized a stacked configurationshown in Figure 5a and Mine A utilized a staggered configu-ration shown in Figure 5b. For the stacked configuration thesensors are placed in a diametrically opposed pattern in themachined slots, and for the staggered configuration thesensors are offset by half the baselength of the sensor. Itwwas felt that the stacked configuration could miss some ofthe localized shearing loads that could occur between the

fsensors, and a staggered arrangement was thereforeconsidered for comparison purposes.

The data was collected using data loggers, shown inFigure 6. Each data box could store over 30 000 readings andwas equipped with four channels (one per rockbolt) that werewired to the instruments. The data loggers were manually setto take readings every hour, which could then be retrievedvia a USB connection and custom software also developed byYieldPoint. Routine visits to the instrumentation site werescheduled to download the data. The data, which wasrecorded in microstrain, was then manipulated to obtain axialload, axial strain, and axial stretch. A conversion factor of153 μ-strain per ton was used, based upon the cross-sectional area of the machined rebar and the elastic modulusof the bolt steel.



Figure 3—Slotted rebar for sensor placement (Kostecki, 2013)

Figure 4—Instrumented roofbolt with an extended-length head (top)and shallow head (bottom) (Kostecki, 2013)

Figure 5—Representation of the stacked gauge orientation (a) used atMine B and Mine C, and the staggered orientation (b) used at Mine A(Spearing, et al., 2012)

Figure 6—d4 logger data-log box by YieldPoint Inc. (Kostecki, 2013)


Instrumentation sites and local geology The selection of the instrumentation sites was vital to theoverall scope of this project. There were nine instrumentationsites in total – one for each of the three roofbolt systems (i.e.FGPR, FGTR, RMAB) for each of the mines. The local geologywwas investigated by a consulting geologist (Padgett, 2010).

MMine AInstruments at Mine A were installed as primary support on-cycle. Figure 7 shows the instrumentation sites. From left toright in Figure 7 the entries were numbered 5, 6, and 7.These entries were chosen for several reasons. First,instruments were placed in three adjacent entries so that thegeology was as similar as possible. Next, the adjacent entryto no. 5 was a belt entry and the adjacent entry to no. 7 wasa return-air entry blocked off by a stopping. Finally, becausemonitoring of the instruments was to last several months,these entries were located in an area such that productionwwould not be hindered over the entire monitoring period.

Instrumented roofbolts were installed in the intersectionsand mid-pillar regions of each entry. The arrows in Figure 7show the direction in which the face was advancing duringinstrument installation, and therefore the mid-pillar wasmined first and the mid-pillar instruments were installed afew days prior to the intersection instruments. The patternsat each site were identical. The diagonal pattern shown acrossthe intersections was of particular importance as thisrepresents the longest span, and therefore would offer thegreatest chance of capturing the highest displacements andloadings over time. The FGTR instrumented bolts wereinstalled in entry 5, FGPR in entry 6, and RMAB in entry 7.The non-instrumented support surrounding each test site,shown in Figure 7, was of the same type as the instrumentedsupport in each area. Most importantly, each instrumentedbolt was zeroed prior to instrumentation using a d-READERinstrument reader provided by YieldPoint Inc. The entrieswwere 6.1 m (20 ft) wide with 24.3 × 24.3 m (80 × 80 ft)centre-to-centre pillars (Spearing, et al., 2011).

Two extensometers were installed at each intersectionand one in the mid-pillar. The extensometers were anchored3.66 m (12 ft) into the roof to measure differentialmovements. Two tilt-meters (shear meters) were installed in

each entry, one in the mid-pillar and one in the intersection.Finally, closure meters were placed at each mid-pillar andintersection (Kostecki, 2013).

Each instrument was given an individual identificationnumber to describe the instrument type (i.e. extensometer,closure, tilt, rockbolt) and the mine location. For example, thecentre instrumented bolt in the intersection of the no. 5 entryis 100575023. In this case the first 5 denotes the mine site(in this case Mine A). The 7 denotes the instrument type (inthis case an instrumented rockbolt) and finally, the 5023denotes the unique instrument identification number. Asimilar nomenclature is followed for remaining instruments,except the 7 is replaced by a 9 for extensometers, 13 fortiltmeters, and a 2 for closure meters.

Mine A local geologyMine A is located in southwestern Indiana and exploits theDanville No. 7 seam of the Dugger Formation. Borescopes to4.27 m (14 ft) above the coal seam were taken at eachinstrument site and the roof lithology is shown in Figure 8. Inthe immediate roof, the first 0.7 m (2.3 ft) on average was amedium-grey silty shale. This was overlain by a mediumdark gray shale to the top of the 4.26 m (14 ft) borescope.Hairline separations were present in the bottom 0.3 m (1 ft)of the immediate roof in entries no. 5 and no. 7, with themost discernible separation in the no. 7 mid-pillar, where a6.35 mm (0.25 inch) -45° hairline separation existed. Withinthe no. 6 entry, hairline separations existed from the0.61–0.91 m (2–3 ft) level. The immediate floor was a softmedium-grey claystone common to Illinois Basin Mines. Thecoal seam thickness was 1.34 m (4.4 ft), on average, and wasrelatively flat-lying. The average mine height was 2.29 m(7.5 ft) with an overburden of 97.5 m (320 ft).

Cutters existed throughout the mine site, shown inFigure 9, along most of the entries and crosscuts. The mostsignificant was a 15.2–30.5 cm (0.5–1.0 ft) cutter at theeastern corner of the no. 6 intersection. A normal fault alsoexisted in the no. 7 entry. This was not discovered until afterthe instruments had been installed and was not intended aspart of the project design. The normal fault had a strike in theN10°W direction with a dip of 20° and a throw of 5 ft, whichcompletely displaced the coal seam. A diagram of the normalfault is shown in Figure 10.

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Figure 7—Instrumentation layout at Mine A, comprising instrumented bolts: multi-point extensometers (#), shear meters, (+) and closure meters (*). Mine A:(o) – normal primary bolts used by mine, (o) –- [FGTR], (o) – [FGPR] and (o) – [RMAB] (Spearing, et al., 2011)

MMine BInstruments at Mine B were installed as primary support on-cycle. The instruments were all installed in the same entry(i.e. entry 3 – Figure 11). This was mainly because ofvventilation issues at the mine, and limited access to theadjacent entries at the time of installation. Parallel entriessimilar to Mine A were initially designed for.

Instrumented roofbolts were again installed in theintersections and mid-pillars regions. The arrows inFigure 11 show the direction in which the face wasadvancing during instrument installation. The FGPR instru-mented bolts are denoted by the red circles up to crosscut 12in Figure 11, RMAB are blue up to crosscut 13, and the FGTRare green up to crosscut 14. The non-instrumented supportsurrounding each test site was of the same type as the instru-mented support, and each instrument was zeroed prior toinstrumentation.

Two extensometers were installed at each intersectionand one in the mid-pillar, as well as tiltmeters and closuremeters. The instrument identification numbers follow thesame scheme as previously discussed for Mine A. The entrieswere 5.5 m (18 ft) wide with 22.9 × 22.9 m (75 × 75 ft)centre–to-centre pillars (Spearing, et al., 2011).



Figure 8—Borescope logs for Mine A (Spearing and Gadde, 2011)

Figure 10—Mapping of fault located in the no. 7 entry of Mine A(Spearing and Gadde, 2011)

Figure 9—Cutter and fault mapping for Mine A (Spearing and Gadde,2011)

Figure 11—Instrumentation layout at Mine B, comprising instrumented bolts: multi-point extensometers (#), shear meters (+) and closure meters (*). Mine B: (o) – normal primary bolts used by mine, (o) – [FGPR], (o) – [RMAB], and (o) – [FGTR] (Spearing, et al., 2011)


Mine B local geologyMine B is also located in southwestern Indiana, and exploitsthe Springfield No. 5 coal seam of the Petersburg Formation.The depth to the instrument site was 106.7 m (350 ft) onaverage. Borescopes were taken at Mine B as at Mine A,although only four borescopes were obtained in total. Thelithology for Mine B is shown in Figure 12.

The immediate roof comprised a black shale in the first0.6 1m (2 ft) overlain by a dark grey shale with limestonelenses up to 3.05 m (10 ft) in the most extreme case. Thiswwas overlain with a brown to medium grey sandy shale up tothe top of the 4.27 m (14 ft) borescope. Hairline separationswwere found from 0.15 m (0.5 ft) up to almost 1.83 m (6 ft).The area was again underlain by weak underclay.

MMine CInstruments at Mine C were installed as supplementalsupport. Figures 13–17 show the instrumentation sites. Thegate roads were already developed and had been supportedwwith primary and secondary support. The instrumentedsupports were installed in the mid-pillar and intersectionsand were all located in the same entry. The FGPR bolts wereinstalled in the no. 86 crosscut and mid-pillar, the FGTR boltswwere installed in the no. 84 crosscut and mid-pillar, andRMAB bolts were installed in the no. 82 mid-pillar andcrosscut. The direction of advance of the longwall is shown inFigure 13.

Two extensometers were installed at each intersectionand one in the mid-pillar, as well as tiltmeters and closuremeters. The instrument identification numbers followed thesame scheme as previously discussed. The gate road was 5.8m (19 ft) wide with 41.1 × 61.0 m (135 × 200 ft) abutmentpillars at a depth of 366 m (1200 ft) (Spearing, et al., 2011).

The primary support at the mine consisted of FGPR boltsspaced at 1.52 m × 1.52 m (5 × 5 ft) spacing along andacross the entry, with wire mesh. The secondary supportused a similar bolting pattern but added steel straps betweenthe previously installed primary supports. The tailgatesupport also included 22 inch (56 cm) metal cans or cribbingat 3.05 m (10 ft) intervals (Reisterer, 2011). All in all, thetailgate was very well supported prior to and after installationof the instrumentation.

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Figure 13—Instrumentation site at Mine C relative to advancinglongwall. The arrows denote the direction of advance (Reisterer, 2011)

Figure 14—Generalized view of the instrumented bolt locations for MineC (Reisterer, 2011)

Figure 15—FGPR at mid-pillar and intersection, Mine C instrumentationsite (crosscut 86) (Spearing, et al., 2011)

Figure 16—FGTR at mid-pillar and intersection, Mine C instrumentationsite (crosscut 84) (Spearing, et al., 2011)

Figure 12—Roof lithology for Mine B instrumentation site (Spearing, et al., 2011)

MMine C local geologyMine C is a longwall coal mine located in Colorado andexploits the Wadge coal seam. Borescopes were conducted atMine C as at mines A and B. The lithology for Mine C isshown in Figure 18.

The immediate roof consisted of broken shale (Unit A) inthe first 0.19 m (0.625 ft) on average. This was overlain byshale (Unit B) up to 0.52 m (1.7 ft) on average. Interbeddedsandstone and shale (Unit C) overlaid the shale up to 1.52 m(5.0 ft) in the most extreme case. Sandstone with minorshale interbeds (Unit D) constituted the region 2.59–2.89 m(8.5–9.5 ft). Above this region were interbedded shale andsandstone (Unit E), sandstone and shale (Unit F), and shale(Unit G). Water ingress was also noticed within Unit F andUnit E during scoping (Figure 18).

Results and discussionAs described earlier, the data loggers were set to extractreadings from the instruments at hourly intervals. Thesereadings were then downloaded to the computer duringregular visits to the mine every two to four weeks. Sincethere were a total of 170 instruments, the data obtained wasextensive. For instance, for Mine A alone over 200 000individual readings were taken from the instrumented bolts.Detailed analysis all of this data is out of the scope of thispaper. Several papers have already been published containinga detailed discussion of results from each mine (Spearing andGadde, 2011; Spearing, et al., 2011; Reisterer, 2011; Ray,Gadde, and Spearing; 2012; Kostecki, 2013). This paperreports and comments on only the broadest findings.

IInitial bolt loadsAs mentioned previously, the data was obtained using theYYieldPoint d4 data-loggers. Unfortunately, these data-loggers

fwere not intrinsically safe (i.e. fnot rated for use in potentiallyexplosive atmospheres) and therefore were not permitted byMSHA law to operate until fresh air had been established inthe entries. This caused a significant delay (6–10 days) in theinitial bolt readings for mines A and B and a short delay (1–2days) for Mine C (Spearing, et al., 2011).

The earliest bolt readings are shown in Figure 19, whichrepresents an average of all the initial bolt readings fromeach bolt type (FGPR, FGTR, and RMAB) from each mine (A,B, and C).

The initial assumption was that the active bolts wouldshow a few tons more load on installation than the passivebolts, as they are tensioned on installation (4–5 t versus1–2 t), especially since the roofbolter was set to 325 foot-pounds (441 Nm) torque (Kostecki, 2013). However, theresults showed that the initial loads on the bolts were notsignificantly different. Although the results may be a poorrepresentation of the initial loads at mines A and B, becauseof the delay in readings, the readings at Mine C wereobtained soon after installation, and yet there was nosignificant difference in loadings.



Figure 17—RMAB at mid-pillar and intersection, Mine C instrumentationsite (crosscut 82) (Spearing, et al., 2011)

Figure 18—Roof lithology for Mine C instrumentation site (Spearing, et al., 2011)

Figure 19—Initial bolt loads from mines A (top), B (middle), and C(bottom), as presented by Spearing, et al., (2011)


fTable II displays a more intuitive representation of theaverage bolt loads per system and per mine. From this table itcan be shown that the time delay for the first readings atmines A and B had a significant effect on the loadingsoverall, as some mining-induced loadings had occurred. Moreimportantly though, Table II captures little difference inloadings between the bolt systems, as the FGPR averaged2.68 t, FGTR 2.71 t, and the RMAB 2.62 t. Severalmechanisms have been proposed to account for the lack ofdifference in loadings:

➤ The first mechanism, which was proposed by Spearinget al. (2011), is that when upthrust from the bolter isapplied to the bolt during installation a significantreduction in tension on the active bolts can be lost, asopposed to active bolts installed with zero thrust. Thiscan be further justified by observing the bolts at MineC. Although only the averages of all the bolts at Mine Care shown in Figure 19, some individual bolt readingsshowed negative axial tension. This was not observedat mines A and B, probably because of the delay inobtaining the initial readings, and enough time hadpassed for mining-induced loadings to the bolts. Thisbehaviour has also been observed by several otherresearchers (Karabin and Debevec, 1976; MahyeraKempen, Conway, and Jones, 1981; Mazzoni, Karabin,and Cybulski, 1981)

➤ Another likely reason could be resin creep occurringsoon after installation. This would be particularlyevident in the active bolts, as very small displacementscould result in the load loss of the bolts.

Overall, no significant difference was found in the initialloading phase at all three mines. Additionally, more detailedstatistical analysis found that there was no significantdifference in loadings over time either (Kostecki, 2013). Inparticular, Kostecki (2013) observed that, for the instru-mented bolts in this study, if 70% of the bolt yield wasassumed to be applied during bolt tensioning, then only0.156 inches of bolt displacement would be needed to eitherretain or lose tension. Seventy per cent of the bolt yield waschosen because the torque-to-tension ratio was not knownfor the bolts and this was the recommended value given by aleading bolt manufacturer. Considering this, it wasdetermined that when bolts are installed where the immediateroof is prone to weathering, such as the case at Mine A, thissmall amount of displacement could ’release’ the tension inthe active bolts over time – that is, if the load was not alreadylost soon after installation. A similar observation was madeby Unrug, Padgett, and Campoli (2004).

ModellingFast Lagrangian Analysis of Continua FLAC3D (Itasca, 2010)was utilized in an attempt to calibrate a numerical model tothe bolt loadings obtained from all three mines. The resultsfrom all three mines and bolts systems were generally thesame, therefore only results from the passive (FGPR) bolts atMine A will be discussed.

The roof lithology (Figure 8) and the floor lithology forMine A were generated into a global grid, shown inFigure 20. This represented a portion of an entire panel atMine A. The objective was to simulate the mining-inducedloadings generated in the bolts as the panel progressed to twopoints in time – when the first readings were acquired andthe readings at the beginning of the next month. These twopoints and times correspond to the relative face positionsshown in Figure 21. Also shown in Figure 21 are thelocations of the no. 5, 6, and 7 entries where the FGTR,FGPR, and RMAB were installed.

The panel was a large region, therefore to limit the modelrun-time, the zones were kept relatively large throughout (10× 10 ft; 3.05 × 3.05 m), except in the area of interest. In thiscase the no. 6 mid-pillar and intersection were of interestbecause this was the location of the passive instrumentedbolts. Therefore the zone sizes were ’densified’ via a FISHsubroutine, which broke the zones down into smaller 0.3 m ×0.3 m (1 × 1 ft) zones. An excavation sequence, which madea 12.2 m (40 ft) cut of coal and then placed a pattern of bolts,was then modelled. The excavation sequence progressed untilthe face positions were matched to those shown in Figure 21.At this point the loads on the instrumented bolts wereextracted from the model and processed in a similar manneras the actual instrumented bolt data. Figure 22 shows thesame global grid as in Figure 20 with the lithology above thecoal pillars removed to show the excavations generated. In

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Figure 20—Global grid for Mine A

Table II

Initial bolt loads (Reisterer, 2011)

Time between installation and 1st reading FGPR, t FGTR, t RMAB, t Average, t

Mine A 6–9 days 1.52 2.92 3.25 2.56

Mine B 10 days 5.83 4.41 3.74 4.66

Mine C 1–2 days 0.68 0.8 0.88 0.79

Average 2.68 2.71 2.62 2.68 2.71

the case of Figure 22, the face positions match the exact faceposition of the ’Start of July Cuts’ shown in Figure 21. Alsoshown is the densified no. 6 mid-pillar and intersectionwwhere the FGPR bolts were located. The model was assumedto undergo elastic-perfectly plastic behaviour and followedthe Mohr-Coulomb failure criteria.

Bolts 100575073 and 100575074 were arbitrarily chosenfor discussion because they best represent the challenges stillremaining in the modelling portion of this study. The actualresults from the instrumented bolts at two points in time (i.e.First Scan Actual and June 30 Actual) and the results of anelastic-perfectly plastic Mohr Coulomb model at two pointsduring the excavation (i.e. First Scan Plastic and June 30Plastic) are shown in Figure 23. The actual bolt loadings ofthe FGPR bolts tend to show peak loadings nearest to theexcavation hangingwall, shown by point A in Figure 23. Thiswwas to be expected, as over time FGPR bolts are loaded by thedownward movement of rock, which should transfer downthe length of the rebar to the head of bolt. For the modelledbolts, the peak loadings tend to be somewhat near the headof the bolt but not nearly as distinctly as the actual loadings.Additionally, the modelled loads tend to be generally lowerand less pronounced with no real peaks in loading, as is thecase for bolt 100575074 (point B of Figure 23). In the actualbolts, the peaks are generated because of some discontinuitydriving the loading at that point (e.g. dilation or shearing ofthe strata). For example, from point A in Figure 23, it isunclear what phenomena are driving this peak load, butthese peak loadings were not re-created within the model.

It was therefore concluded that although the modellingshows potential to recreate the in situ loadings, challengesstill remain. A particular challenge is replication of thebearing plate at the head of the bolt, which could be thereason behind the lack of peak modelled loads nearest to thehangingwall. Challenges also remain in modelling thegeological structure on a local scale. For instance, dilationand shearing of the immediate roof over time could accountfor the absence of peak loadings shown by point B inFigure 23.

ConclusionsAfter comparison of the in situ data of the three most popularprimary roofbolt systems, there was no evidence to indicate adifference in performance of the active primary roofboltsversus the passive primary roofbolts on any of the threemines. Additionally, in the initial loading phase, the activebolts showed no difference in loading, indicating that tensionbleed-off is more of a concern than originally thought. Forthe initial computer modelling studies, of replicating in situprimary roofbolt loading mechanisms, challenges still remainin obtaining a good match and replicating the discontinuous



Figure 21—Map of the relative face positions for Mine A

Figure 22—The global grid showing the excavation sequence and thedensified region of the FGPR bolts

Figure 23—Actual and modelled elastic-perfectly plastic Mohr Coulomb(First Scan Plastic and June 30 Plastic) loadings for the 100575073FGPR bolt (top) and the 100575074 FGPR bolt (bottom)


froof rock and in situ bolt behaviour over time. In particular,simulation of the bolt bearing plate and the recreation of thegeological structure seem to be the greatest challenges at thistime. This reinforces the idea that in situ measurements arestill needed for design and to improve current supportpractices. The in situ monitoring technology from YieldPointwworked well over the entire monitoring period. The increasedcoverage of the long baselength displacement sensortechnology improves measurement of the overall load profileof the rockbolts. Due to the length of the long baselengthsensors, and the end-to-end arrangement of sensors, a moreaveraged loading profile is obtained at the expense oflocalized loads captured by short baselength strain gaugesused by previous in situ bolt studies.

AAcknowledgementsThe funding and support provided by NIOSH (under BAAnumber 2008-N-10989) is greatly acknowledged, as well asthe considerable support given by Peabody Energy. Specialthanks to Jennmar Corporation, Minova USA, and Dr A.J.Hyett from YieldPoint Inc. for providing the instrumentationfor the project and for their assistance during the instrumen-tation installation.

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▲


IntroductionOne of the hazards of deep-levelWWitwatersrand gold mines is rockburstingassociated with mine seismic events. Thishazard is addressed principally through thedesigns for stoping layouts and support (e.g.ggJJager and Ryder, 1999). Three broad factorsare considered for layout design, namelyregional support in the form of rock pillars,local stability through correct sequencing andface shapes, and the presence of geologicaldiscontinuities and intrusions. Backfill iswwidely used to provide a dual function ofregional and local support. Spottiswoode et al.(2008) studied dip pillar mining at two minesand found that seismicity was proportional toelastic strain energy release and the dip pillarswwere performing stably as required.

While rockbursts are much less common inthe mines of the Bushveld Complex, they areconsidered to be an increasing hazard(Ledwaba et al., 2012). Essrich et al. (2011)recommended that similar hazard controlmeasures to those applied for deep gold minesbe applied for mining of the platinum reefs, inparticular the Merensky Reef. These measuresinclude avoidance of highly-stressedremnants, ‘improved sequencing’, preventionof long abutments, adherence to stopping linesbetween raise lines, limiting and controllingleads and lags, and ‘cutting pillars to thecorrect width and length dimensions’. These‘suggested changes’ are arguably too vague tobe readily implemented without more studiesof the relationship between mining geometryand sequencing and associated seismicity. Wehope to show in this initial study that themodelling and back-analysis methodologiesthat we present for the first time can be appliedto answer some of the questions implicit in thebroad recommendations of Essrich et al.(2011).

In this paper we simulate possible pillarfailure and further deformation using a limitequilibrium model for pillar strength. Limitequilibrium modelling was suggested byBrummer (1987) using a high ‘effective’ stopewidth (mining height) as a way to explain thevertical extent of fracturing around deep-levelWitwatersrand stopes. Napier and Malan(2007) applied limit equilibrium modelling tosimulate deformations of in-stope pillars usingtwo measures of reef strength, namely ‘intact’and ‘residual’ (post-failure). The same authors(Malan and Napier, 2011) referred touncertainties in pillar strength and loading

Pillar behaviour and seismicity inplatinum minesby S.M. Spottiswoode and M. Drummond*

SynopsisCrush pillars are widely used in mine workings on the Merensky Reef in theBushveld Complex to prevent panel collapses. Crush pillars are expected tofail as or soon after they emerge from the face and failure should occurnon-violently. Unfortunately, violent failure occurs frequently and is saidto be the main cause of seismicity associated with mining of the MerenskyReef.

Recent work by Napier, Malan, and du Plessis has shown that limit-equilibrium quasi-static models are able to simulate pillar failure usingthree states of strength of rock in pillars, namely intact, residual afterfailure, and decayed strength after later time-dependent (viscous)weakening. We have previously introduced an additional state of strengthto account for the dynamic failure that results in seismic events, and foundthat this approach could be used to generate synthetic seismic cataloguessimilar to observed seismicity for deep-level gold mines, where seismicitytakes place mostly on advancing faces. The less brittle seam material of theMerensky Reef, compared to the brittle quartzites and lavas of theWitwatersrand reefs, results in little or no face bursting and is modelledwith an assumed plastic strain of some 0.005 over an effective stope widthof 2 m before failure. When this plastic yield is surpassed, we allow the reefto fail ‘seismically’.

We show that synthetic seismic catalogues modelled in this way havesome of the features of observed seismicity. Analysis is greatly facilitatedusing our custom-built software that reads the mine’s survey data into adatabase and presents results in an interactive graphical form.

Keywordsseismicity, dynamic failure, crush pillars, pillar behaviour, pillar failure,simulation, numerical modelling.

* Drummond Technical Services.© The Southern African Institute of Mining and



Pillar behaviour and seismicity in platinum mines

ff f fstiffness as reasons for moving beyond the sole use ofempirical formulae for pillar design towards combiningmonitoring and numerical modelling to obtain the bestinsights into design problems.

They extended the work further (Napier and Malan,2012) by considering a third measure of strength to accountfor time-dependency in the fractured rock mass, andintroducing a ‘decayed strength’ that is reached exponentiallyand is described in terms of a half-time (for example 20days) in a manner similar to radioactive decay. Du Plessis etal. (2011) presented modelled stope closure data andconcluded that Napier and Malan’s (2012) time-dependentmodel was not adequate to explain observed stope closures.They plan further fieldwork to guide changes to their model.

The work by Napier, Malan, and du Plessis mentionedabove followed a considerable amount of fieldwork anddetailed numerical modelling by Watson and others (e.gWWatson et al., 2008; 2010) to provide some measures ofintact and post-failure strength.

The modelling work presented here uses the limitequilibrium approach of Napier, Malan, and du Plessis with afourth value of strength, namely ‘dynamic strength’, thestress held by an element of reef when it initially loses itsintact strength or when its short-term post-failure strength isexceeded. These four measures of strength were introducedby Spottiswoode (2001) for studies of seismicity associatedwwith the Witwatersrand deep gold mines. We assess theextended model by comparing catalogues of syntheticseismicity with observed seismicity.

Source mechanisms of Bushveld Complex mineseismicityImpala Platinum mines the Merensky and UG2 reefs in thewwestern limb of the Bushveld Complex. Seismicity has beenincreasing (Ledwaba et al., 2012) and is considered to be anincreasing hazard. Most of the seismicity, as indicated byrockburst damage, is associated with the in-stope pillars,wwhich are nominally 6 m by 3 m in size but may differsubstantially for various reasons.

Whereas seismic source mechanisms on deep-level goldmines are generally compatible with shear failure (e.gHoffmann et al., 2013), source mechanisms of events atImpala (Spottiswoode et al., 2006) and other mines in thearea are compatible with pillar failure and accompanyingstope closure (Malovichko et al., 2012).

AA limit equilibrium model for pillar failureFollowing the lead of Malan and Napier (2011), we modelpillar strength in terms of a limit equilibrium model. Figure 1is a discretized approximation of reef strength in terms ofnormal and normal stresses ahead of a face or into anabutment or pillar.

[1]

[2]

[3]

wwherem is the strengthening factor with confining stress s

U fis the UCS, as experienced at the facec is the cohesive strength against shearμ is the coefficient of frictionT = (EwEE

Sw) is a measure of the slenderness of each small (T<<1)limit equilibrium element, with Sw being the stope width (orheight in layman’s terms) and EwEE the element width.

Eliminating τi and σi from Equations [1], [2], and [3] wehave an equation for the increase of horizontal stress aheadof the face:

[4]

where constants e and f are given by

[5]

and

[6]

and then the vertical limit equilibrium stress is given byEquation [1].

Note that Equation [4] does not guarantee that theconfining stress increases with increasing distance from theface, as a value of element width EwEE ≥ μmμ Sw causes thehorizontal confining stress, and hence the limit equilibriumstrength, to become infinite (when T = μm) or negative. Thisμis clearly incorrect, as Napier and Malan (2011) and othershave shown that the limit equilibrium strength increasesexponentially with distance from the face. This mathematicalproblem disappears in the limit as the element width tends toinfinity (T → ∞ or EwEE → 0). We therefore take care to choosevalues of element size EwEE << μmμ

Sw within practical limits.

Identifying and measuring synthetic seismic events

Constructing a synthetic seismic eventAs seismicity on the Bushveld Complex platinum minesappears to be predominantly associated with in-stope pillars,we cumulated convergence associated with seismic stressdrop within unmined areas to form synthetic seismic events.The ‘location’ of a synthetic event is given as the centre ofgravity of the convergence on the elements. Most eventslocate on in-stope pillars and, for most pillar shapes, plotwithin the pillar. Plots of seismicity and mining within afinite time window of may incorrectly appear to have occurredin mined ground, as will be seen in Figure 5.

▲


Figure 1—Forces and dimensions for limit equilibrium model

( )

Calculating the size (moment-magnitude) of syntheticeventsSudden loss of reef strength and accompanying convergenceof the roof and floor of the stope and across the failed reefconstitutes a seismic event in this paper. The ‘size’ of theevent is given by the volume of convergence multiplied by anelastic constant. In general, the volume change attributed to apillar cannot be calculated by direct summation as associatedstope convergence might be affected by deformations in otherareas of reef. We take an energy approach to calculate thevvolume of convergence.

Consider three states of a pillar-stope configuration ateach on-reef element, namely before any mining, and thestates immediately before and after a seismic failure, as inFigure 2.

The work done to move from the previous to the currentconfiguration can be written either directly from step 1 to 2 orfrom the difference in work done from virgin (pre-mining)conditions to steps 1 and 2 (see Table I):

[7]

This simplifies to

[8]

Separating the reef (R) and stope (S) components andsetting the zero values from Table I gives

[9]

The change in volume around the failed reef andassociated stoping is then

[10]

and is called ΔV here.We compare observed seismicity to mining in any area

and time period using

[11]

where χ is an elastic modulus taken as (λ+2G)/2.λλ

Computer program development

MMinXMinX is a software environment for creating, running, andvvisualizing MinFT solutions. MinX employs an MS AccessTM

database to store both mine plans and solutions. The mainreason for employing a database was encapsulation (i.e.avoidance of an otherwise messy file system) but otherdatabase features were attractive with respect to efficient dataretrieval (e.g indexing).

MinX essentially employs a pipeline architecture, aspipelines accommodate later modifications and extensionswith a low amount of disruption (Garlan and Shaw, 1993). Itis hoped that as the software reaches maturity, the pipelineaspects may be exposed via the user interface to allow usersto reproduce or create new workflows as well as modifyexisting processes.

MinX process An advantage of MinX is that surveyed digital mininggeometries are employed. Monthly mining polygons areimported in ESRI Shapefile format (an open format) – .dgn(Bentley Microstation format) and other proprietary formatsare easily converted to Shapefile format via the mine surveydepartment’s software. The authors attempt to employ onlyopen, published formats to allow for easy sharing and accessof data – e.g the ESRI Shapefile format, which has become ade-facto standard for spatial data (ESRI, 1997)

Mining outlines are stored within the MS Access databasein Shape binary format, though it is intended to employ theWKB format (Well Known Binary format of the OpenGeospatial Consortium) in future versions (Open GISConsortium, 1999). Queries are executed based on bothmining date ranges and geometric intersections between thearea of interest and bounding boxes of mined polygons.

Optional on-screen digitizing allows users to makecorrections to mining layouts. This has proved to be partic-ularly useful in the elimination of ‘slivers’ (thin unminedareas between adjacent polygons due to poor digitizing) thatmay, during rasterization, be interpreted incorrectly as pillars.The digitizing facility also allows users to reconcile theboundaries of early unsurveyed layouts with later surveyedpolygons. Polygons representing mined areas can be createdfor any particular mining step. Users can also elect tooverwrite mined polygons with their own unmined polygons.User-created polygons reside in a layer of their own and donot affect the imported surveyed polygons – they are onlycombined in the raster created during the scan process.



Figure 2—Sketch of pillar and stope convergence associated with pillarfailure

Table I

Symbols used to describe the stresses anddisplacement discontinuities on unmined and minedground

Mining step Stress Displacement discontinuity

Stope Reef Stope Reef

0. No mining σVσσ √ σVσσ √ 0 0

1. Previous step 0. σ1iσ R √ D1iS ? D1i

R √

2. Current step 0. σ2σσ iR √ D2i

S ? D2iR √

√: values provided directly as part of the simulation?: unknown values


Sheets are created and positioned interactively by theuser on the mine-plan. Dip and dip direction are calculatedautomatically on placement, based on local geometry, but canbe over-ridden if desired. All sheet-related parameters andMinFT run-flags are easily set through dialogs (briefdescriptions, default values, and ranges of allowed values arealso provided for run-flags).

Mine plan polygons are scanned (per mining step) intosheet images (comprising a raster of percentage-minedblocks). The creation of MinFT input files, launching of theMinFT solver, and subsequent loading of the solution backinto the database are automated.

MinX issuesNot long after the first release, it became apparent that MinXand MinFT were too tightly ‘coupled’ – most obviously whenproblems were encountered updating both the database andthe MinX software to accommodate changes and extensionsmade to MinFT. Given the advantages of decoupling(upgrades and extensions could be made to MinFT withoutcorresponding changes to MinX or the database), it wasdecided to expend considerable effort on the problem.Currently an xml file is employed to communicate to MinX allrelevant metadata with respect to both MinFT variables (e.gname description, units) and MinFT run-flags (e.g name,description, valid values/range, default values). This hasproved highly effective in allowing autonomous andasynchronous development of the MinFT solution engine.

Further problems arose as solutions for larger and largersheets caused the 2 GB size limit of MS Access to beexceeded. At first it was thought that with automatedmaintenance of the database (i.e. scripted compress andrepair operations) this could be avoided, but it is now evidentthat to accommodate very large sheets it will be necessary toprovide a database per sheet anyway. This move will actuallyprovide more decoupling as there will now be one databaseproviding mining geometry and several sheet databases.Maintenance of the sheet databases will in future becomeirrelevant as a new database will be created programmatically

every time a solution is run and loaded. Whether it will bebeneficial to provide the databases to MinX through links inan essentially empty master database will have to bedetermined through experiment. It remains to be seen, nowthat the initial desired single database model is no longerfeasible, whether multiple linked databases would prove tohave any advantage over multiple files (the situation thedevelopers were trying to avoid). Certainly, the singledatabase model would work well with a shared ‘industrial-strength’ database (e.g MS SQL Server) and the additionalspatial functionality would be welcome, but this would impactseverely on portability.

MinX interfaceIn line with modern interface design, interface panes areconfigurable by the user and can be re-sized and eitherdocked or floated. This allows users to configure the interfaceto best suit the format and resolution of the monitor(s) inuse.

An overview pane ensures that there is always a view ofthe entire mine plan. The actual area being worked in ishighlighted on this overview. It is envisaged that this panecould be made interactive to allow for fast repositioning todifferent areas of the mine plan.

A manager pane shows all sheets available and allowsthe user to select or create a new sheet to work with. Asmentioned previously, when new sheets are created a dialogprompts users and assists to complete the sheet definitionprocess. Selection of a sheet causes the surface projectionwindow to zoom to the extents of the selected sheet. Furtheractivation triggers the opening of an on-sheet projectionwindow for the creation and/or viewing of a sheet solution.

Solutions are viewed in an on-sheet projection window.The on-sheet projection window is layer-based and, as wellas sheet variables, allows for visual comparison of observedvs. modelled seismic events. A glyph provides sheetorientation information (dip and dip direction), while legendsprovide information about the mining step and active-variable ranges.

▲


Figure 3—The MinX interface. The small panes on the left are (from top to bottom) ‘Manager’, ‘Info’, and ‘Overview’ panes. The sheet is shown in SheetProjection view. To the immediate right of the Sheet Projection window is the ‘Sheet-Data’ pane. Pillar histories are visible at far right in the ‘Histories’pane. The ‘Layer-Order’ pane is visible at bottom right

fA Layer-Order pane provides a means of determining therendering order of layers, also providing the ability to switchlayer visibility on or off as desired.

The Sheet-Data pane allows selection of mining step andvvariable. Variables are currently refined into three groups,namely ‘Dependent’, ‘Difference’, and ‘Independent’.‘Dependent’ variables represent the default type – valuesexist for every mining step – they are listed in the variableswwindow in black font. ‘Difference’ variables are calculatedfrom ‘Dependent’ variables and show the difference in valuesbetween the current step and the previous step – listed inblue font. ‘Independent’ variables do not change with miningstep (i.e. they occur only once and are stored with a miningstep value of -1) – they are listed in green font.

Pillar history plots (stress, convergence, and stress vs.convergence) are linked to pillar selection in the on-sheetprojection window. Conversely, selection of an individualhistory in any of the plot windows will highlight therespective pillar in the on-sheet projection window.

In order to retain a sense of how the model is defined, itwwas decided to visualize the sheet elements as discrete gridcells devoid of interpolation (as opposed to e.g. contours orggmethods employing values interpolated from cell centres tocell vertexes). The value of each cell is mapped to a colourvvalue through a lookup table. Image manipulation methods(provided by the application framework) allowed forrelatively fast rendering times. Seismic events are visualizedas circles (centred on event origins and scaled to magnitude)– modelled events appear with white outlines, whereasrecorded events appear with black outlines. Again, there hasbeen no problem with rendering times – although should thisat some stage prove to be a bottleneck, the circle geometrieswwill be reduced to far simpler primitives (e.g. squares,ggdiamonds).

MMINFTMINFT calculates stresses and displacement around tabularmining excavations using the displacement discontinuitymethod (DDM). The most calculation-intensive part of theDDM is calculating stresses from displacement discontinuities(elastic stope convergences). In MINFT this is done with theaid of Fourier transforms (Peirce et al., 1992).

Extensive changes to the code used by Spottiswoode(2001) were made for the platinum version of the MINFTprogram. Changes were necessary for introduction of the limitequilibrium model described above; to integrate MINFT with

the MinX environment; to exploit the MinX graphicalinterface; and to accommodate the particular mininggeometry used in the platinum mines.

As shown above, the limit equilibrium model requiressmall element sizes. The strength values are built up elementby element from open stopes into the solid using the strengthparameters and values in Table II. The values were basedpartly on those used by Malan and Napier (2012) with the‘dynamic’ strength equal to the ultimate strength. We havenot ‘tuned’ the values through back-analysis for this paper.

Mining outlines are made available on a monthly basis. Aface advance of 20 m or a typical month’s mining could resultin actual stress increase and decrease of over 100 MPa. Itwas necessary therefore to interpolate the month’s advance(called major steps) in small increments (minor steps) totrack stress changes on pillars. A typical day’s face advanceis about 1 m and we used an element size of 0.8 m in thisstudy. Solutions consisted of 117 major steps (close to 10years) extending up to September 2013 and typically over1000 minor steps, with each step allocated to a date to allowcomparison between observed and modelled seismicity.

Platinum mine seismicity and mining geometry arestrongly influenced by pillars, and in-stope pillars inparticular. Pillars are left either for regional support or forlocal hangingwall support (grid pillars). Surveying aroundsmall pillars is difficult, especially if they have started tofracture and a substantial amount of failed rock has been(correctly) left in place. The hand-digitizing mentioned abovemay still leave small mistakes, and some of these can be (andare) rectified automatically, such as isolated mined orunmined elements.

In MINFT, pillars are defined as areas of reef that consistof adjacent (contiguous) elements that are unmined duringthe last step. In most cases, there are many more in-stope (orgrid) pillars than regional support pillars.

Integration with the MinX environment has requiredsome customization of reading input and writing output files.However, the MinX graphical interface gives many opportu-nities for the display of values for an assortment ofparameters that describe the mining geometry and programsolutions. Parameters are either fixed over the duration of thesolution or change from month to month. Some time-varyingparameters can be displayed in terms of the difference frommonth to month. The parameters were chosen during codedevelopment to assist in visualizing and debugging. We hopethat users of the MinX suite will use these displays to gain



Table II

Strength and time decay parameters

State U, MPa M C, MPa μ e f

Intact 50 2.0 5.0 0.6 53.9 2.85Residual 5 2.0 2.0 0.6 7.69 2.85Ultimate 1 2.0 0.5 0.6 1.69 2.85Dynamic 1 2.0 0.5 0.6 1.69 2.85Stope width 2.0 mElement size 0.8 mYoung’s modulus 70 GPaPoisson's ratio 0.28Reef deformation at intact strength before failure 10 mmResidual strength half-life 20 days


funderstanding of the how the model and data analysiswworks. Some of the available parameters are listed inTable III.

The PANG parameter shows groups of panels that weremined ‘close’ to one another in space and time. We chose‘close’ to be 60 m, as it seemed to be approximately theseismic location error as judged by the scatter of locationsaround active faces and their adjacent pillars.

As the entire stress-deformation history of eachindividual pillar is of interest, average stress and averagedisplacements for every minor mining step and every pillarare written to a file for plotting by MinX.

The stress field in the platinum mines appears to beapproximately hydrostatic, with k-ratios (horizontal / verticalstress) of around 1.0 (Handley, 2013). For the current studywwe assumed k = 1.0 and hydrostatic stress. This has theadvantage of eliminating the need to solve for on-reef shearstresses and ride components, thereby minimizing solutiontimes.

Platinum mine seismicity is considered to occur on pillars(e.g. Essrichgg et al., 2011). Identification of modelled seismicevents is therefore generally straightforward once pillars havebeen identified, as is the case with MINFT. Seismic moment iscalculated for sudden failure of pillars using Equation [11].

AAnalysisSuperficial analysis of a 410 m by 410 m area of Impala mineis shown here mostly in terms of screen dumps and X-Yplots. Figure 4 is a screen dump of stope convergence withpillar deformations for the study area. One in-stope pillar hasbeen selected and its stress-convergence-time plots arehighlighted in red.

Figure 5 illustrates observed and modelled seismicity.Most modelled seismicity takes place on the pillars – someseismicity takes place ahead of the advancing faces. Therewwould be considerably more face seismicity if we did notallow some (10 mm in this case) reef deformation at theintact strength before allowing the reef to fail. The in-stopeseismicity results from failure of the face, associated in part

▲


Table III

Some geometry or solution parameters displayed byMINX. ‘Type’ is ‘fixed for not time-variable, ‘vary’ fortime variable, and ‘v+d’ to display time-variableparameters and their difference from step to step.The meaning of PANG is explained in the text

Short code Type Description

VZZ fixed Virgin stress normal to reef, MPa

PATT fixed Mining step

NAPS fixed Pillar number

PANG special Global panel number

DZZ v+d Roof-to-floor convergence, stopingand reef deformation, mm

DZZF v+d Portion of DZZ on pillars

SZZ v+d Stress normal to reef, MPa

APS v Average pillar stress, MPa

DZZS v+d Portion of DZZ that takes place ‘seismically’v+d

Figure 4—Screen dump of face outlines and DZZ (convergence) of the study area in September 2013. Dark blue is unmined and unfailed reef. The markedpillar with its history of stress-convergence-time is shown

Figure 5—Observed (black circles) and modelled (white circles)seismicity between 15 May and 15 June 2011. Light blue through to redshading shows intensity of ‘seismic’ deformation

with the lagging configuration of panels. As expected, thewith the lagging configuration of panels. As expected, theobserved seismicity shows considerably more scatter than themodelled seismicity.

The total amount of modelled seismicity mimics theobserved seismicity quite well (Figure 6) considering all theassumptions and uncertainties and the fact that we havemade very few attempts at ‘tuning’ strength values to matchthe modelled results to the observed seismicity. The lowernumber of small observed events compared to the number ofmodelled events is a result, in part, of network sensitivity.

We now compare modelled to observed seismicityassociated with two identified areas of mining (Figure 7),based on the global panel numbers (PANG, above). This wasfacilitated by using a purpose-built CSV file written byMINFT. This file lists the total amount of observed andmodelled seismic moment associated with each panelgrouping for each month. Two well-grouped large areas arelabelled 1 and 2 in Figure 7. Global panels were not alwaysas clearly grouped, as can be seen by the wide range ofcolours on the right of Figure 7. Poor grouping occurred forcomplicated mining situations, such as when re-raising wasneeded to re-establish panels that had become difficult tomine.

For each area, we plot the cumulated modelled andobserved seismicity expressed as inferred volume of co-seismic convergence (Equation [10]) as a function of the areaof influence for capturing the events (Figure 8).

As expected, the lagging panels of group 2 were moreactive, both for the modelled and the observed seismicity,than the previous group 1 panels. The modelled seismicitymatched the observed seismicity well for the lagging faces(group 2), but over-estimated the seismicity in group 1.

Data for other global panels has not been presented here,as interpretation would arguably be best served bycomparison with similar situations elsewhere on the minewwithin a wider study than this initial work.

The slope of the graphs in Figure 8 is analogous to γEγ(normalized seismic deformation) presented by Spottiswoodeet al. (2008) for two much larger case studies in deep goldmines. The observed values for two global panels in Figure 8are 0.018 and 0.068 for panels 1 and 2 respectively. This issubstantially less than the values of 0.25 and 0.19 reportedby Spottiswoode et al. (2008) for gold mines.

One of the features of both the observed and modelledseismicity is that there are more events than pillars: eachpillar might fail seismically many times. This is illustrated bythe multiple stages of stress drop in the highlighted stress-convergence plot on the bottom right of Figure 4.

fOne might intuitively expect that the rate of stress drop isa factor that controls the burst potential of a pillar. To studythis, we show the loading stiffness that drove each event oneach pillar with a size of less than 50 m2 as a function ofpillar size in Figure 9. Note the large range of stiffnessvalues. For comparison of the estimates from the individualevent loading stiffness values, we created two specialsimulations:

1. The stiffest loading is expected to be for the case offailure of each pillar when surrounded by unminedground stabilized only by large pillars, as shown bythe ‘Mined first’ symbols in Figure 9

2. The softest loading might intuitively be expected tooccur for the pillars to be intact until they are allowedto fail completely only after all mining has place. Thisis shown by ‘Mined last’ symbols based on a two-stage simulation: namely, all final mining followed bycomplete failure (evaporation) of the smaller (< 50



Figure 6—Comparison between observed and modelled seismicity. Left: cumulated observed and modelled seismic moment as a function of cumulatedarea mined

Figure 7—Order of mining (left) and numbered ‘global’ panel numbers(right), numbered in order in which they were mined. The panels in box2 were started from a later raise and therefore lagged behind shallowerpanels and panels in box 1

Figure 8—Cumulated seismic volume as a function of cumulated areaof influence for the two global panels (1 and 2). Solid lines indicate datafor observed (‘o’) and dashed lines for modelled (‘m’) values


m2) pillars. This was not the case, as much of the softestloading takes place with some elements holding some of thestress drop of failing elements. This could be important forunderstanding the damage potential of pillar events.

DiscussionMost work to date on numerical modelling and seismicstudies in South African mines has focussed on the deep goldmines (e.g. Jager and Ryder, 1999). Much of the research hasggfocused on shear slip on geological discontinuities (e.g.ggHoffmann et al., 2013), as large seismic events arecommonly attributed to previously mapped faults or dykes.On the other hand, seismicity is not always dominated by themapped discontinuities, and has been found to be broadlyproportional to elastic strain energy released in two mines inthe Carletonville gold field (Spottiswoode et al., 2009). Elasticstrain energy release for flat-dipping reefs is a weak functionof the horizontal stress. Handley (2013) has pointed out thatthe apparently greater success with pillars aligned with thedip direction compared to pillars along strike could be due tothe horizontal stress component along strike generally beinggreater than the dip-aligned horizontal stress. Narrow deep-level stopes are surrounded by fractures that extend manymetres from the reef. A limit equilibrium approach forWWitwatersrand gold mines would perhaps need to considerseveral layers extending into the hangingwall and footwall.Horizontal stress would play an important role, as wouldbackfill in limiting the bulking of the face area.

The work presented here is mostly a description ofprocesses for modelling deformations around a typicalplatinum mining geometry and sequence. The aim has beenmore as an introduction to what MINX / MINFT can do thanhow it should be used for mine planning. Application on amine is briefly summarized in Figure 10.

RReactiveThe work in this paper is of a reactive nature based on back-analysis of a single area. The general term ‘pillar stability’can be divided into two components, namely well-behavedyyield of grid (in-stope) pillars and large pillars maintaining acore of unfailed ground that provides regional stability bybeing capable of maintaining a stress an order of magnitudelarger than the virgin stress. It is of utmost importance that anominally stable pillar does not fail unexpectedly. We suggest

that extensive back-analysis work be done to studysituations that might have been considered to have beenstable, but which did generate large events.

ProactiveUnder ideal geological conditions, a regular grid of in-stopepillars and regional pillars might be designed. However, thegeometry of the mining in the study area contains sufficientevidence to indicate that odd-shaped pillars are likely to beused. It is essential that regional pillars, of whatever origin,do not fail entirely, nor that pillars that are meant to fail doso at a later stage. On the other hand, in-stope pillars do needto fail in or close to the face and then provide enough supportresistance.

ConclusionsThis paper reports the first results from a new suite ofprograms to model pillars in platinum mines. The ultimateaim of this modelling is to provide a tool for on-mine rockengineers to interpret current and planned mining geometryby extrapolating comparison of historical modelled andobserved seismicity into the future for better and safermining. Further work is planned in collaboration with minestaff, and it is hoped to expand the work to other miningoperations on the Merensky Reef.

AcknowledgementsWe thank Michael du Plessis, John Napier, Francois Malan,and Jan Kuijpers for useful discussions. The work would nothave been possible without many discussions with, andassistance, from Rock Engineering staff of Impala Platinum.Les Gardner drove the discussion that led to Figure 10. Thework has been supported in part by Impala Platinum, whohave also given permission to publish this paper.

ReferencesBRUMMER, R.K. 1987. Modelling the non-linear behaviour of fractured seams in

deep gold mines. APCOM 87. Proceedings of the Twentieth InternationalSymposium on the Applications of Computers and Mathematics in theMineral Industries, Johannesburg, South Africa, 19-23 October 1987.South African Institute of Mining and Metallurgy, Johannesburg. vol. 1,pp. 21–32.

DU PLESSIS, M., MALAN, D.F., and NAPIER, J.A.L. 2011. Evaluation of a limitequilibrium model to simulate crush pillar behaviour, Journal of theSouthern African Institute of Mining and Metallurgy. vol. 111, no. 12. pp.875–885.

ENVIRONMENTAL SCIENCES RESEARCHRR INSTITUTE INC. 1997. ESRI Shapefile TechnicalDescription. ESRI White Paper. Environmental Sciences Research Institute,Redlands CA.

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Figure 9—Loading stiffness driving synthetic seismic events (‘Event’) asa function of pillar size for smaller pillars. Values for ‘Mined first’ wereobtained by mining the pillars only while leaving the stopes unmined.‘Mined last’ was the stiffness for each pillar by not allowing any failureof these pillars until the very last step

Figure 10—Diagram showing an initial proposal for application foranalysis and planning software presented here

ESSRICH, F., HANEKOM, J.W.L., STANKIEWICZ, T.B.A., and RANGASAMYRR , T. 2011.Minimising the increasing seismic risk in the platinum sector. Safety inDraft Final Project Report. Project number: SIM100301. Safety in MinesResearch Advisory Committee, Johannesburg. 368 pp.

GARLAN, D. and SHAW, M. 1993. An introduction to software architecture.Advances in Software Engineering and Knowledge Engineering, vol. I.ggWorld Scientific Publishing, New Jersey. pp. 6–8

HANDLEY, M.F. 2013. Pre-mining stress model for subsurface excavations insouthern Africa. Journal of the Southern African Institute of Mining andMetallurgy, vol. 113, no.6. pp. 449–471.

HOFFMANN, G., MURPHY, S., SCHEEPERS, L., and VAN ASWEGEN, G. 2013. Surfacestress modelling of some shear slip seismic events that occurred inAnglogold Ashanti’s tabular mines. 8th International Symposium onRockbursts and Seismicity in Mines, St Petersburg and Moscow, 1–7September 2013. Geophysical Survey of Russian Academy of Sciences,pp. 219–231.

JJAGER, A.J. and RYDERRR , J.A. 1999. A Handbook on Rock Engineering Practice forTabular Hardrock Mines. SIMRAC, Johannesburg.

LEDWABA, L.S., SCHEEPERS, J., DURRHEIM, R.J., and SPOTTISWOODE, S.M. 2012.Seismic damage Mechanism at Impala Platinum Mine. SHIRMS 2012,Second Southern Hemisphere International Rock Mechanics Symposium,Sun City, South Africa, 14–17 May 2012. Southern African Institute ofMining and Metallurgy, Johannesburg. pp. 367-386.

MALOVICHKO, D., VAN ASWEGEN, G., and CLARK, R. 2012. Mechanisms of largeseismic events in platinum mines of the Bushveld Complex (South Africa).Journal of the Southern African Institute of Mining and Metallurgy,vol. 112, no. 6. pp. 419–429.

NAPIER, J.A.L. and MALAN, D.F. 2007. The computational analysis of shallowdepth tabular mining problems. Journal of the Southern African Instituteof Mining and Metallurgy, vol. 107, no. 11. pp. 725–742.

NAPIER, J.A.L AND MALAN, D.F. 2011. The design of stable pillars in theBushveld Complex mines: a problem solved? Journal of the SouthernAfrican Institute of Mining and Metallurgy, vol. 111, no. 12. pp. 821–836.

NAPIER, J.A.L and MALAN f, D.F. 2012. Simulation of time-dependent crush pillarbehaviour in tabular platinum mines. Journal of the Southern AfricanInstitute of Mining and Metallurgy, vol. 112, no. 8. pp. 711–719

OPEN GIS CONSORTIUM. 1999. Open GIS Simple Features Specification for SQL.

PEIRCE, A.P., SPOTTISWOODE, S.M., AND NAPIER, J.A.L. 1992. The spectral boundaryelement method: a new window on boundary elements in rock mechanics.International Journal of Rock Mechanics and Mining Sciences andGeomechanical Abstracts, vol 29, no. 4. pp. 379–400.

SPOTTISWOODE, S.M. 2001. Synthetic seismicity mimics observed seismicity indeep tabular mines. Keynote address: 5th International Symposium onRockbursts and Seismicity in Mines. dSouth African Institute of Mining andMetallurgy, Johannesburg. pp. 371-378.

SPOTTISWOODE, S.M, SCHEEPERS, J., and LEDWABA, L. 2006. Pillar seismicity in theBushveld Complex. Proceedings of SANIRE 2006: Facing the Challenges.South African National Institute of Rock Engineering. pp. 140-158.

SPOTTISWOODE, S.M., LINZER, L.M., and MAJIET, S. 2008. Energy and stiffness ofmine models and seismicity. 1st Southern Hemisphere International RockMechanics Symposium, Perth, Western Australia, 16-19 September 2008.Australian Centre for Geomechanics. pp 693–707.

SPOTTISWOODE, S.M., MILEV, A.M., LINZER, L.M., and MAJIET, S. 2009. Evaluationof the design criteria of regularly spaced dip pillars (RSDP) based on theirin-situ performance. Draft Final Project Report SIM 04 03 01. Safety inMines Research Advisory Committee, Johannesburg.http://stevespot.yolasite.com/resources/RSDP.pdf

WATSONWW , B.P., RYDERRR , J.A., KATAKAKK , M.O., KUIJPERSKK , J.S., and LETEANE, F.P. 2008.Merensky pillar strength formulae based on back-analysis of pillar failuresat Impala Platinum. Journal of the Southern African Institute of Miningand Metallurgy, vol. 108, pp. 449–461.

WATSONWW , B.P., KUIJPERSKK , J.S., and STACEY, T.R. 2010. Design of Merensky Reefcrush pillars. Journal of the Southern African Institute of Mining andMetallurgy, vol. 110, no. 10. pp. 581–591. ◆



IntroductionNkomati Mine is a joint venture betweenAfrican Rainbow Minerals ARM (50%) andNorilsk Africa (50%), who jointly manage themine and project. It is situated betweenBadplaas and Nelspruit in the MpumalangaProvince of South Africa, approximately 300 km east of Johannesburg (Figure 1). Thenickel deposit is situated in a steep-sidedvvalley with limited flat ground for mininginfrastructure development, as illustrated inFigure 2.

Due to limited available level ground, awweathered rock slope was cut at the base of amountain spur in order to create a flat platformfor construction of the primary crusher plantand run-of-mine stockpiles (Figure 3). Asspace is limited around the mining area, oreprocessing at Nkomati is based on a highreliability of flow of material through thecrusher plant, with minimal usage of other and

larger designed stockpiles. Evidently, anycrusher plant shutdown will render the mineas a whole unproductive and put excessivestrain on the medium- to long-term large orestockpiles, the deposition rates for which arerestricted by founding material consolidationrequirements.

Historic slope performanceIn early 2010 a small toe failure and cracksdeveloped on the slope above the primarycrusher. A review of the design wasundertaken, which included detailed mappingof the geology and laboratory testing by meansof of Gradings and Atterburg Limit tests(Dlokweni and Terbrugge, 2010). The analysisof the slope indicated that the slope stabilitywas sensitive to groundwater and slope angle,even at the relatively flat design angles of 23°.Three recommendations were made: to flattenthe slope to 19°, backfill the slope withcompacted waste material, or dewater the slopeand manage the risk. Following considerationof the benefits and costs associated with eachoption, it was recommended that the slopeshould remain unchanged and that surveymonitoring, depressurization, andgroundwater monitoring systems be installed.

An automated survey monitoring systemwas installed and piezometer holes weredrilled. Survey monitoring has been ongoing;however, as the piezometer holes were dry ondrilling, the piezometers were not installed andno further dewatering measures were taken.No further records related to groundwatermonitoring were available at the time of thisstudy.

Management of the Nkomati Mine crusherslope failureby R. Armstrong* and K. Moletsane†

SynopsisDue to limited available level ground, Nkomati Nickel Mine cut a weatheredrock slope at the base of a mountain spur in order to create a platform forconstruction of the primary crusher plant and run-of-mine stockpiles. Asspace is limited around the mining area, ore processing at Nkomati is basedon a high reliability of flow of material through the crusher plant, withminimal usage of other and larger designed stockpiles. Evidently, anycrusher plant shutdown will render the mine as a whole unproductive andput excessive strain on the medium- to long-term large ore stockpiles, thedeposition rates for which are restricted by founding material consolidationrequirements.

At the onset of the 2012 rainy season, movement was identified on theslope monitoring system and cracks developed on the slope. After a minorfailure on the crusher slope an assessment of the slope stability wasconducted and a slope management plan recommended, which includeddeployment of real-time monitoring. An evaluation of the conditionsleading to instability was conducted and the likely causes for the failureidentified. A full evaluation of the slope monitoring, rainfall, and miningconditions was undertaken and movement triggers were determined. Thispaper describes the events leading to the development of the failure andthe evaluation of the monitoring data to determine a management plan forthe failure that allowed for minimal shutdowns of the primary crusher.

Keywordsslope stability, slope management, slope monitoring.

* SRK Consulting.† Nkomati Nickel Mine© The Southern African Institute of Mining and



Management of the Nkomati Mine crusher slope failure

Cracks were observed early in the rainy season of 2012high up on the slope together with an increase indisplacement measured on the survey monitoring system(Figure 4). This prompted a review of the slope instabilityand the requirement of a management plan that would ensure

f fthe safety of the personnel working at the primary crusherarea, and also prevent unnecessary downtime for the crusher.Leading up to the review, the mine redeployed the open pitslope stability radar to augment the total station monitoringand dumped a waste rock buttress at the toe of the failure.

GeologyNkomati Mine is located within the Uitkomst Complex, whichis intruded into the lower Transvaal Supergroup. The maficmagma intruded vertically into the Transvaal host rocks andformed into what has been described as an ‘anvil–shaped’body.

The lower levels of the valley expose basement granite,followed by Black Reef Formation (the base of the TransvaalSupergroup) through to the Timeball Hill formation higherup. The Transvaal sediments have been intruded by discon-

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Figure 1—Location of Nkomati Mine

Figure 2—Gladdespruit valley and Nkomati Mine

Figure 3—Location of the primary crusher in the mine complex

Figure 4—Location of cracks on the primary crusher slope

tinuous diabase sills. Geological investigations wereundertaken involving mapping the slope (Dlokweni andTerbrugge, 2010) and interrogation of historic borehole coresand logs. These indicated that the slope consists of anunweathered to slightly weathered dolomite bedrock,including chert horizons along the bedding, capped by acompletely weathered diabase sill. Depth of weathering wasuncertain over the extent of the slope; however, the coresdrilled approximately halfway up the slope indicated that thebase of weathering was at 22–35 m.

Slope monitoringFollowing the recommendations in the 2010 review, Nkomatiinstalled an Optron robotic total station to the south-east ofthe slope together with 15 reflective prisms which weremeasured several times a day (Figure 5). Measurementstaken in the early (pre-sunrise) morning were used forregular surveillance of the slope. Following the detection ofslope movement, the Groundprobe slope stability radar wasredeployed from the open pit and stationed to the south-westof the slope to supply pseudo-real time slope monitoring.

Groundwater and rainfallNkomati is in a summer rainfall area. The first rainfall in the2012 season occurred in September with occasional showers,but the rains started in earnest during November. It is

important to note that when the piezometers holes weredrilled following the 2010 recommendations and found to bedry, it was during the dry winter months, following whichthere is no groundwater information. On inspection of thesite in December 2012 after the initiation of the slope failure,it was found that one of the old piezometers holes (locatedbetween CP23 and CP7 in Figure 5) contained standingwater. Several weeks later (January 2013), the hole was dry.This indicates that the groundwater on the crusher slope ismeteoric. During the rainy season groundwater builds upalong the weathered/bedrock surface. Due to the lowpermeability of the weathered rock at the base of the slopethe water egress is slower than ingress, allowing for a porepressure build-up that will dissipate with time.

Slope failure triggersObservations of the slope indicated that degradation of thetoe had occurred resulting in undercutting of the slope. At thetime of inspection (December 2012), waste rock was in theprocess of being dumped at the slope toe in an attempt tobuttress the failure. Figure 6 shows a comparison of the toeof the slope in 2010 (left) and December 2012 (right). Basedon the height of the TR100 dump truck the slope wasundercut by 5 m (vertically) above the buttress. Theundercutting of the toe resulted in an increase of the overallslope angle from 23° to 25°, the 2° increase being well withinthe sensitivity of the slope as determined in the 2010 report.



Figure 5–Location of the primary crusher slope monitoring

Figure 6—Comparison of the slope toe in 2010 (left) and in December 2012 (right)


fA comparison of the survey monitoring results andmeasured rainfall is presented as Figure 7. It is evident thatthe movement of the slope is related to major rainfall events.

The combination of the in situ groundwater and the over-steepening of the slope is considered to be the driving factorfor the slope instability.

Remedial action and failure management planObservations of rainfall and slope monitoring records indicatethat there is a significant increase in slope movementfollowing large rainfall events. It was recommended that anyrainfall event greater than 20 mm should be considered a‘trigger’ event and monitoring data should be reviewed andthe area evacuated if necessary.

The inverse velocity technique (Rose and Hungr, 2007)wwas also recommended as a tool for analysing monitoring.Since the velocity of movement becomes asymptotic as failureis approached, it stands to reason that the inverse of thevvelocity will trend to zero. Using this method, the inversevvelocity graph can be extrapolated to an estimated time offailure. It is important to note that the method leads to anestimation, and the techniques used to extrapolate theinverse velocity trend can affect the predicted failure time. Adaily average velocity and inverse velocity graph for CP2 ispresented in Figure 7. The accelerations in the velocity graphcan be linked to rainfall events up to two days prior to thespike. Furthermore, these accelerations can be correlated witha decrease in the inverse velocity. It should be noted that atthe time of the site visit the inverse velocity had a slightupward trend. It is assumed this reduction in velocity (andincrease in inverse velocity) is related to the dumping of thebuttress at the slope toe.

Proposal for future remediationFollowing the above assessment, a stability investigation wasundertaken to determine the risk to the primary crusher from

ffurther instability. This programme is currently underway,and has involved the installation of inclinometers into theslope to determine the depth of the failure surface, testingundisturbed soil samples to define the material properties,geophysical tests to determine the depth of the weatheredsurface, back-analysing the slope to determine the overallstrength parameters, and an exercise to design a long-termremedial measure to mitigate the risk to the primary crusher.

ConclusionsFollowing the review of the slope failure at Nkomati, thefollowing conclusions can be drawn:

➤ The failure initiated following the undercutting of theslope and the onset of the rainy season

➤ The failure responded to buttressing the toe by areduction in slope movement

➤ Piezometers are required to determine the actual porepressure that builds up during the rainy season

➤ Rainfall events greater than 20 mm were consideredtrigger events for slope movement

➤ Following the remedial measures taken by the mineand management of the failure, the primary crusher isable to operate under the failure until permanentmitigation measures can be put in place.

AcknowledgementsThe authors would like to thank Nkomati Mine for thepermission to publish the results of this study.

ReferencesDLOKWENI, T. and TERBRUGGE, P.J. 2010. Review of the stability of the Nkomati

Primary Crusher Slope. Report no. 413130. SRK Consulting,Johannesburg, South Africa.

ROSE, N.D. and HUNGR, O. 2007. Forecasting potential rock slope failure in openpit mines using the inverse-velocity method. International Journal of RockMechanics and Mining Sciences, vol. 44, no. 2. pp. 308–320. ◆

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Figure 7—Survey monitoring results compared to daily rainfall

IntroductionSeismographs were first installed to monitormining-related seismicity in South Africa in1910. The first in-mine seismic systems wereinstalled in the 1960s, mainly for researchpurposes. Intensive research, development,and commercialization during the 1990s led tothe widespread implementation of real-timedigital monitoring systems and quantitativemethods of analysis (Durrheim, 2010;Mendecki et al., 1997).

Since then, the understanding of mine-induced seismicity and the quantification ofthe seismic rock mass response has formed thebasis of seismological analysis and seismichazard mitigation in South Africa. Thisprinciple of quantifying and understanding therock mass deformation and failure mechanismhas been introduced in other countries indifferent forms and focused on differentmining environments. Potvin and Wesseloo(2013) point out that the mines in Australia,Canada, and Sweden tend to have morecomplex three-dimensional orebodies and aregenerally smaller and much more containedcompared to the seismically active SouthAfrican mines. For that reason it is easier toinstall a more sensitive three-dimensionalarray to cover the mine volume. With the moresensitive systems in Australia, Canada, and

fSweden, local rock engineers tend to focus onthe overall rock mass response to miningbased on accurate source location and theanalysis of populations of seismic events withmagnitude of completeness as small as ML-2.A grid-based spatial analysis of seismic datawas developed to improve and simplify quanti-tative seismological interpretation within thisenvironment. The methods, however, havebroader application.

Funk et al. (1997) presented work on thevisualization of seismicity that resulted in thesystems for generating contours andisosurfaces of seismic parameters. The workpresented here generalizes and extends theconcepts used in that work.

Analysis with spatial filtersSeismic events tend to cluster at the locationsof active seismic sources where some form ofdynamic failure process occurs. The identifi-cation and understanding of seismic sources isimportant in seismic risk management in thatthey may be, or become in the future, thecause of significant seismic hazard. Inparticular, small events may start to formclusters at an early stage of extraction, with arelatively small stress change. When theseismic system is sensitive enough to capturesmall events, this can assist in the timelyidentification of seismic sources and allow forthe tracking of how these sources respond tomining and, more specifically, how seismichazard related to these sources evolves asextraction progresses.

Grid-based analysis of seismic databy J. Wesseloo*, K. Woodward*, and J. Pereira*

SynopsisQuantitative seismology is an important tool for investigating mine-induced seismicity and the quantification of the seismic rock massresponse. The spatio-temporal interpretation of seismic data within anever-changing three-dimensional mining environment provides somechallenges to the interpretation of the rock mass response. A grid-basedapproach for the interpretation of spatial variation of the rock massresponse provides some benefits compared with approaches based onspatial filters. This paper discusses a grid-based interpretation of seismicdata. The basic methods employed in the evaluation of the parametervalues through space are discussed and examples of applications todifferent mine sites given.

Keywordsquantitative seismology, mine induced seismicity, rock mass response,spatial evaluation.

* Australian Centre for Geomechanics, TheUniversity of Western Australia, Australia.



Grid-based analysis of seismic data

f fInterpretation of the character of seismic sources in spaceand time is generally done by analysis of some spatially sub-filtered data-set. This sub-filtering is often simply based on athree-dimensional volume in space (polygon). In Australia,Canada, and Sweden, the data is often sub-filtered onclusters.

The most widely used clustering method is probably thatimplemented in the software mXrap (formerly known as ms-rap) based on the Comprehensive Seismic Event Clustering(CSEC) technique described by Hudyma (2008). The CSECmethod is a semi-automated two-pass approach, which wasdeveloped on the basis of generic clustering techniques:CLINK and SLINK (Jain et al., 1999, Romesburg, 2004). Thefirst pass of clustering using CLINK is totally automated, andclusters together events based exclusively on spatial criteria.The CLINK clusters are then submitted to a second pass ofprocessing, where clusters are selectively grouped into’cluster groups’ representing individual seismic sources. Thiscluster grouping is a manual process that requires interpre-tation of the likely seismic sources at the mine and a soundknowledge of the geology and the induced stress conditions.Cluster grouping is generally based on the similarity of sourceparameters, the spatial proximity of clusters, and on thecorrelation of the location with known geological or geometricfeatures.

The cluster grouping process can be seen as building aseismic source model by using the generated clusters as basicbuilding blocks. In this sense it is similar to analysing anarea with the use of polygons, as the polygons become thebasic units within the seismic source model. The use of aspatial filter (cluster groups or polygons) to provide a basicspatial unit for quantitative seismic analysis is a common andpractical approach. This, however, introduces a bias ofinterpretation towards the pre-defined polygon as thepolygon is originally chosen by the analyst based on pre-conceived ideas. This process is, per se, subjective, and thevvalue depends to a large degree on the understanding andtraining of the person performing this analysis. Subjectivity,however, is part of geotechnical engineering and attemptingto eliminate subjectivity from geotechnical analysis is a futileexercise. With the grid-based approach, however, we can aimto reduce interpretation bias by providing a spatial interpre-tation of the data that is independent of any chosen spatialfilter.

Having said this, one has to recognize that the grid-basedinterpretation is not free of user influence as it also isinfluenced by the chosen analysis parameters. It is ourconviction, though, that the nature of the analysisparameters, and the ease of testing the influence on theanalysis parameters on the results, leads to a systematicreduction in personal bias.

Grid-based analysis of seismic dataIn the grid-based approach, the seismic source parameters areassessed through space by interpolating the sourceparameters. This approach allows for anomalies to beidentified without prior selection of groups or polygons. Thisis illustrated in Figure 1 and Figure 2.

Figure 1 shows the spatial distribution of b-values at anAustralian mine. High values occur around the stopingvvolumes while low b-values occur at the lower abutment.

ff ffThese differences can be related to the difference in thesource mechanism in these areas; the higher b-valuescorrespond with stress fracturing seismicity, and the lowerb-values relate to a shear mechanism.

The same plot is combined with a spatial distribution ofapparent stress in Figure 2. The colouring of each grid pointin space is the same as that in Figure 1, but in Figure 2 eachgrid-point marker is scaled by the geometric mean of theapparent stress. The apparent stress is proportional to themean shear stress at the source of the event (McGarr, 1994).The areas of the mine showing higher b-values generallyshow a low apparent stress, while the lower abutment areashows a high apparent stress state corresponding with low b-values. In this example, the distribution of b-valuesthrough space can be obtained without a pre-defined modelof rock mass response.

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Figure 2—Grid-based interpretation of seismicity showing the distri-bution of b-value by colour. The size of the grid point markers arescaled by apparent stress. Blue and red highlighted areas enclose thestoping and abutment areas respectively

Figure 1—Grid based interpretation of the spatial distribution of b-values in a mining area at an Australian mine. Blue and red highlightedareas enclose the stoping and abutment areas respectively

General principleThe general principle behind the grid-based approach forquantitative mine seismology can be summarized as follows:

Assign a representativep seismic parameter value to gridpoints in space based on the events in its neighbourhoodgin order to extract information from the variation of thesedifferent parameter values in space.Added value is obtained from this approach when the

different parameters are retained on each grid point, whichallows for the analysis of these different parameters togetheras shown later.

What can be regarded as ‘representative’ and ‘theneighbourhood’ depends on the purpose of the analysis, thedensity and quality of the seismic data, and the type ofparameter interpolation that is performed i.e. obtaining thecumulative parameter assessment, interpolation of the meanvvalue, or obtaining the b-value.

The neighbourhood is defined by assigning a maximuminfluence distance, with the addition of two more qualitychecks. For a grid point to be assigned a representativeseismic parameter, the grid point events around it must beboth close enough and dense enough. This is illustrated inFigure 3. The condition that events must be dense enough inthe vicinity of the grid-point prevents the transfer ofparameter values from areas further away, with dense data,to a grid point where the density of the events close to thegrid point does not warrant the calculation of a parametervvalue.

Grid-based analysis of seismic dataThe grid-based interpretation of seismic data requiresdifferent approaches for different types of parameters,wwhether it is the mean or cumulative value of a parameterthat is of interest, or the b-value.

It is important to note that in all the different approaches,the gridding process involves some level of smearing, thedegree of which is dependent on the analysis parameters thatare discussed in this paper. It is important that the resolutionof the interpretation should match the resolution of theoriginal input data. Sparse data-sets would require moresmearing than high-resolution dense data-sets. A simplesensitivity analysis should be performed to test thesensitivity of the outcomes to chosen input parameters.

Obtaining a grid-based interpretation of b-valueThe b-value of the frequency magnitude distribution isproportional to the mean of the magnitude and, as such, issimply a statistical parameter. The appropriate b-valuecannot be obtained without knowledge of the magnitude ofcompleteness of each subset of data. For this reason, themethod for obtaining the spatial distribution of the b-value isquite involved and is treated separately in the sister paperpublished in this volume (Wesseloo, 2014).

For current purposes it will suffice to summarize theprocess as follows. For each grid point:

➤ Obtain the closest N events➤ For these events obtain the mmin and associated b-

value➤ Retain b-values for grid points passing the quality

tests.

Mean value of all parametersIn order to obtain the mean value of parameters, thegeometric mean of the parameters of the closest events toeach grid point is calculated. The process can be summarizedas follows. For each grid point:

➤ Find all the events (if ≥ N) within a distanceNN Rmin orthe closest N events within a seach distance of Rmax

➤ Calculate the geometric/arithmetic mean for theparameter

➤ Perform quality checks (see above) and retaincalculated values for only the grid points passing thequality tests.

This approach is used for obtaining spatial distribution ofparameters like energy index (EI), apparent stress (AS), andtime of day (TOD).

Smearing cumulative parametersIn the cases where one is interested in the cumulative effectof different events, for example to obtain an event density orthe cumulative apparent volume, a smearing process is used.In contrast to the method used to obtain the mean parameterof neighbouring events (previous section), the parametervalue (intensity, I) of each event is distributed to (or‘smeared onto’) grid points within its zone of influence.

This procedure can be summarized as follows:

➤ For each event– Find all grid points within its influence zone– Distribute a portion of its value to every grid point

➤ For every grid point– Sum all the portions received from each event– Perform quality checks.

This is performed with a variable smoothing where thekernel bandwidth is linked to the event source size.

The distribution of the events’ parameter is performedwith an inverse distance weighting. The cumulativeparameter at each grid point is obtained as the sum of all thevalues of all the events registered to that grid point. This canbe expressed as follows:

[1]



Figure 3—Illustration of the concept of data neighbourhood


[2]

[3]

[4]

wwhere pjp is the cumulative parameter at grid point j, and s arethe location vector of the event and grid points. The intensityvvalue, I, is the parameter value of interest and K(θ) is thekernel function. The bandwidth function h defines theinfluence zone of each event, i.

F is a correction factor that ensures that no errors areintroduced due to the discretization of the volume space, i.ethat the following condition is met:

[5]

In other words, summing the parameter values over allevents must equal the sum of the parameter value associatedwwith each of the grid points.

This process is graphically illustrated in Figure 4. Thecircles in Figure 4a represent different events with differentsource sizes. The parameter values of each of these curvesare distributed to the grid points in the influence zone withthe kernel function (Figure 4b). These values for each gridpoint sum to the final spatial distribution of the parametervvalue (Figure 4c).

In the smearing process, described in the followingsection, each event has an influence zone. Our currentapproach is to define an influence based on the event sourceradius, as defined by Brune (1970). A lower cut-off valueequal to the grid spacing is imposed to ensure stability of themethod for coarser grid discretization. A limiting ceiling valueis also introduced for numerical efficiency and stability. Theresults are not sensitive to the ceiling value.

fIt is important to note that the results of the smearingprocess are not sensitive to the influence of large events, asthe parameter values of these large events are distributed tomore grid points within the larger influence zone.

Grid-based quantitative analysisThis section provides a short discussion on some of theparameters used in the grid-based analysis and examples toillustrate the application of the method. The use of thesemethods is not limited to these parameters.

Energy stress and apparent stressApparent stress is generally calculated as:

[6]

where G is the shear stiffness of the rock mass, and E andMoMM are the total radiated energy and average moment for anevent, respectively. As indicated by its name, the definition ofapparent stress relates to the stress state in the rock mass atthe occurrence of the event. The apparent stress is propor-tional to the mean shear stress at the source of the event,(McGarr, 1994), and is defined as follows:

[7]

where η is the seismic efficiency and τ and τrτ are the peakand residual shear stress, respectively.

As η is unknown, the absolute shear stress is alsounknown. The value of apparent stress is, however, a goodindicator of the relative stress state. This concept is refined byMendecki (1993), who showed that for a given slope of thelog(E)-log(M) relation, the intercept value relates to theMMstress level. A simpler way to express this relative value ofthe intercept is the log(EI), as defined by van Aswegen andIIButler (1993).

Obtaining the log(E)-log(M) trendline introduces someMMdifficulties with unsatisfactory ‘best-fit’ lines. This problemcan easily be overcome. For the sake of maintaining the focusof this paper, this will be discussed further in Appendix A.

The results of a grid-based analysis of the log(EI) at anIIAustralian mine are shown in Figure 5. The colour scale ofthe grid points reflects the values of log(EI). TheIItransparency of the grid points is also scaled with log(EI).IIThe upper 50% of the log(EI) values are more solid while theIIlower 50% are more transparent.

In this particular case, low log(EI) values occur at theIIcentre of the volume with surrounding higher log(EI) values.IIThis corresponds with lower stress areas in the immediatevicinity of the mined stopes, while the more competent rockfurther away from the stopes is under a higher stress state.The particular shape of the grid cloud is determined by thelocation of seismic data, as grid points are generated onlywhere seismic data exists.

Time of dayThe time-of-day parameter (TOD) is a measure of thetemporal differences in the seismic response. It is defined asthe ratio of the rate of seismicity occurring within a specifiedtime window(s) to the rate of seismicity occurring outside of

▲


Figure 4—Illustration of the smearing of parameters onto grid points

those time windows. As an example, where the rock massreacts strongly (in a purely temporal sense) to blasts, theTOD value will be high for a time window around blast time.Orepass noise, on the other hand, results in TOD values ofless than 1 for time windows around shift change, as noorepass activity occurs during shift change.

fGrid points with a low TOD were isolated for anAustralian mine. These grid points all located in a couple ofdistinct areas. The diurnal chart for the events reporting tothese grid points is shown in Figure 7. From this chart it isclear that the events are small and inversely correlated to theshift change, indicating that these events are human-inducednoise, in this case orepass noise. The fact that these eventsare human-induced noise is highlighted by the fact that thetime of lunch breaks during the two shifts is visible in thediurnal chart.

Figure 8 shows the b-value and TOD plots for threeadjacent areas in a mine. Area (1) has an unnaturally highb-value with a very low TOD and is the result of crushernoise. Area (2) has a very high b-value and a higher TODand is the result of a raise bore experiencing some dogearing.Area (3) has a lower, but still fairly high, b-value, with avery high TOD. The seismicity in this area generally relates tostress fracturing around development blasting temporallyconcentrated around blast time. The diurnal charts for thesethree areas area shown in Figure 9.

Cumulative damageIt is generally accepted that there is a correlation betweenhistorical seismic activity in an area and the damageaccumulated in the rock mass. Despite the work of Falmagne(2001), Cai et al. (2001), Coulson and Bawden (2008), andPfitzner et al. (2010), there appears to be no accepted way toquantify this damage accumulation from seismic data. Untilthese difficulties are solved, we propose the use of apparentvolume and the cube root of moment as proxies for damage.

Apparent volume has been linked to the amount of co-seismic strain (Mendecki, 1997), while the cube root ofmoment is proportional to the maximum displacement at theseismic source (McGarr and Fletcher, 2003).

Figure 10 shows the results of a grid-based analysis at anAustralian mine. In this example, log(EI) as a proxy forIIstress and the cube root of moment as a proxy for damageare combined. Log(EI) is represented by the colour scale andIIthe transparency varies with damage. The mean log(EI) isIIcalculated for a 6-month data period, while the damage isaccumulated over the whole history of the mine.



Figure 5—Grid-based interpretation of log(EI(( ). Both colour andIItransparency reflect the log(EI(( )II

Figure 6—TOD definition

Figure 7—TOD definition


The destressed area in this case corresponds with thearea of high historical damage, with some high-stress areason the abutments, where similar levels of damage haveaccumulated. It should be noted that the destressing did nottake place due to the damage alone but is a result of themining voids. As one would expect, the area of high damageis concentrated close to the stopes.

Event densityEvent density is conceptually a very simple parameter and isvery easy to interpret as the number of events occurring perunit volume. From a mathematical viewpoint, event density isa cumulative parameter with the intensity value, I, equal to 1(refer to Equation [5]) and for this reason is calculated withthe same method as is used for the cumulative damage.

Figure 11 shows examples from the Tasmania Mine fordifferent time periods during the life of the mine.

Calibrating numerical modelsIn mining geomechanics, the need for calibrating orconstraining the models with physical observations is wellrecognized. Seismic data provides a valuable source ofinformation on the rock mass response to mining, and forthis reason has been used by some investigators to providecalibration data for their models.

Often the calibration of numerical models with seismicdata is limited to a visual correlation between the eventlocation and strain or stress contours from the models, or thevisual correlation of event density with areas of higher plasticstrain. More recently, correlations between the energy releasemonitored within a specified cell and the modelled plasticstrain energy have been used (Levkovitch et al., 2013; Arndtet al., 2013). Both of these groups limit themselves to energyand perform basic grid calculations, simply summing theenergy of all monitored events located within a specific gridcell.

▲


Figure 9—Diurnal charts for the events shown in the three areas inFigure 8

Figure 10—Analysis results, combining log(EI(( ) results in colour withIIcumulative ‘damage’ results plotted with varying transparency

Figure 8—b-value and TOD plots for three adjacent areas in anAustralian mine

We are of the conviction that the grid-based approach tothe evaluation of seismic data presented here provides theopportunity to better utilize the seismic data to constrainnumerical models.

As the grid-based interpretation can be continuallyupdated, this provides further opportunity to continually testpredicted rock mass behaviour against experiencedbehaviour, with the possibility of flagging deviation from thepredicted behaviour.

Concluding remarksA grid-based interpretation of seismic data has beendiscussed and some examples of results obtained with themethod presented. A grid-based interpretation allows thespatial variation of seismic source parameters to be evaluatedwwithout predetermined analysis volumes. As such, it providessome buffer against biasing of interpretations towards pre-conceived ideas.

The gridding process involves some level of smearing, thedegree of which is dependent on the analysis parametersdiscussed. It is important that the resolution of the interpre-

ftation should match the resolution of the original input data.A sparse data-set would require more smearing than high-resolution dense data-sets. A simple sensitivity analysisshould be performed to test the sensitivity of the outcomes tochosen input parameters.

The grid-based analysis approach is well suited tocompare with results from numerical modelling approaches.

AcknowledgementsOur sincere appreciation to William Joughin and ProfessorRay Durrheim for reviewing the paper. The following organi-zations provided funding for this research through the MineSeismicity and Rockburst Risk Management project: BarrickGold of Australia, BHP Billiton Nickel West, BHP BillitonOlympic Dam, Independence Gold (Lightning Nickel), LKAB,Perilya Limited (Broken Hill Mine), Vale Inc., Agnico-EagleCanada, Gold Fields, Hecla USA, Kirkland Lake Gold, MMGGolden Grove, Newcrest Mining, Xstrata Copper (Kidd Mine),Xstrata Nickel Rim, and The Minerals Research Institute ofWestern Australia.

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Figure 11—Event density results for different time periods at theTasmania Mine. The mine layout is representative of the mine duringthe stage represented in (d)


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AAppendix A

Obtaining the log(E)-log(M) trendlineIt should be recognized that a least-squares ‘best-fit’approach assumes the total radiated energy (E) to be avvariable dependent on the total moment (M). ThisMMassumption is incorrect as M and E are two independentparameters and as such the use of the least-squares best-fitmethod is invalid.

Applying the general least-squares best-fit approach tothe Log(E)-Log(M) relation underestimates the slope of thetrendline and overestimates the intercept (Figure 12).

When evaluating the log(E)-log(M) relationship we areMMnot interested in obtaining log(E) as a function of log(M) butMMin the general statistical relationship between theseparameters.

Wesseloo and Potvin (2012) suggested the use of thelog(E)-log(M) quantile-quantile (QQ) relationship for thisMMpurpose, which is the method implemented in the softwaremXrap (Figure 13).

It should be noted that this approach, although muchsimpler, is equivalent to the approach suggested by Mendecki(2013).

The QQ method for obtaining the log(E)-log(M)MMrelationship is performed as follows.

➤ Independently sort E and M, both in ascending order➤ Plot log(Ei) against log(MiMM ) for every value of i. Note

that the actual values of log(Ei) and log(MiMM ) are notfrom the same event and the only link between them isthe fact that they both represent the N

i quantile of thetwo different sets log(E) and log(M)MM

➤ For practical use a best-fit line can be fitted to the QQrelationship. ◆

▲


Figure 12—Least-squares ‘best fit’ to the log(E(( ) log(EE M(( ) dataMM

Figure 13—‘Best fit’ to the QQ plot of log(E(( )-log(EE M(( ) dataMM

IntroductionOne of the cornerstones of seismic datainterpretation and hazard assessment is theGutenberg-Richter (GR) relationship in thefrequency-magnitude plot. Estimation of thespatial variation of the b-value is useful forboth the general interpretation of themechanism of rock mass response as well asseismic hazard assessment (Wesseloo 2013).

The assessment of the spatial variation ofseismic parameters in general is discussed in asister paper in this volume (Wesseloo et al.,2014). This paper focuses specifically onobtaining the b-value, as it is quite involvedand warrants a separate discussion.

To estimate the b-value requires that themagnitude of completeness, mmin, is known.As both these parameters vary spatially andtemporally, it is necessary to automaticallyobtain the most likely mmin and b-value forevery spatial subset of data.

The paper discusses the algorithm forspatially sub-sampling the data as well as thealgorithm for obtaining the mmin and b-valuefor every spatial sub-sample.

bb-valueThe frequency-magnitude relationship (inversecumulative distribution) of seismic eventmagnitude generally follows a power lawrelationship which is often described by thewwell-known GR relationship. The b-value

f fdescribes the frequency distribution ofmagnitudes occurring in a given seismic data-set and, as such, is a key component in anyseismic hazard assessment. Assessing thespatial variation in the b-value forms oneof the key components of any seismichazard map.

Apart from the obvious importance of theb-value for hazard assessment, it is also avaluable parameter for interpreting the rockmass deformation and failure mechanism.Several studies in both seismology and themining environment support this notion.

Wyss and his co-workers (Wyss et al.,1997) pointed out that mapping the b-value isequivalent to mean magnitude and assumesthat this is proportional to the mean cracklength. They also point out that along faultzones the low b-values seem to correspondwith asperities (Amelung and King, 1997;Wiemer et al., 1998; Wiemer and Wyss, 1997),while high b-values correspond with creepingsections of faults. High b-values seem to be acharacteristic of active magma chambers(Wiemer et al., 1998; Wyss et al., 1997) whereseismicity is dominated by the creation of newfractures under stress build-up.

Mogi (1962) noted that increasing materialheterogeneity results in a high b-value, whileothers have pointed out that an increase inapplied shear stress (Scholz, 1968; Urbancicet al., 1992; Wyss et al., 1997) or an increasein effective stress decreases the b-value(Wyss, 1973; Wyss et al., 1997).

In the mining environment different b-values have been associated with differentrock mass failure mechanisms. Legge and

Evaluation of the spatial variation of b-valueby J. Wesseloo*

SynopsisThe estimation of the spatial variation of the b-value of the Gutenberg-Richter relationship is important for both the general interpretation of themechanism of rock mass response and seismic hazard assessment. Theinterpretation of b-value as a parameter of rock mass response is discussedin this paper. The methods applied to evaluate the spatial variation of b-value and the algorithm for obtaining the magnitude of completeness andb-value for subsets of data are presented with some verification analyses.The algorithms presented enable the automation of a spatial evaluation ofb-value.

Keywordsmine induced seismicity, seismic hazard assessment, Guttenberg-Richterrelationship, b-value, spatial evaluation.

* Australian Centre for Geomechanics,The University of Western Australia, Australia.



Evaluation of the spatial variation of b-value

Spottiswoode (1987) pointed out that higher b-values maybe expected to result from seismic events occurring ondifferent planes dispersed in a three-dimensional volume,wwhile lower b-values will be associated with eventsdistributed uniformly in two dimensions, as on a singleplane. Further along the scale, very low b-values can beassociated with one-dimensional linear distributions, whichcould result from the interaction between the mining-inducedstress change and extensive planar discontinuities.

The b-value has in some cases been linked to a fractaldimension, D. Aki (1981) related the b-value and the fractaldimension as D = 2b. The conclusions of Spottiswoode andLegge are consistent with a fractal interpretation of thebb-value, where a b-value of 0.7 would relate to a D of 1.5,wwhich could be interpreted as planar spatial distribution ofevents; and a b-value of 1.5 relates to a D of 3, which couldbe associated with a three-dimensional distribution of events.

The spatial assessment of the b-value is, therefore,vvaluable for both hazard assessment and the interpretation ofthe rock mass response to mining.

A generalized and simplified summary of the literature onthe interpretation of the b-value is provided in Table I.

Evaluation of the spatial variation of b-valueIn order to evaluate the spatial variation of b-value, a grid isgenerated over the volume of interest and the b-valueobtained for every grid point. Several crustal seismologystudies were performed where the spatial variation in b-valuewwas evaluated (e.g. Wiemer et al., 1998; Wiemer and Wyss,1997; Wyss et al., 1997). These studies used the methodsdeveloped by Wyss and co-workers that are incorporated intoa Matlab library (Zmap) (Wyss et al., 1997). Themethodology presented here is, in general terms, similar tothe approach used by Wyss and his co-workers.

The method used in this study can be summarized asfollows:

➤ Events with magnitudes much smaller than theestimate of the overall sensitivity based on the wholedata-set (mmin - Δ) are excluded from the analysis. Thisis done to speed up the calculations by excluding verysmall events that do not contribute to obtaining theb-value. Including these very small events also has anegative impact on the overall algorithm performanceas it reduces the number of events useful for b-valuecalculations within the search distance Rmax from eachgrid point

➤ For each grid point, the mmin and b-value are obtainedfrom the closest N points and calculated with the searchradius limited to a value Rmax. Rmax is a user-defined

fvalue which depends on the resolution of the data, thedata density, and the purpose of the analysis.

With this method, only the maximum search distance isspecified and the real search distance is determined by thedistance to the Nth neighbouring event. Each grid pointNNtherefore has a unique search distance. This is illustrated inFigure 1, where the sizes of the spheres illustrate the searchvolume with radius Rmax.

A user-defined Rmax value limits the analysis to beperformed on grid points with N or more values within aradius smaller or equal to Rmax, the purpose of which is torestrict the grid point b-value to local data.

Several quality checks are built into the analysis, whichare discussed in the sister paper (Wesseloo et al., 2014). Inaddition to these, the following checks are also implementedin relation to the b-value assessment:

➤ The value of mmin must be within reasonable expectedbounds

➤ The number of events with magnitude greater thanmmin must exceed a set threshold value.

▲


Table I

Typical interpretation of b-value in terms of rock mass deformation mechanism

Low b-value High b-value References

Events localize on a plane Events spread out within the volume Legge and Spottiswoode (1987)

Corresponds with asperities on a fault Correspond with creeping sections of faults Amelung and King (1997); Wiemer and Wyss (1997); Wyss et al. (2004)

Increasing material heterogeneity Mogi (1962)

Increase in applied shear stress Scholz (1968); Urbancic et al. (1992)

Figure 1—Illustration of the search distance and associated b-value foran Australian mine

This added quality check is necessary to ensure thatreasonably stable b-values are obtained, as the standarddeviation of the b-value is inversely proportional to thesquare root of the number of events used in the b-valuecalculation (the number of events above mmin) (Kijko andFunk, 1994), i.e.:

[1]

AAlgorithm for finding mmin and b for a given subset of

dataIn order to obtain the grid-based spatial distribution of thebb-value, I developed an algorithm to automatically obtain themmin and b-value for the subset of data associated with everygrid point. Examples of the results obtained by the algorithmare shown in Figure 2.

The algorithm aims to maximize the number of datapoints included above mmin, while minimizing the deviationfrom the log-linear GR relationship. This described process isperformed for any data-set for which the mmin and b-valueneed to be obtained.

The process for obtaining mmin can be described asfollows:

➤ Sort the data-set of magnitudes in descending order➤ Calculate the b-value for subsets of the data, where

each subset of data is defined as consisting of datapoints 1 to k where k varies between 10 and the fullnumber of data points in the data set. The b-value foreach subset, i, is therefore defined as follows:

[2]

➤ fThe minimum value of k = 10 is an arbitrarily chosenpractical lower limit and can be set higher. The purposeof this minimum value is simply to ignore erraticbehaviour for the very high tail end of the distribution

➤ For each subset k, obtain the Kolmogorov-Smirnovgoodness of fit parameter, KS. This parameter is notsensitive to the exponential tail end of the distributionand is, therefore, well suited for the stable estimationof mmin

➤ The decision parameter, C, is defined as follows:rr

[3]

The decision parameter has the form (A(( · B ) · (C ), witheach of these components combined to provide a goodand stable estimate of mmin.A gives more weight to steeper b-values. Thiscomponent mitigates against the search algorithmovershooting the true mmin value as a result of abalancing effect of residual values on both sides of thebest-fit relationship near the mmin value.B gives more weight to more data included above themmin value and works against local minimum valuesfor small values of k.C defines the goodness of fit, with larger valuesdefining a better fit

➤ The value of k for which Ck is a maximum defines thenumber of data points in the full data-set. That is:

[4]

Independent check for the algorithm

Check on the second momentFigure 3a shows the result of the mmin algorithm applied to



mmin and b-value using the proposed algorithm


fdata from an Australian mine. The dashed lines show the GRrelationship for b ± σbσ . The change in the decision parameteris shown in Figure 3b. Figure 3c shows the first and secondmoment values of the distribution with mmin assumed to beat each of the event magnitude values. This graph serves asan independent check of the algorithm.

Figure 3c shows the 1st and 2nd moment values of thedistribution with mmin assumed to be at each of the eventmagnitude values. For a negative exponential distribution,the first and second moments (mean and standard deviationof the distribution) are equal.

It should be recognized that the open-ended GRrelationship is the negative exponential distribution of thetranslated values of (Magnitude - mmin).

For the GR relationship, therefore, the translated first andsecond moments should be equal. The change in thecalculated first and second moments of the data-set is shownin Figure 3c. In this case, the algorithm estimate of mmin is atthe smallest magnitude before the values of the first andsecond moments start to deviate from each other. That is thesmallest value of mmin at which the distribution exhibits thecharacteristics of a negative exponential distribution. As thesecond moment is not used in the mmin algorithm, thisprovides independent support for the reliability of thealgorithm.

AAlgorithm verification using synthetic data-setsIn order to verify the developed algorithm, synthetic data-setswwere generated. These enable the known values of thebb-value and mmin to be compared with those obtainedautomatically through the use of the proposed algorithm.

To generate the synthetic data-set, random deviatesampling was performed from a specified Truncated GRrelationship. This data-set was randomly distributed in arectangular volume with an arbitrary chosen array of sensors.The distance between the event and sensor location was used

to determine which events will be detected by the system. Theresulting data-set provides one data sample for testing theproposed algorithm.

▲


Figure 3—Results from mmin-b-value algorithm applied to data from anAustralian mine; (a) provides the frequency-magnitude relationship,while (b) and (c) plot the change in the value of the decision parameterand the first and second moment of the magnitude, with magnitudechange, respectively

Figure 4—Example results of the verification tests performed on synthetic data-sets

The abovementioned process was repeated severalhundred times to obtain the distributions of mmin andbb-values. Two examples are shown in Figure 4.

In the figure, a histogram of the estimated mmin values isshown in blue on the chart on the right side. Also shown inthe right hand chart is one of the several hundred sampleddata-sets in red, together with its corresponding mmin valueas a black dot on the curve and the fitted GR relationship as asolid blue line.

The distribution of mmin values follows a fairly narrowdistribution around the true value.

On the left side in Figure 4, a histogram of estimatedbb-values is shown in green. The distributions of b-valuesfollow a normal distribution around the specified b-value. Itshould be noted that a variation in the estimated b-value willresult from the fact that each of the samples provides only asmall portion of the true population. This can be seen inFigure 5. The solid green histogram shows the variation inthe estimated b-value obtained from the data-set for whichthe mmin value is estimated, while the open blue histogramshows the distribution of results for which the true mminvvalue is specified.

The similarity in the distributions confirms the reliabilityof the method for obtaining a good GR relationship for thedata-set. Note that it is not implied that same b-value isalways obtained for the two cases, but that the inherentuncertainty in the b-value is not increased by applying thealgorithm to calculate mmin.

Results of evaluation of the spatial variation of bb-valueThe method described previously was applied to the data-base of Tasmania Mine (formerly known as Beaconsfield).Figure 6 shows the isosurfaces enveloping the higher,intermediate, and lower b-values for different dates duringthe history of the mine. The green volume indicates b > 1.2,orange indicates 0.8 < b < 1.2, and red b < 0.81. The volumecovers changes over time as mining progresses and theseismically active volume increases.

This method clearly demonstrate that the b-valuechanged over time for different areas. Some areas show ahigh b-value which reduces as mining progresses, and laterincreases with further progressing of the mining. The originalhigh b-value is associated with fracturing taking place asstress change occurs. With further mining deformationmechanism is dominated by larger structures with anassociated lower b-value. At later stages in the mining, thestresses on these structures are released and the seismicitytakes the form of continued fracturing in the hangingwall.

This is similar to the results reported for a deep-levelSouth African environment by Legge and Spottiswoode(1987). In contrast to some of the other areas, the western(left) abutment continues to exhibit a low b-value throughoutthe mining history. This implies that seismic deformation inthis area is typically concentrated on major structures.



Figure 5—Example results of the verification tests performed onsynthetic data-sets

Figure 6—Examples of the spatial variation of the b-value at TasmaniaMine at different stages throughout the mine life

1Note that these isosurface values were chosen for the local magnitudescale used on site, which tends to give lower b-values than for momentmagnitude


Concluding remarksThe b-value of seismicity has been linked to the seismicdeformation mechanism of the rock mass and as such isimportant for quantitative seismology in mines. The spatialand temporal evaluation of the b-value is important for theevaluation of the rock mass response to mining and changesin seismic hazard as a result of mining.

The evaluation of the spatial variation of the b-value wassuccessfully implemented on a grid basis with an automatedmethod for obtaining the mmin and b-value for subsets ofdata.

AAcknowledgementsMy sincere appreciation to Mr William Joughin and ProfessorRay Durrheim for reviewing the paper. I thank the followingorganizations who provided funding for this research throughthe Mine Seismicity and Rockburst Risk Management project:Barrick Gold of Australia, BHP Billiton Nickel West, BHPBilliton Olympic Dam, Independence Gold (Lightning Nickel),LKAB, Perilya Limited (Broken Hill Mine), Vale Inc., Agnico-Eagle Canada, Gold Fields, Hecla USA, Kirkland Lake Gold,MMG Golden Grove, Newcrest Mining, Xstrata Copper (KiddMine), Xstrata Nickel Rim, The Minerals Research Institute ofWWestern Australia.

ReferencesAKI, K. 1981. A probabilistic synthesis of precursory phenomena. Earthquake

Prediction, an International Review. Maurice Ewing Series, vol. 4.Simpson, D.W. and Richards, P.G. (eds.). American Geophysical Union,Washington. pp. 566–574.

AMELUNG, F. and KINGKK , G. 1997. Earthquake scaling laws for creeping and non-creeping faults. Geophysical Research Letters, vol. 24. pp. 507–510.

KIJKOKK , A. and FUNK f, C. 1994. The assessment of seismic hazards in mines.Journal of the South African Institute of Mining and Metallurgy, vol. 94,no. 7. pp. 179–185.

LEGGE, N. and SPOTTISWOODE, S. 1987. Fracturing and microseismicity ahead of adeep gold mine stope in the pre-remnant and remnant stages of mining.6th ISRM Congress, Montreal, Canada. Balkema, Rotterdam.pp. 1071–1077.

MOGI, K. 1962. Magnitude-frequency relation for elastic shock accompanyingfractures of various materials and some related problems in earthquakes.Bulletin of the Earthquake Research Institute, University of Tokyo,vol. 40. pp. 831–853.

SAMMIS, C., NADEAU, R., WIEMERWW , S., and WYSSWW , M. 2004. Fractal dimension andb-value on creeping and locked patches of the San Andreas Fault nearParkfield, California. Bulletin of the Seismological Society of America,vol. 94. pp. 410–421.

SCHOLZ, C. 1968. The frequency-magnitude relation of microfracturing in rockand its relation to earthquakes. Bulletin of the Seismological Society ofAmerica, vol. 58. pp. 399–415.

URBANCIC, T.I., TRIFU, C.I., LONG, J.M., and YOUNG, R.P. 1992. Space-timecorrelations of b-values with stress release. Pure and Applied Geophysics,vol. 139. pp. 449–462.

WESSELOO, J. 2013. Towards real-time probabilistic hazard assessment of thecurrent hazard state for mines. Proceedings of the 8th InternationalSymposium on Rockbursts and Seismicity in Mines. Saint-Petersburg -Moscow, pp. 307–312.

WESSELOO, J., WOODWARD, K., and PEREIRA, J. 2014. Grid based analysis ofseismic data. Journal of the Southern African Institute of Mining andMetallurgy, vol. 114, no. 10. pp. 815-822.

WIEMERWW , S., MCNUTT, S., and WYSSWW , M. 1998. Temporal and three-dimensionalspatial analyses of the frequency-magnitude distribution near Long ValleyCaldera, California. Geophysical Journal International, vol. 134. pp. 1–13.

WIEMERWW , S. and WYSSWW , M. 1997. Mapping the frequency-magnitude distributionin asperities: An improved technique to calculate recurrence times? lJournalof Geophysical Research, vol. 102. pp. 15115–15128.

WYSSWW , M. 1973. Towards a physical understanding of the earthquake frequencydistribution. Geophysical Journal of the Royal Astronomical Society,vol. 31. pp. 341–359.

WYSSWW , M., SHIMAZAKI, K., and WIEMERWW , S. 1997. Mapping active magmachambers by b-values beneath the off-Ito volcano, Japan. Journal ofGeophysical Research, vol. 102. pp. 20413–20422. ◆

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IntroductionTendon support units are primarily tested fortensile and shear strength and performance.Hangingwall support designs are frequentlybased on the axial load-bearing capacity of thetendons (i.e. the pure tensile strength of thetendon), rather than the tendon’s load-bearingcapability in shear (which is usually signifi-cantly lower than the tensile strength). Theload-bearing capacity and support performanceof tendons under different combinationloading situations and installation anglesshould be investigated and quantified toensure this information can be incorporatedinto the fundamentals of the support design.This becomes even more significant whengeological structures are known to be present.

In an ideal world, tendons would beinstalled at 90° to the surface of theexcavation, yet this is sometimes notphysically or practically achievable. Across the

industry, accepted installation angles typicallyvary between 70° and 90° to the hangingwall,with even lower angles being specified (and/ormeasured) on occasion. Justification andsupport performance information for thesedifferent installation angles should beavailable for the support design. At all instal-lation angles (including 90°) the varied natureof geological features (including joints) canresult in various combination loadingsituations and different loading angles on atendon. This does not create pure tensile orshear loading, but results in variouscombinations of concurrent tensile and shearloading on the tendon, influencing theperformance characteristics used for thedesign.

Limited information and test results areavailable for tendon performance undercombination loading. A testing programme ofcombination loading on tendons was thereforeconducted. By sharing the outcomes anddifficulties encountered in performingcombination load testing on friction tendonsupport units, the authors hope to assist inadvancing the combination load testingmethod towards a standardized internationaltest method for all types of support tendons.

Such a standardized combination testmethod will deliver more appropriate supportcapacity data to better address variedgeological influences, allow for better supportdesigns, and assist with back-analysis afterfailures. This would also provide manufactureswith a standard to test their products against,and rock engineers with a constant base forcomparison between support units, as well asmore reliable criteria for selection of theappropriate support unit type for a specificrock mass environment.

Testing tendon support units under acombination loading scenarioby N.L. Ayres* and L.J. Gardner*

SynopsisTendon support systems have been successfully used to stabilizeexcavations. Tendon support systems are routinely designed using theaxial load-bearing capacity of tendons, namely the tensile strength. Toattain tensile strength the tendon must be loaded along its length, whichoften does not occur in practice. Tendons should optimally be installed at90° to the surface of the excavation to achieve maximum penetrationdepth, yet this is often not physically or practically possible, and instal-lations at angles less than 90° occur.

Furthermore, the intersection of geological features within the rockmass frequently results in complex loading situations on tendons. Theposition and angle at which loading occurs results in differentcombinations of tensile and shear forces acting on the tendon, which canimpact on the support performance of each unit and ultimately the wholesystem. All factors that influence the support system should be understoodand taken into account to ensure a sound support design.

Combination loading situations are further investigated and tested toobtain a better understanding of the mechanisms involved and the effectson tendon load-bearing capacity. Tendon support units were tested atdifferent installation angles to establish the tendon performance,mechanical behaviour, and load capacity during these loading situations.The results and outcomes are aimed at providing rock engineers withadditional data and improved understanding of how tendons could performunder certain conditions.

Keywordstendon support, combination loading, shear strength, tensile strength.

* Impala Platinum Limited.© The Southern African Institute of Mining and



Testing tendon support units under a combination loading scenario

Process for determining testing method forcombination loadingOwing to the limited literature dealing with combinationloading, together with the lack of a formal testing method,the testing jigs and test method for combination load testingwwere developed by trial and error. Several factors wereinvestigated to determine the basis of the testing method,including:

➤ Typical installation angles and how these influencetendon loading

➤ Geological and mining-induced structures/disconti-nuities within the rock mass and how these influencetendon loading, particularly when combined with arange of installation angles

➤ The type of tendon being tested➤ Characteristics of the testing machine➤ Simulating how the behaviour of an installed tendon is

affected by the in situ rock mass environment, partic-ularly with regard to the loading forces and directions.

Installation angleIn Figure 1, a number of installation angles are illustrated,ranging from -90° to +90° from the horizontal. All instal-lation angles between these extremes are possible. It can beseen that, due to the layout of an excavation, a number ofdifferent installation angles are required. In certainsituations, some installation angles are not achievable inpractice due to limitations of excavation size and/ororientation. In particular, it should be noted that in a 35°inclined excavation the typical tendon installation of 90° tothe hangingwall results in an installation angle of less than90° to the horizontal (as illustrated in Figure 1, this results ina 55° installation angle).

Geological structures and rock blocksMost rock masses are divided into blocks of various sizes,shapes, and orientations by joints and other geologicalstructures. All blocks are subjected to the influence of gravity,wwhich always acts vertically. The position and orientation atwwhich the excavation intersects the blocks, together with theorientation of the structures, results in various loadingdirections as illustrated in Figure 2.

Tendon A in Figure 2 is installed vertically and at 90° tothe hangingwall, and should be subjected to a pure tensileload under the influence of gravity. However, due to theorientation of the geological structures, the resultant loadingforce is not vertical and a combination of tensile and shearloading now acts on the tendon. This, together with the rangeof tendon installation angles, leads to an infinite number ofpossible loading situations.

Testing of all installation angles and joint intersections isneither practical nor possible, and therefore only selectedangle intervals were tested. Results can be interpolatedbetween testing intervals. It should be noted that in manyinstances during mining, induced stresses can influenceexcavations from any direction and thereby further complexloading situations are created in a three-dimensionalenvironment. Combination load testing is a simplification asonly a two-dimensional loading situation is simulated.Different failure modes such as shear, dilation, cantilever,and toppling are possible, although only the shear anddilatory failures are discussed in this paper.

Movement along geological structures will occur as eithershearing (where lateral movement occurs along the jointplanes) or dilation (where the joint planes move away fromeach other and open up). Pure tensile and pure shear loadingoccur at 0° and 90° respectively, as illustrated in Figures 3and 4. Combination loading occurs between 0° and 90° – thisloading is either predominantly tensile (tendon is extended)or predominantly compressional loading (tendon isshortened). The combination of the tensile and shear loadcomponents varies as the loading angle changes in relation tothe tendon’s long axis.

Where the force acts parallel to the failure plane, shearingoccurs (as illustrated in Figure 3). Loading will then be byeither tensile or compressional shear. In Figure 3, the tensileshear zone (where the tendon extends) is to the right of 0°,and the compressional shear zone, where the tendon will becompressed along the failure plane, to the left of 0°. At 45°,the shear, tension, and compression components are equal.This should be the inflection point at which the mechanicalperformance of the tendon can change.

Where the forces do not act in parallel, but rather at anangle across the failure plane, dilation occurs. Figure 4illustrates the combination loading situations where the force

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Figure 1—Illustration of tendon installation anglesFigure 2—Geological structure orientations creating different loadingdirections

acts perpendicular to a horizontal failure plane. To the leftand the right of 0°, the effect on the tendon will be the same.AAs the load angle on the tendon changes from 0° to 90° thetensile component will decrease from maximum to zero whilethe shear component increases from zero to maximum.

Figure 5 further illustrates all these loading conditionsoccurring in situ within the rock mass. The loose block willload the tendon and any movement on the joint plane willcause the load to be concentrated at the point where thetendon intersects the structure. This will be true for frictiontendons and full column grouted tendons, as they behave inthe same manner. Mechanically end-anchored tendons willtake up load along a greater portion or the entire length ofthe tendon, depending on the loading direction.

Tendon typeThe type of tendon used for the testing programme was thehydraulically pre-stressed friction bolt manufactured by NewConcept Mining (Pty) Ltd. For a friction tendon there is directcontact between the entire length of the tendon and the rockmass and therefore loading will be concentrated at theintersection of a structure, as previously explained. Li andStillborg (1999) propose the concepts of ‘neutral point’, ‘pick-up length’, and ‘anchor length’ which describe themechanical coupling at the interface between the rock and a

ffriction tendon. The paper discussed the shear stress anddecoupling along the tendon during loading and how thisaffects the axial load. This provides a better understanding ofthe loading in a jointed rock mass and how several axialstress peaks can exist along the tendon.

The HydraboltTM and XpandaboltTM are manufactured inthe same way; the difference lies in the pressure remaining inthe tubular tendon after pre-stressing. The tendons are pre-stressed with water and the XpandaboltTM releases the waterafter pre-stressing, while the HydraboltTM retains the water inthe tube, resulting in a stiffer support unit and a shortercritical bond length. Both tendons have a roll or ‘valley’ alongthe length of the tendon which creates a sectional profileresembling a horseshoe shape. For the tests, the ‘valley’ wasalways placed in the same direction to ensure the testedprofile was constant, to counteract any possible effect of thetendon profile on the load performance.

Testing machineAs the tendons must be tested at different angles, a jig isrequired to hold the tendons in the required positions. A two-part steel jig (as seen in Figure 6) houses the tendon andcreates the required failure plane for testing. Examination ofthe testing machine revealed that limited space was availablefor the tendon and the test jig; therefore the test jigs couldnot be very bulky. The stroke (i.e. the distance over whichthe machine can create a loading force) was limited. Theclevis attachment points on the machine were alignedvertically. The jig attachment flanges and holes, as well asthe failure plane, had to intersect this vertical line so thatforces were transmitted to the tendon and not to the test jig.Any influence from the test jig would skew the results of thetendon performance. The jig attachment flanges and holeshad to be correctly aligned so that the jig portions, failureplane, tendon, and the loading direction all lined up.

Attachment flanges tended to be large, to enable lining upthe holes and failure plane, yet they needed to be kept assmall as possible to utilize the stroke of the testing machine.As the angle of installation tended toward 90°, the holes inthe attachment flanges grew further apart; this was much

fmore pronounced in the vertical failure plane testing. Many ofthe tendons in the 70° and 80° range fitted into the machinebut too short a stroke was available to test the tendonssuccessfully; so the test set was incomplete.



Figure 3—Combinations of shear loading

Figure 4—Combinations of tensile loading

Shear loading, cause by lateral movementg, yCombinations of loading created on a vertical failure plane under theg pinfluence of gravity at various intersection anglesg y g

Figure 5—Different combination loading situations due to geologicalstructures


f ffTesting of the tendons at different installation angles, andtherefore under different combination loading situations ordifferent combinations of tensile and shear load, was madepossible by creating failure planes (one vertical and onehorizontal) that intersected the tendon axis at differentangles, as shown in Figure 6 (installation angles of 45° (left)and 70° (right) are shown). This allowed the force or load toact at different angles across the tendon axis and therebyproduced different combinations of tensile and shear forcecomponents. A tendon installed in the test machine at 60°installation angle with a horizontal failure plane is shown inFigure 7.

The test machine generates either a compressional ortensile force in a vertical direction. To achieve shear or lateralmovement the force must act parallel to the failure plane andthe failure plane must therefore be vertical, as seen inFigure 6. All tests where the failure plane was vertical arereferred to as vertical tests. The vertical failure planeintersected the tendon axis at different angles and this inturn related to different installation angles. All possibleconfigurations could be achieved with this set-up. The tensileforce component increased as the intersection angle betweenthe failure plane and the tendon axis decreased from 90°towards 0°. Jig configurations allowed for the testing oftensile shear only – no compressional shear was tested in theinvestigation.

To achieve dilation or opening up of joints, where forcesact perpendicular to the failure plane, the failure plane had tobe horizontal, as seen in Figure 6; therefore these tests arereferred to as horizontal tests. The force acted in the gravita-tional direction and this configuration resulted in predomi-

f fnantly tensile forces. The failure plane intersected the tendonaxis at different angles and this in turn related to differentinstallation angles. The limited extent of the stroke on themachine did not allow for the testing of high (i.e. near-vertical) installation angles as the tendons were too long. Theshear force component increased as the intersection angle ofthe failure plane to the tendon axis decreased from 90°towards 0°.

Bending moment and rotation have a large influenceduring the combination loading. In the investigation, onlyforces that acted parallel or perpendicular to the failure planewere investigated. Numerous other configurations can existwhere the force acts at angles less than 90° to the failureplane. This can occur where the joint or failure plane is nothorizontal and loading occurs under the force of gravity orthe applied force direction is not vertical i.e. when a rotationor cantilever occurs. The results of tests where the appliedforces act either parallel to, or perpendicularly across the joint/ failure plane could possibly be interpolated to representsuch situations.

The test jigs were prepared in such a manner that theapplied force acted either parallel or perpendicular to thefailure plane. Observations during the tests revealed that thevertical test configuration created more than just acombination of tensile and shear forces. Compression acrossthe axis of the tendon was generated in the area of the failureplane, which increased the circumferential or radial forcesdue to the decrease in the circumference. During horizontaltests, a number of couples and moments occurred, whichresulted in rotation.

To limit rotation of the test specimen during testing, twotendons were used to attach the jig to each clevis. Tendonswere installed upside-down in the machine. The machine waszeroed at the lowest position to allow for the maximumstroke length. The upper section of the machine was raisedvertically to represent a purely gravitational load. The jig wasthen pulled until the tendon failed, and the strength of thetendon material determined. In the case of the 90° test, theloading force acted axially along the tendon, creating a purelytensile force on the tendon.

Simulating in situ conditionsSimulation of the in situ conditions of the installed tendonand the surrounding rock mass was investigated to reproducethe loading conditions to which tendons are subjected. As thetendons being tested were friction bolts, testing was carriedout at the same diameter and profile that the tendons wouldbe when loaded underground. A jig was required topressurize the tendons to the correct diameter. As thetendons could not be properly secured in the testing machineat different installation angles, several jigs were required.

Simulation of all in situ conditions was not possible forall components for a number of reasons, including:

➤ All jigs were constructed from steel tubing. The frictioncontact plane was thus steel (tendon) on steel (jig). Thefrictional load from steel on steel is much lower thanthat of steel on rock. Where possible, the tendon waslocked into the jig to prevent slipping to test the loadperformance of the tendon, rather than the pulloutstrength

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Figure 7—Test set-up for a tendon installed at 60° with a horizontalfailure plane

Figure 6—Test jigs used for testing tendons at 45° (left) and at 70°(right)

➤ fTesting was carried out with all forces acting in thevertical plane as per gravity

➤ The test jigs were set up so that the centre of gravityfor each test was in the centre of the failure plane.Testing did not take into account other loadingsituations, such as cantilevering or where the centre ofgravity could be offset from the tendon axis

➤ The steel jigs created sharp edges along the failureplanes, which would form prominent shearing/cuttingedges in the vertical tests.

➤ The rigidity of the jigs did not allow for any breakout inthe areas where the main force concentrations occur(breakout could occur here in the in situ conditions),although some deformation did occur at these points.

The test jigs were constructed to form a stiff testingsystem, which aimed to represent reality. During the initialtests, the rotation and deformation of the jigs revealed thatthe testing system was not stiff enough. A second set of jigswwas constructed, using thicker steel tubing and with gussetswwelded along the length of the jig. Two holes (instead of one)wwere cut into the attachment flanges and clevises to preventrotation.

Despite these improvements, rotation of the test tendonsstill occurred, indicating that the system was possibly still toosoft. The progressive rotation during testing of a tendon and30° test jig is shown in Figure 8. Deformation and failure of asecond generation 65° vertical test jig with gussets is shownin Figure 9. The two attachment tendons at the attachmentflange onto each clevis (as shown) failed to prevent rotationon the jig about the end of the gusset position, resulting inthe test jig tearing along the attachment flange.

The results for the combination loading tests and puretensile test are illustrated in Figure 10. This shows all the testresults with the maximum loads achieved during everycombination load and tensile test. The data includes themaximum loads achieved during slipping of tendons and jigfailures, which introduces a large degree of variability intothe data for the combination loading and tensile tests. Someof the loads therefore appear to be low and trend lines cannotbe established as the data is skewed by the affected data. The

fdata-set is not complete, and further testing is required toadequately describe the performance, yet the general trend ofperformance is shown.

From the failure curves for the vertical and horizontaltests respectively at the different tendon installation angles, itwas noted that the curve profiles for both the HydraboltTM

and X-PandaboltTM for each test type (i.e. the vertical andthe horizontal tests) are similar. This indicates that the testresults are comparable and that both tendon types behavesimilarly, but with different degrees of stiffness and loadcapacities.

Conclusions

Testing process➤ Combination load testing is a challenging process that

requires testing jigs to be prepared for differentintersection angles. This can be time-consuming andcostly

➤ Thought must be given to fitting the test jig and tendoninto the testing machine and achieving the correctloading direction, so as to test the tendon performancewithout interference from the test jig itself

➤ Simulation of in situ installed tendon and rock massconditions is difficult, but must be taken into consid-eration



Figure 8—A series of photographs from a 30° horizontal test showingthe rotation that occurs during testing Figure 10—Plot of maximum loads achieved for all tests

Figure 9—A test jig with 65° installation angle and vertical failure plane,where failure has occurred on the test jig itself


➤ Robust test jigs are required, as the torque componentloads affect both the test jig and testing machineadversely. A test jig machined from a solid block ofmetal would be more appropriate, although theresilience of such a jig for conducting multiple testswould have to be confirmed

➤ In a testing programme, the testing machine and alimited number of jigs should be made and tested in afirst phase. Unexpected defects in the jig design andtest outcomes may require modification and re-manufacture of the test jigs

➤ Anchoring the tendon into the jig securely andpreventing slippage is problematic – this requiresfurther experimentation for each type of tendon to betested

➤ Testing at higher installation angles requires largertesting machines with longer strokes (over 1 m) toaccommodate vertical alignment of the attachmentpoints

➤ Photographs and videos taken during the testingprocess represent very valuable evidence, as theyrecord actions that cannot be seen with the naked eyeand can be revisited numerous times after testing

➤ If possible, physical investigation (and re-investi-gation) of the failed units can offer clues to themechanism of failure and are valuable records.

Test results➤ Combination loading of tendons commonly occurs in

any rock mass, due to the variety of jointingorientations and tendon installation angles

➤ The intersection angle of the tendon and the geologicalstructure/discontinuity, together with the position andorientation of the load, will determine the ratio oftensile component versus shear component

➤ Generally, the tendon failure mode tends towardstensile failure, where the tensile component is higherthan the shear component

➤ Where the shear component is higher than the tensilecomponent, the failure loads are much higher than forpure shear

➤ A component of rotation is involved in cases where thetendons tend towards tensile failure, and this can aid inwedging blocks of rock in place and preventing failures.

Finally, this testing programme represents a mere startingpoint for understanding the combination loading on tendons.Further testing of all types of tendons is required to reach abetter understanding of the effects of combination loading ontendon support units and systems.

AAcknowledgementsThe authors wish to express their gratitude to themanagement of Impala Platinum Limited for assistance inperforming the research and permission to publish the work.The authors acknowledge the contribution of New ConceptMining (Pty) Ltd for the supply of tendons, testing facilities,and assistance during testing.

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IntroductionMalmberget town (Figure 1) is located innorthern Sweden, approximately 70 km northof the Arctic Circle.

The Malmberget iron-ore deposits havebeen known for centuries. The ore was initiallytransported from the mine by horses andreindeer, until the railway to the coastal city ofLuleå was completed in 1888 and large-scaleproduction began. Today most of the refinediron ore products from Malmberget such asfines and pellets are transported to Luleå for

use in steel mills mainly within the Baltic Searegion. The mine consists of around 20orebodies of varying sizes and shapes(Figure 2).

Production in 2012 was 14 Mt of crude orefrom 12 of the orebodies mined to a maximumdepth of approximately 850 m below surface.The main mineral is magnetite, but there arealso smaller quantities of haematite. The mineis operated by Luossavaara-Kiirunavaara AB(LKAB). The town of Malmberget has existedin close relationship with the mine for wellover a hundred years. The first houses werebuilt by miners who wanted to live close totheir place of work. More miners followed andsoon a shanty town resembling those found inKlondike during the gold rush era wasestablished on the hill slopes of Malmberget(Figure 3).

The rich ore deposits were not depleted,and as the town grew the old shacks wereeventually replaced by modern buildings.Mining started in open pits, but by the 1920smore than 90% of all mining was performedunderground (Forsström, 1973). Manyorebodies dip to the southwest, whichgradually brings mining activities closer to thetown itself. Since the 1960s sublevel cavinghas been the predominant mining method.This is a large-scale method, in which differentactivities take place on several levels simulta-neously (Figure 4).

This method makes it possible toefficiently mine deposits at great depth atcompetitive cost. The main disadvantages aresubsidence of the ground surface and, forMalmberget town, vibrations caused byseismic events due mainly to the caving ofhangingwall areas (Figure 5).

Estimation of future ground vibration levelsin Malmberget town due to mining-inducedseismic activityby T. Wettainen* and J. Martinsson*

SynopsisMalmberget town is located in northern Sweden, approximately 70 kmnorth of the Arctic Circle. Parts of the town overlie more than 20 ironorebodies, consisting mainly of magnetite with smaller quantities ofhaematite. The mine is operated by the mining company LKAB. Miningstarted in the 17th century, but not until the railway to the coastal city ofLuleå was completed in 1888 did large-scale production commence. Around1920, mining proceeded underground and today sublevel caving is the onlymining method used. Sublevel caving causes subsidence of the groundsurface, and buildings and residential areas have been relocated due to themining activities for more than 50 years. The number of seismic eventsaccompanied by strong ground vibrations is now increasing. In 2008 themine received a permit from the Environmental Court of Sweden to increaseproduction to 20 Mt of crude ore per year. A prerequisite for the permit wasthat the mine conducts a number of investigations regarding the environ-mental impact on the residents of Malmberget. One of these investigationsconcerned how seismicity will change as production increases and whatmeasures could be taken to reduce inconvenience to the town residents.Today the mine possesses an extensive seismic monitoring system withmore than 180 underground and surface geophones. For this study, elevenseismically active volumes in Malmberget mine were identified, and foreach of them, a yearly future maximum magnitude interval was estimatedbased on the current production plan. Relationships between historicalseismic events and measured ground vibrations in the town of Malmbergetwere established, and future ground vibrations caused by expected seismicevents were estimated using a probabilistic approach. The outcome was thenumber of intervals of expected ground vibration per year and permonitoring point. Possible measures to reduce inconvenience for the townresidents include blast restrictions, sequencing, and possibly pre-conditioning. The ultimate long-term solution is an almost completerelocation of Malmberget town. This process has recently been formalizedand LKAB is taking an active part in realizing this goal.

Keywordssublevel caving, mining-induced seismicity, surface vibrations, futureestimations, environmental impact.

* LKAB, Sweden.© The Southern African Institute of Mining and



Estimation of future ground vibration levels in Malmberget town

Buildings, residential areas, and infrastructure have beenrelocated due to the presence of mining areas for more than50 years, and the acceptance of this process is generally highamong the residents of Malmberget. Inconvenience caused byseismic events is, however a relatively new problem.Vibrations from blasts have always been a part of everydaylife in Malmberget, but vibrations from seismic events areperceived differently. Not only do they occur randomly ratherthan at specific hours, but the ground motion is also differentdue to the frequency and characteristics of the source. In2008 the mine received a permit from the EnvironmentalCourt of Sweden to increase production to 20 Mt crude ore peryear. A prerequisite to the permit was a number of investi-gations to be conducted by the mine regarding the environ-mental impact on the residents of Malmberget. One of theseinvestigations concerned how seismicity will change asproduction increases and what measures could be taken toreduce inconvenience for the town residents.

Seismic monitoring systemThe mine possesses an extensive seismic monitoring system,provided mainly by the Institute of Mine Seismology (IMS).The first sensors were installed in 2005, around the timewhen vibrations of unknown origin started to occur inMalmberget. The system has been expanded stepwise and bythe end of 2013 there were around 180 sensors operationalin the mine. The array consists of a few 1 Hz geophones butmainly 4.5 Hz (1/3) and 14 Hz (2/3) geophones. The 1 Hzgeophones are installed on concrete slabs. Five are located onthe surface in a ring formation surrounding the mine. Anadditional 1 Hz geophone is located underground in order toimprove the vertical location accuracy of seismic events. Datafrom the 1 Hz array, which is stored in a separate database,was not used in this project, since the array had only recentlybeen installed. The other types of geophones are all installedunderground, from around 100 m below surface to thedeepest level of the mine. Approximately 500 actual seismicevents are currently processed daily. The classification ofseismic events is done manually. LKAB uses the same localmagnitude scale (MLM ), which is based on seismic energy (E)and moment (M), as many South African minesMM

[1]

In addition to the geophones installed in the mine, thereare also eight 8 geophones bolted to foundations ofresidential buildings around town to serve as vibrationmonitoring points. The incoming vibration is measuredaccording to Swedish Standard SS 460 48 66. Figure 6

▲


Figure 1—Aerial view of Malmberget

Figure 2—Orebodies in Malmberget, metric scale, plan view

Figure 3—Malmberget main street 1895 (LKAB archives)

Figure 4—Sublevel caving

Figure 5—Environmental impact of sublevel caving

f f fshows these monitoring points on surface, for which futurevvibration levels have been estimated. The amplificationeffects on the upper floors of buildings have not beenconsidered, since each building responds differently.

Estimation of future ground vibrations

Seismic sourcesThe first step is to define seismically active volumes orsources in the mine. As new seismic events occur, they areautomatically placed in spatial clusters by the analysissoftware mXrap (formerly MS-RAP), developed by theAAustralian Centre for Geomechanics. Clusters are then sortedinto larger groups by mine personnel on a daily basis.Seismically active volumes were identified by combininggroups together. The criteria for creating a volume were (i) toinclude most events with a local magnitude of 1 or higherand (ii) to obtain a good visual fit to the Gutenberg-Richtercurve. This curve represents a frequency-magnitude distri-bution and is commonly used for seismic analysis (Gutenbergand Richter, 1944; 1954). It shows the activity rate (numberof events) and the relation between small and large eventsfor a given timespan and population. The observedmagnitudes are assumed to be exponentially distributedwwithin the sensitivity of the array. Eleven seismically activevvolumes were identified in the mine (Figure 7).

Only seismic events associated with the seven majororebodies were considered in the current analysis. The otherorebodies are smaller and mined at shallower depths. Theyhave not yet caused significant seismic events and are notexpected to do so within the foreseeable future.

PProduction and activity rateUsing a well-defined population of seismic events, it ispossible to statistically model the distribution of themagnitude of events using the Gutenberg-Richterrelationship, and from that, estimate the distribution of themaximum event for a given period of time. However,

predictions are valid only under the condition that, given theknown covariates, the estimated parameters in the distri-bution will have the same characteristics in the future. TheEnvironmental Court asked LKAB how seismicity will changewhen production increases. For this reason, the historicalrelationship between activity and monthly production wasmodelled for each of the seven major orebodies. All seismicevents, regardless of size, recorded by the seismic systemand spatially associated with the individual orebodies wereconsidered. A linear regression model was used to describethe relationship between mining-induced activity as afunction of production and mining depth during the sametime period. For a given production rate and mining depth,the activity (number of events per month), is assumed to belognormally distributed, and the maximum likelihoodestimation (MLE) of the parameters is considered under thecondition that the parameters are positive.

Mining depth was included as a parameter but proved tobe insignificant for the data considered in this study. Datafrom 2010 until late 2012 was used, and the number of newmining levels established during this time is limited. Theactivity-production relationships were then used to predict



Figure 6—Vibration monitoring points in Malmberget town. Blue line is the industrial fence, and black line is recent fence advancement

Figure 7—Isometric view of events in seismically active volumes. Blackdots represent the surface vibration monitoring points in Figure 6


ffuture activity based on the current production plan (seeexamples in Figure 8–10). In this study we restrict ourselvesto considering the orebodies individually, given their monthlyproduction and their monthly average mining depth. Thismeans that possible interactions between the orebodies areneglected in order to reduce the dimensionality of theestimation problem. On a mine-wide level they aredependent, but to capture this dependency we need to resortto multilevel or hierarchical models (Gelman et al., 2004;Gelman and Hill 2007) and treat the estimation problemjjointly. However, this will increase the dimensionality by afactor equal to the number of orebodies; often together withadditional hyper-parameters describing the mine-wide priordistributions for the model parameters.

FFuture magnitudesThe probability density function (PDF), p(m), of themagnitude m is modelled as a sum of exponential distrib-utions in order to account for the possible presence of several

ffdifferent seismic sources within one single volume. The sumof exponential distributions is also multiplied by a sensitivityfunction for the seismic measurement system. The entire PDFis given by

[2]

where

[3]

is the exponential tail and

[4]

describes the sensitivity function where h(.) denotes theHeavyside function. The term

[5]

depends only on the model parameters and makes theentire distribution in Equation [2] integrate to unity. Theparameter vector for the entire PDF is θθ = [μ, τ, wT, ββTββTββ ]T,where w = [w0,…,wk-1]T is the weights under the conditionthat ||w||1=1 and the elements in ββ = [ββ0β ,…,ββK-1ββK ]T are theparameters for the exponentials in Equation [3]. Figure 11shows an example of the density in Equation [2] and itscomponents in Equations [3] and [4] for K=1 and θθ = [-1,1/2, 1, 2]T.

The number of mixtures to use is determined by theBayesian information criterion (BIC) to avoid over-parameter-ization (Stoica and Selen, 2004; Pintelon and Schoukens,2001). Examples of estimation results using Equation [2]from the different orebodies can be seen in Figure 12-14.Note the model's ability to capture complex features, such asthe data shown in Figure 13 exhibiting an additional activityat m=-1 and the distinct mode in Figure 14.

▲


Figure 8—Modelled historical activity (top) and predicted future activity(bottom) for Fabian orebody

Figure 9—Modelled historical activity (top) and predicted future activity(bottom) for Dennewitz orebody

Figure 10—Modelled historical activity (top) and predicted future activity(bottom) for Kapten orebody

The advantages of introducing a sensitivity function(Equation [4]) is to avoid a hard truncation of the data at aspecific sensitivity threshold using the more commonmagnitude distributions such as the Gutenberg-Richterrelation for magnitudes or the open-ended power law(Gutenberg and Richter, 1944; Ishimoto and Iida, 1939), theupper-truncated power law (Page, 1968) or gamma typedistributions (Saito et al., 1973).

Truncating data in general means that our statisticalmodel is valid only in a specific region of the data. Truncationalso means that we need to estimate the region for which ourmodel is valid, and there is a risk of either throwing awayvaluable data if we truncate too much or jeopardizing thevalidity of our model if we expand this region too much. Thischoice will affect the uncertainty of our parameter estimatesand may also contribute to bias estimates using impropermodels (see e.g. Pintelon and Schoukens, 2001; Kay, 1993;Scharf, 1990). For example, the lower truncation limit forusing a translated exponential distribution (i.e. the open-ended power law) would be in the region containing most ofour data and the estimation effects will be even more severe.The difficulties of applying some of the common magnitudedistributions mentioned above can be seen e.g. in Mendecki(2008). Lasocki and Orlecka-Sikora (2008) also argue thatthe use of these common distribution models brings about anunacceptable and systematic over- or underestimation of theseismic hazard parameters.

Introducing a sensitivity function in Equation [4] meansthat the statistical model is valid for the entire data-set at thecost of two additional parameters (μ,τ) describing thesensitivity of the system. Considering a mixture ofexponentials means that we can also model more complexbehaviour discussed above and allow the collected data,together with an information criterion, to determine theshape of the distribution in the volume of interest.

The other alternative for modelling a complex size distri-bution is to consider a non-parametric kernel estimator of thedensity (see e.g. Lasocki and Orlecka-Sikora, 2008; Kijko etal., 2001). This method has the ability to capture complexshapes and behaviour, but comes at the cost of choosing anappropriate kernel function and corresponding kernel



Figure 11—An example of the probability function in Equation [2] and itscomponents (Equations [3] and [4]) for the parameter vector θθ = [-1, 1/2,1, 2]T

Figure 12—Estimated probability density function (PDF) of themagnitudes in Dennewitz volume

Figure 13—Estimated probability density function (PDF) of themagnitudes in Fabian volume

Figure 14 – Estimated probability density function (PDF) of themagnitudes in Printzsköld 3 volume


fbandwidth (or scale). The main reason for not taking thisroute in this study is that the number of kernel parameters isproportional to the number of events, resulting in anunmanageable density in the sequential Monte Carloanalysis. Also, the largest magnitudes observed here stillhave an exponential (or power law) behaviour that we like topreserve.

Instead, we have focused on using only a few kernels inour mixture model. The kernel includes the sensitivity of themeasurement system in order to avoid the truncationproblems discussed above. It can handle complex shapescaused by multiple sources (Figure 12-14) and we havepreserved the exponential tail (i.e. the power law behaviour).The cost of a parametric mixture model is that we have toestimate the appropriate number of mixtures (or kernels) touse by applying some cross-validation techniques (Ljung,1987) or some well-established information measure (Stoicaand Selen, 2004).

Gutenberg-Richter curves for each volume were plottedand modelled using measured data from 2010 until late2012. Since the sensitivity of the system is limited, smallevents are more difficult to detect and the number of eventsbelow the sensitivity limit is less, as shown in Figures 12-14and the top plot of Figure 15. The vertical axis in latter plotrepresents probability of occurrence but could also shownumber of events. Probability 100% (10° or 1) corresponds tothe total number of events in the data-set. We expect alldetectable events to be larger than approximately magnitude-2, which is roughly the detection limit of the seismic systemin this particular area of the mine. The curves werenormalized to one year and annual activity rates were used toobtain future magnitudes. In this way, maximum expectedmagnitude intervals were estimated for each volume andyyear.

The model of the survival function, representing the bluecurve in the top plot in Figure 15, is given by

[6]

wwhere P(m) is the cumulative distribution function (CDF) ofthe magnitude describing the measured data represented bythe black dots. The survival function simply gives theprobability that the magnitude of an event is larger than m.The complementary cumulative histogram of the magnitudedata, represented by the black dots, is shown with relativefrequencies as opposed to the number of events in eachhistogram bin to obtain an estimated probability. Usingrelative frequencies means that we can plot the comple-mentary cumulative histogram of the magnitudes against theprobability given by the survival function.

For a specific activity (number of events per year),denoted by A, we can estimate the magnitude distribution ofthe largest event that year. If the cumulative probabilitydistribution P(m) of the magnitude m is given, then for agiven specific activity A the probability that m is themaximum value is (P(m))A, assuming independentidentically distributed magnitudes (see e.g. Gumbel (1967),ggand consequently 1-(P(m))A is the probability that m may beexceeded. To simplify the sequential Monte Carlo analysisdescribed below, this distribution is approximated by atranslated exponential distribution describing the dominatedterms in the tail of the distribution (P(m))A. Let SF(m)=1-

P(m f) denote the probability that the magnitude of an event islarger than m. The intersection of the horizontal red line atm=mlow (Figure 15) gives us probability A-1 of occurring. Themagnitude density of the largest event occurring is approx-imated by

[7]

where mh is the magnitude of the largest expected event thatyear and p(m) denotes the PDF of the magnitude of an event.The shape is given by a translated exponential distributionstarting from mlow where the term A makes it integrate tounity. A comparison between the approximation and truedistribution (P(m))A for a specific activity A can be seen inFigure 16.

However, as the estimated activity and the model SF(m)are associated with uncertainties, shown by the dashed redand blue lines in the top of Figure 8, the intersection is alsouncertain. Monte Carlo integration is applied to obtain anaverage distribution p(mh) for K possible intersectionsmlow(k), k = 1,...,K, taking these uncertainties into account.This part of the analysis is where the approximation of(P(m))A in Equation [7] is useful. The effect of the approxi-mation errors using Equation [7] (Figure 16) is small incomparison to the uncertainties from the possibleintersections shown in Figure 15. Note also that theassumption of independent identically distributed magnitudesleading to (P(m))A is often violated in mining conditions. Theresulting average distribution of K possible intersections is

▲


utions for Fabian volume

Figure 16—Comparison between the approximation in Equation [7] andthe true distribution for A=10 000

represented by the blue curve in the bottom plot in Figure 15together with the histogram of the possible intersectionsmlow(k) in red. The green histogram mmedian(k) shows thedistribution of the median magnitude of each p(mh|mlow(k)).To summarize, we approximate the largest event per year tohave a magnitude density according to the blue curve.

One of the 11 identified seismic volumes includes acaving void. The Printzsköld orebody was not initially minedfrom surface, but mining started underground. This createdan empty room that eventually will cave up to the groundsurface. It seems that the Gutenberg-Richter approachoverestimates the magnitudes in this case. The measureddata has a poor fit to the Gutenberg-Richter model(Figure 17).

One possible explanation could be that there is a differentratio between small and large magnitudes in this particularcase. Caving rooms in Malmberget have been monitoredbefore and experience shows that the magnitudes associatedwwith this process are not significant. Instead, the followingequation, presented in Mendecki (2008), was used toestimate future maximum magnitudes for the caving volume.

[8]

wwhere logPgg max is equal to the largest observed seismicpotency plus the largest jump between two record potenciesin the observed series. logPgg was replaced with localmagnitude MLM in this analysis.

Table I shows MmaxMM for each seismic volume and year.The median magnitude of each MmaxMM interval is considered asMMmaxMM here for illustrative purposes, except in columnPrintzsköld 1 alt. where Equation [8] was used and theactual MmaxMM is obtained. Printzsköld 1 is the volume with thecaving room and the magnitude difference between the twomethods vary between MLM 1.1 and MLM 1.4. Even thoughEquation [8] does not take production increase into account,the magnitude it provides is considered reasonable in thiscontext.

Surface vibrationsRelationships between magnitudes, spatial locations, andmeasured surface vibrations were established for eachsurface monitoring point. A regression model is used toestimate the vibration as a function of the magnitude of theevent, the hypocentre location of the event, and the sensorcoordinate on the surface. The vibration is assumedlognormally distributed and MLE of the parameter isconsidered under the condition that the parameters arepositive. A calibration function which is retrieved through

festimation errors from previous events is used to describe theattenuation caused by wave propagation from specificlocations to each sensor. The calibration reduces theestimation errors by 50% when it is evaluated using cross-validation. 275 historical seismic events with a total of 1027corresponding surface vibration values were used forcalibration. Only vertical vibration components wereconsidered since this data is more abundant. Cross-validationwas performed and the root mean square deviation (lgPGV) is0.22. Figure 18 shows measured and predicted peakvibrations from 90 seismic events at a specific monitoringpoint.

Using the surface vibration relations and expectedmagnitude distributions, future vibrations at the monitoringpoints were estimated probabilistically. Monte Carlosimulation was used to sample hypocentre, magnitude, and



Figure 17—Gutenberg-Richter curve, model for caving volumePrintzsköld 1

Table I

Mmax by volume and year

Alliansen Dennewitz Parta 1 Parta 2 ViRi Kapten-Fabian Kapten Fabian Printzsköld 1 Printzsköld 1 alt. Printzsköld 2 alt. Printzsköld 3 alt.

2013 1.9 2.1 2.4 2.7 1.8 1.4 0.5 2.0 3.1 1.8 2.2 2.42014 1.9 2.3 2.5 2.8 1.8 1.6 1.1 1.9 3.1 1.8 2.2 2.42015 2.0 1.8 2.4 2.7 1.9 1.6 1.1 2.0 3.2 1.8 2.2 2.52016 2.0 2.3 2.4 2.6 1.8 1.5 0.5 2.0 3.2 1.8 2.2 2.52017 2.0 2.2 2.3 2.6 1.8 1.6 1.0 2.0 3.2 1.8 2.3 2.52018 2.1 2.3 2.3 2.6 1.8 1.7 0.9 2.1 2.9 1.8 2.0 2.22019 2.0 2.2 2.3 2.6 1.8 1.7 1.0 2.2 3.1 1.8 2.1 2.42020 2.1 2.2 2.2 2.4 1.7 1.7 1.0 2.2 3.0 1.8 2.0 2.3

1.9 2.1 2.4 2.7 1.8 1.4 0.5 2.0 3.1 1.8 2.21.9 2.3 2.5 2.8 1.8 1.6 1.1 1.9 3.1 1.8 2.22.0 1.8 2.4 2.7 1.9 1.6 1.1 2.0 3.2 1.8 2.22.0 2.3 2.4 2.6 1.8 1.5 0.5 2.0 3.2 1.8 2.22.0 2.2 2.3 2.6 1.8 1.6 1.0 2.0 3.2 1.8 2.32.1 2.3 2.3 2.6 1.8 1.7 0.9 2.1 2.9 1.8 2.02.0 2.2 2.3 2.6 1.8 1.7 1.0 2.2 3.1 1.8 2.12.1 2.2 2.2 2.4 1.7 1.7 1.0 2.2 3.0 1.8 2.0

Figure 18—Measured vs predicted peak vibrations, linear scale (top)and log scale (bottom) at monitoring point Sveavägen 7


f fcorresponding surface vibrations for each seismic volume.Figure 19 shows maximum expected peak vibrations for eachyyear at a specific monitoring point.

Table II summarizes the median of the maximumexpected peak vibration values for each monitoring point.The critical year for each point is highlighted. No predictionshave been made beyond 2020 because of large uncertaintiesin the production plan. Every change in production input wwill result in different vibration levels. The maximummeasured vertical vibration ever within populated areashas been 19.5 mm/s, although the absolute majority is below5 mm/s.

Measures to reduce inconvenience

LKAB has identified a number of measures that could betaken in order to reduce vibrations from seismic events inpopulated areas.

➤ Blast restrictions are already applied in the mine andcan be used in specific areas based on previousexperience. A numerical stress model can also be usedto identify potentially critical areas or orebodies. Thenumber of orebodies is decreasing with depth as someof the smaller orebodies will become narrower withdepth while the larger ones will increase in size. Blastrestrictions will have an increasingly negative effect onproduction

➤ Sequencing can be used to avoid stress concentrationsin known structures or weak zones. Currently,sequencing is used primarily to ensure undergroundsafety. Using it also with the intention to reducesurface vibrations will most likely adversely affectproduction

➤ Pre-conditioning methods such as hydraulic fracturinghave been used in other mines to reduce large seismicevents in production areas (Quinteiro, 2012). LKABwill investigate this option further, but the events thatcause disturbances on the surface are usually locatedsome distance into the hangingwalls. It may be difficultto access these areas with pre-conditioning equipment

➤ Relocation of Malmberget town is the ultimate solution.This is also the measure that will provide the desiredresults with high confidence. Malmberget has contin-uously been affected by the mine piece by piece forover 50 years. Buildings have been demolished ormoved and roads have been closed. Since it became

clear to LKAB that the largest orebodies expand withdepth, a long-term action plan for communityrelocation became necessary. In 2012 an agreementwas reached with the municipal council stipulatingLKAB’s responsibilities as well as obligations by local authorities. The agreement contains a time planfor relocation of residential areas in Malmberget(Figure 20). This is dependent on successfulcompletion of corresponding expansion plans by themunicipality in Malmberget’s twin town Gällivare, some5 km to the south. The relocation plan does not includeone area in eastern Malmberget or housing areasowned by LKAB, where efforts will be made tomaintain good living conditions.

ConclusionsLKAB was asked by the authorities to assess the probablelevels of strong ground motion from future seismic eventsand their impact on residents of Malmberget. LKAB used awell-known method based on Gutenberg-Richter curves andobserved historical data to estimate the largest expectedseismic magnitudes. To estimate the impact of an increase inproduction, the curves were adjusted for different activityrates. This modification has to our knowledge not been doneor published before, but is a way to try to answer thequestion from the Environmental Court. The final outcomewas intervals of expected future vibrations under givenconditions and probabilities. One advantage in a miningenvironment is that the driving force of seismic events,mining itself, can be controlled to some degree. Measures canbe taken in order reduce seismic activity, but how fast and towhat extent results will be achieved is impossible to say.Many measures could also lead to production losses and

▲


Table II

Estimated vertical peak vibration (mm/s per year) and monitoring point

Hermelinsbacken 7 Lövberga Konsum Bolagskontoret Hertiggatan 18 Murgatan 1 Sveavägen 7 Malmstavägen 11

2013 10.0 6.1 9.3 18.9 4.5 7.4 13.1 8.2

8.3 14.6 9.15.56.8 10.4 21.111.12014

2015 9.7 6.0 9.2 18.7 5.7 7.3 12.9 8.1

2016 8.7 5.2 8.0 16.4 4.4 6.4 11.3 7.0

2017 9.1 4.7 7.2 14.8 6.0 5.7 10.2 6.3

2018 7.4 4.7 7.2 14.7 6.2 5.9 10.1 6.3

7.1 6.9 10.1 6.37.12019 7.5 4.7 7.2 14.7

2020 6.1 3.7 5.6 11.6 7.0 6.6 8.0 5.0

7.4 4.7 7.2 14.7 6.2 5.9 10.1

6.9 10.17.5 4.7 7.2 14.7

6.1 3.7 5.6 11.6 7.0 6.6 8.0

Figure 19—Estimated maximum vertical vibration levels at monitoringpoint Sveavägen 7

LKAB depends on large-scale production in order to remaincompetitive. The only way to completely avoid complaintsfrom residents near a seismically active mine is not to haveany neighbours at all. It has long been a principle of LKABnot to mine directly under residential areas, even thoughground subsidence caused by mining of shallow-dippingorebodies would not occur in many years, if ever. Therelocation of Malmberget is partly to ensure the comfort ofresidents rather than being a safety issue.

ReferencesFORSSTRÖM, G. 1973. Malmberget. Department of Human Geography, University

of Stockholm. ISBN 91-85336-54-8 (in Swedish).

GelmAN, A. CARLIN, J.B., STERN, H.S., and RUBINRR , D.B. 2004. Bayesian DataAnalysis, 2nd edn. Chapman & Hall/CRC.

GELMAN, A. and HILL, J. 2007. Data Analysis using Regression andMultilevel/Hierarchical Models. Cambridge University Press, New York.

GUMBEL, E.J. 1967. Statistics of Extremes. Colombia University Press, NewYork.

GUTENBERG, B. and RICHTERRR , C.F. 1944. Frequency of earthquakes in California.Bulletin of the Seismological Society of America, vol. 34. pp. 185–188.

GUTENBERG, B. and RICHTERRR , C.F. 1954. Seismicity of the Earth and AssociatePhenomena, 2nd edn. Princeton University Press, Princeton, NJ.

ISHIMOTO, M. and IIDA, K. 1939. Observations of earthquakes registered with themicroseismograph constructed recently. Bulletin of the EarthquakeInstitute of Tokyo University, vol. 17. pp. 443–478 (in Japanese).

KAYKK , S.M. 1993. Fundamentals of Statistical Signal Processing: EstimationTheory, vol. 1, Prentice-Hall. New Jersey.

KIJKOKK , A., LASOCKI, S., and GRAHAM, G. 2001. Non-parametric seismic hazard inmines. Pure and Applied Geophysics, vol. 158. pp. 1655–1675.

Lasocki, S. and Orlecka-Sikora, B. 2008. Seismic hazard assessment undercomplex source size distribution of mining-induces seismicity.Tectonophysics, vol. 456. pp. 28–37.

Ljung, L. 1987. System Identification: Theory for the User. Prentice-Hall, NewJersey.

Mendecki, A. 2008. Forecasting seismic hazard in mines. Keynote address:First Southern Hemisphere International Rock Mechanics Symposium,Perth, Western Australia, September 2008.

Page, R. 1968. Aftershocks and microaftershocks of the great Alaskaearthquake of 1964. Bulletin of the Seismological Society of America,vol. 58, no. 3. pp. 1131–1168.

PINTELON, R. and SCHOUKENS, J. 2001. System Identification: a Frequency DomainApproach. IEEE Press, New York.

QUINTEIRO, C. 2012. Report from mine visit at El Teniente, Chile. Internal LKABdocument.

SCHARF, L.L. 1990. Statistical Signal Processing: Detection, Estimation and TimeSeries Analysis, Vol. 1. Addison-Wesley, New York.

STOICA, P. and SELEN, Y. 2004. Model-order selection: a review of informationcriterion rules. IEEE Signal Processing Magazine, vol. 21, no. 4.pp. 36–47.

WETTAINEN, T., MARTINSSON, J., and PERMAN, F. 2014. Investigation U6, Seismicactivity Malmberget. LKAB report 14-806 (in Swedish). ◆



Figure 20—Time plan for relocation of Malmberget

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Introduction

Outsourcing strategies have been viable inorganizations since the 1980s (Hätönen andEricksson, 2009), and gained significantmomentum during the 1990s (Morgan, 1999;Corbet, 2004). Fill and Visser (2000) consideroutsourcing as one of the most widely adoptedpractices followed by firms. Firms started tooutsource functions that were not consideredtheir area of expertise in order to become morecost-efficient (Porter, 1996). A number ofauthors have argued whether or notoutsourcing can be viewed as strategy. In thepublication ‘What is Strategy?’ Porter (1996)strongly protests that outsourcing is a tool andnot, in and of itself, a strategy. In contrast tothis, outsourcing is defined as a strategicdecision (Embleton and Wright, 1998;Gottschalk and Solli-Saether, 2005). McIvor(2000) goes as far as to say that outsourcingcan succeed only if carried out from a strategicperspective, and by fully integrating it with an

organization’s larger corporate and operationalstrategy. Regardless of the different opinionsin the literature, most authors agree thatoutsourcing is an important tool forimplementing rapid strategic change.

Outsourcing has further become aninternational phenomenon in order to providebusinesses with a competitive edge in a globalmarket. Several publications report on variousaspects of international outsourcing, e.g.expansion and development path (Mol et al.,2004), partnership model (Kedia and Lahiri,2007), drivers of offshore business processes(Kshetri, 2007), skills-intensive tasks andwage inequality (Anwar et al., 2013).Although international outsourcing related atsome stage mainly to the manufacturingsector, such as changing the productionlocation to obtain a capital-labour trade-off, itprogressed to service-based and knowledge-based outsourcing, such as advancedinformation technology design, legal services,medical diagnostics, etc. (Parkhe, 2007).Recent publications indicate a tendency toreturn manufacturing to home countries due torising labour costs in originally preferredcountries, such as China, India, and Mexico(Eichler, 2012; Pearce, 2014; Kazmer, 2014).

Outsourcing is prevalent in the retail andmanufacturing industries (Bryce and Useem,1998; Kazmer, 2014), whereas mining is oneof the industries with the lowest propensity foroutsourcing (Embleton and Wright, 1998). Thesupposition can be made that this is due to thefact that, historically, mining has been quite aprotected industry, unlike manufacturing andretail where the fierce competitive environmenthas forced companies to be innovative withregard to their business models. In acommodity-based business such as mining,outsourcing has become a potential solution toovercome two main challenges, namely cost

Outsourcing in the mining industry: decision-making framework and criticalsuccess factorsby C.J.H. Steenkamp* and E. van der Lingen*

SynopsisTheoretically, the main driver behind a mining operations’ sourcingdecision should differ from company to company, and within a companyfrom project to project, but in reality it often relates to cost. Researchconfirms that there are a number of factors, including cost, to considerwhen choosing between in-house and outsourced mining. While theliterature is rife with factors to consider, little information exists on how toapply these in practice and the relative importance of the different factorsto be considered.

A study was conducted to determine whether mining is truly a corecompetency for a mid-tier commodity specialist mining company.Furthermore, a decision-making framework for mining operations sourcingwas developed, and the critical success factors that should be adhered to ifoutsourced mining is chosen were determined.

The research showed that owner mining is not a core competency forthe mining company investigated. A decision-making framework wasdeveloped using the order winner/order qualifier structure, which can beused to facilitate the mining sourcing decision. The most important tools atthe disposal of a mine owner’s team to manage a contractor miner are thesocial and output control mechanisms, according to the critical successfactors study.

Keywordsinsourcing, outsourcing, contractor, mining, decision-making, criticalsuccess factors.

* Department of Engineering and TechnologyManagement, University of Pretoria, South Africa.

© The Southern African Institute of Mining andMetallurgy, 2014. ISSN 2225-6253. Paper receivedrrMay 2014; revised paper received Aug. 2014.


Outsourcing in the mining industry: decision-making framework and critical success factors

fand the acquisition and retention of skilled people (DeloitteManagement Consulting, 2012). In apparent contradictionwwith conventional outsourcing theory, which dictates thatcompanies should focus outsourcing efforts on non-corecompetencies, many mining companies have consideredoutsourcing their mining operations i.e. drilling, blasting,loading and hauling – the very core of their business (Quelinand Duhamel, 2003).

Table I shows a summary of the literature on outsourcingin general (Jiang and Qureshi, 2005). The literature isdominated by research focusing on the outsourcing processwwith the outsourcing decision, the focus of this research, andoutsourcing results lagging behind. Popular research method-ologies include surveys and conceptual framework, which willform the basis of the research methodology of this study aswwell.

While the literature is rife with long lists of factors toconsider, little to no information exists on how to apply thesein practice and the relative importance of the different factorsto be considered (Quelin and Duhamel, 2003). Theopportunity therefore exists to develop a framework fordeciding between outsourcing and insourcing of core miningoperations.

A study was conducted to determine whether mining istruly a core competency for a specific mid-tier geographiccommodity specialist mining company and evaluate thisagainst the perceptions among management. A decision-making framework for mining operations sourcing wasdeveloped. The study further set out to determine the criticalsuccess factors (CSFs) that should be adhered to ifoutsourced mining is the decision. The objectives of thisstudy are to:

➤ Determine whether mining is truly a core competencyfor a mid-tier geographical commodity specialist andevaluate this against the perceptions amongmanagement in such a company

➤ Develop a decision-making framework for miningoperations sourcing for future mining projects, whichincludes a prioritized list of factors to consider

➤ Determine the CSFs that should be adhered to ifoutsourced mining is the chosen option.

Strategic outsourcing decision factors

The context within which outsourcing decisions are made isextremely important, and making the decision on cost aloneis dangerous and short-sighted (Fill and Visser 2000). McIvor

f f f(2000) proposes a couple of factors that should form theframework within which a company should makeoutsourcing decisions, e.g. cost analysis, associated risks,supplier influences, and strategic intent. Recent research byFreytag et al. (2012) defines three major categories thatshould be used when evaluating outsourcing decisions,namely (i) cost-based, (ii) competence-based, and (iii)relationship-based. Quelin and Duhamel (2003) do not placeas much emphasis on relationship-based factors, and splitcost considerations into two categories, namely (i)operational costs and (ii) effective use of capital. They alsoadd flexibility-based factors as a separate category. Theapproaches of Freytag et al. (2012) and Quelin and Duhamel(2003) were combined for the purposes of this study, asshown schematically in Figure 1.

Operational cost-based factors

Transactional cost theory and the drive for efficiency has longbeen the dominant reason for outsourcing (Holcomb and Hitt,2007). Any organization, but particularly commodity organi-zations like mining companies, strives to minimizeproduction and transaction costs. Companies sometimesoutsource a function to convert a fixed-cost operation into avariable-cost operation (Freytag et al., 2012), therebyminimizing the risk of a negative profit margin under lowproduction volumes. Often the decision on project issues isnot made on the best fit, but more on the sensitivity of theproject business case. In order to consider cost comparisons,one must take care to evaluate outsourcing on par withinternal capabilities and their associated costs (Embleton andWright, 1998). The one area of concern, according to Kirk(2010), is the redundancy in overheads – as both the ownerand contractor will require some management and adminis-trative functions, even under a fully outsourced miningmodel.

Capital efficiency-based factors

In addition to minimizing low volume risk, companies preferto outsource functions that depend heavily on fixedinvestment in order to avoid spending large amounts ofcapital (Quelin and Duhamel, 2003). In mining operations,for example, conducting the function in-house means that the

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Table I

Number and focus of publications on outsourcing(Jiang and Qureshi, 2005)

Research Decision Outsourcing Outsourcingtype framework process results

Case study 34 54 15Survey 31 28 14Conceptual framework 24 19 8Mathematical modelling 7 13 11Financial data analysis 1 0 4Total 97 114 52[%] 36.9% 43.3% 19.8%

97 11436.9% 43.3%

Figure 1—Outsourcing decision factors to consider

company needs to invest capital at start-up in order toacquire a mining fleet (trucks, excavators, dozers, etc.) andthen periodically replace these assets as they age, whichrequires additional capital. A mining contractor will modelthis, and build the capital requirement into their variable rate;thereby smoothing cash flow and converting capital spentinto a variable operational expenditure. The work by Kirk(2010) substantiates this further, saying that for miningcompanies, the main consideration from a corporateperspective is the availability, accessibility, and cost ofcapital. The key difference between owner and contractormining rests in the former being heavily capital-intensiveinitially, but potentially lower cost in terms of operationalexpenditure over the life-of-mine.

FFlexibility-based factors

Mining is an industry with a number of variable influences,from geology and labour conditions to the seasons andcommodity prices. Flexibility-based factors historically comesecond only to cost when outsourced mining is motivated,wwith various aspects to be analysed. Each mining project isunique, and presents its own unique complexities andchallenges. Kirk (2010) suggests that projects with arelatively short life-of-mine (five years or less) and withwwidely varying mining rates will be suitable candidates foroutsourced mining. Holcomb and Hitt (2007) indicated thatflexibility-based factors become a higher priority in organi-zations operating in a market where technology is the basisof competitive advantage, and with significant technologicaluncertainty. In the mining industry, the tendency is to movefrom labour-dependent, largely manual technologies toautomated methods, resulting in mining contractorspartnering with equipment suppliers to enable them to accessnew technologies.

Competence-based factors

From a competence-based perspective, various factors need tobe considered. A company should look for opportunities to (i)protect and develop its core competencies internally, even at aslightly higher transactional cost, and (ii) look to balance andsharpen its competitive edge by outsourcing non-corecompetencies to best-in-class service providers (Freytag etal., 2012). By accessing more efficient and potentially morevvalue-creating capabilities a firm can fundamentally changeits competitiveness in the marketplace. Holcomb and Hitt(2007) support the contention that firms should formalignments with outsourcing partners in order to gain accessto complementary capabilities or competencies.Complementary competencies are defined as those that notonly supplement a company’s internal core competencies, buthave the ability to enhance them as well. During times ofindustry-wide shortages of skills, outsourcing can furtheralleviate the need to invest in expensive individuals, (Kirk,2010). Mining is one of the industries where the attractionand retention of talent is one of the main operationalchallenges.

RRelationship-based factors

Outsourcing decisions are not made on cognitive reasonsalone (Webb and Laborde, 2005). No company is truly a sole

entity, and this is considered under the relationship-basedgrouping of factors. Outsourcing, if applied correctly, cancreate bonds and network an organization in such a way asto increase productivity (Freytag et al., 2012). The strategicrelationship between client and vendor becomes quiteimportant. Holcomb and Hitt (2007) indicate that goalcongruence, or the degree of overlap between the two parties’strategic and operational objectives, must be considered. Inorder for a win-win relationship to exist and be sustained,objectives must be aligned (or more specifically, alignable).Managing capabilities, even if not under the directoperational control of the managing party, is in itself also acapability (Loasby, 1998). A company consideringoutsourcing a function should evaluate its own ability (as acore competency) to manage such an arrangement.

Critical success factors for contractor mining

The benefits of contractor mining are not always achieved,even when the model suits the project perfectly. Dunn (1998)provides examples where, after substantial periods under thecontractor mining model, companies have concluded thatowner mining is significantly cheaper, has marginally lowerrisk, and in general is better value for money. In a surveyconducted by Deloitte Consulting (2012) 48% of respondentshave at some stage terminated an outsourcing contract, andin a third of these cases ended up insourcing the function. Itis thus believed that the benefits of contractor mining are nota given, but are heavily dependent on choosing the rightcontractor, setting the appropriate incentives throughcontracting, and implementing the business solutioncorrectly.

According to Kang et al. (2012), the appropriate controlmechanisms should be in place in order to ensure therealization of outsourcing benefits. They group these intothree categories, namely (i) process control mechanisms, (ii)output control mechanisms, and (iii) social controlmechanisms. However, the foundation of a successfuloutsourcing arrangement starts with the development of acomprehensive and intelligent contract (Webb and Laborde,2005). In this study, contract-based factors will be added asan amendment to the Kang et al. (2012) mechanisms(Figure 2).



Figure 2—Critical success factor categories for contractor mining


Contract-based mechanisms

According to Kirk (2010), contracts between owners andmining contractors have become more detailed. This is aresult of both parties acquiring additional experience, and ina number of occasions learning some expensive lessons inhow to manage a win-win contractor mining relationship.CSFs under this category (Webb and Laborde, 2005) include:

➤ A fair and mutually beneficial contract ➤ Adequate incentive schemes, both gains and penalties –

and how these will be shared between owner andservice provider

➤ Flexibility in the contract to allow for changes to scopeand conditions.

There is, however, still widespread disagreement on thebest practice in structuring these contracts, with some ownersstill employing the traditional schedule-of-rates contract andothers moving towards a more cost-plus-profit arrangement,and in some extreme cases even offering equity in the miningowner company. In addition to this, various incentive andpenalty schemes have also been employed, with varyingsuccess.

PProcess control mechanismsProcess control mechanisms focus on the vendor’s method,i.e. the process through which the service provider delivers(Kang et al., 2012). CSFs under this category include:

➤ Standard operating procedures➤ Formalization of roles and responsibilities on all

positions➤ Training on the outsourcer’s processes, procedures,

methodologies, and policies➤ Extensive reporting from service provider to owner on

standards and performance➤ Support (formalized within the employee performance

contract) of internal functional employees – withoutwhich no outsourcing arrangement can thrive (Webband Laborde, 2005). Not having this in place oftenleads to employees of the outsourcer feeling threatened.

Output control mechanisms Output control mechanisms are focused on the goals andobjectives of the outsourced process (Kang et al., 2012). CSFsunder this category include:

➤ The establishment of goals and objectives. It isimportant to focus on measurable objectives to ensureclarity of expectations (Webb and Laborde, 2005)

➤ Recovery plans when outcomes are at risk➤ Regular reviews of contractor performance – including a

detailed reporting schedule – on all required levels➤ A strong group of contract management specialists to

manage deviations from the contract (Embleton andWright, 1998).

Social control mechanisms

Social control is most prevalent in outsourcing arrangementsdriven by the need to innovate (Kang et al., 2012). CSFsunder this category include:

➤ Shared values and beliefs, driven by a mutual respectand cultural fit between the contractor and owner. The

f fservice provider should fit into the culture of theoutsourcer (Webb and Laborde, 2005)

➤ Vendor relationship management is often stated to bethe single most important factor to any outsourcingarrangement (Parsa and Lankford, 1999). Thelongevity and success of an outsourcing model isdependent on the success or failure of the client/vendorrelationship (Webb and Laborde, 2005)

➤ Strong communication channels, both formal andinformal. Clients appreciate when service providerscommunicate with them in a proactive manner, andvice versa (Webb and Laborde, 2005)

➤ Senior management support and continuousinvolvement.

Quelin and Duhamel (2003) state that frequently citedoutsourcing benefits cannot be divorced from the efforts andinvestments required to continuously monitor and control theservice, and as such all four control mechanisms discussedabove will be considered as part of this research.

Research propositions and hypotheses

Mining tested as core competency

The first proposition evaluates mining as a core competencyusing the criteria of Quinn and Hilmer (1994). They use thefollowing dimensions to evaluate whether a function is core:

(i) Skill or knowledge sets, not products or functions:Based more on intellectual property and knowledgethan on physical assets

(ii) Flexible long-term platforms capable of adaptationor evolution: This competency evolves over time,and creates flexibility, rather than inhibiting it

(iii) Limited in number: Most successful organizationstarget only two or three core competencies. Formining companies it has been suggested that onlytwo core competencies are required for success –financing and management (Hamel and Prahalad,1996)

(iv) Unique sources of leverage in the value chain: Corecompetencies often fill gaps in the industry wheresevere knowledge deficiencies exist, for which thecompany has been specifically positioned throughinvestment

(v) Areas where the company can dominate: Typicallythe areas where an organization can significantlyoutperform its peers. According to Stacey et al.(1999), core competencies can be identified byasking the following questions: What does theorganization do better than anyone else? What doesthe organization do so well that it will be able to sellthis as a service to other companies? Where does acompany achieve best-in-class status?

(vi) Elements important to customers in the long run:Companies should ask themselves if their customersand shareholders care that they perform thisfunction

(vii) Embedded in the organization’s systems: Corecompetencies are not determined by a couple ofparticularly talented employees, but rather bysystems and practices that are standard and thatsurpass the employment history of these talentedemployees.

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H1H : The tested reality will show mining operations as anon-core competency for the company underinvestigation

H2HH : There will be a difference in perception and testedreality – miners will believe that mining is a corecompetency.

Choosing between owner and contractor miningThe second research proposition relates to the development ofa decision-making framework, using the outcomes of anorder-winner/order-qualifier analysis. This framework showsunder which conditions owner mining has the upper hand inthe mining sourcing decision, and under which scenarios itwwill not. Generic decision frameworks can be misleading, asthe reason behind an outsourcing decision will very muchdepend on what an organization is trying to achieve. Thefollowing hypotheses are made with regard to the miningsourcing decision:

H3HH :33 Operational cost-based factors will emerge as orderqualifiers rather than order winners for a specificmodel (insourced or outsourced mining)

H4HH : Capital efficiency-based factors will be an orderwinner for contractor mining

H5HH :55 Flexibility-based factors will be an order winner forcontractor mining

H6HH : Competence-based factors will emerge as orderqualifiers rather than order winners for a specificmodel (insourced or outsourced mining)

H7H :77 Relationship-based factors will be an order winnerfor owner mining.

Critical success factors for contractor mining The third research proposition is to determine and prioritizethe four CSFs in the event that outsourced mining is thechosen alternative. The top-priority CSFs should be evaluatedagainst a mining company’s internal capabilities to ensurethat if contractor mining is chosen as the preferred scenario,the internal workings of the firm support the success of thisstrategy.

H8HH :88 Contract-based mechanisms will be high-prioritycritical success factors

H9HH : Social mechanisms will emerge as high-prioritycritical success factors

H10H :00 Output mechanisms will be prioritized relative toprocess control mechanisms.

Research methodology

All research techniques have inherent inadequacies, and assuch are best applied in conjunction with other techniques inorder to counter these shortcomings, also known as triangu-lation. The methods chosen for this research includesfindings from literature, the sample survey, and judgementtask or nominal group (Barry et al., 2009) methods.

The objectives for the judgement task as part of thisstudy were:

➤ Discuss and group the various factors (potential orderwinners and order qualifiers) that should be consideredby a mine owner when choosing between owner andcontractor mining

➤ Discuss and group the various CSFs that should be inplace in order to ensure that contractor mining achievesits theoretical benefits.

The findings from the literature, as well as the outcomesof the judgement task conducted, were consolidated in asurvey, structured as a combination of a questionnaire and astructured interview. The questionnaire consisted of foursections, namely one for the respondent’s details and one oneach of the three research propositions. This researchinstrument was then used in gathering data from 80% of therelevant heads of departments and general management of amid-tier geographic commodity specialist mining company.The sample was stratified on three dimensions of the organi-zation to ensure a complete data-set is gathered:

➤ Mining type—managers from both underground andeeopen cast operations

➤ Departments—respondents from various departments,ssincluding mining, beneficiation, finance, andengineering

➤ Seniority—heads of departments (middle management)yyand general management.

ResultsRespondents included managers from both underground(28%) and opencast (72%) operations. This is also represen-tative of the ratio of the number of operations of this mid-tiermining group. A good balance between the differentfunctional backgrounds was accomplished and the percentagefeedback from the different departments consists of mining(22%), finance (22%), plant (17%), engineering (6%), andgeneral management (33%). The ratio of senior management



Figure 3—Mining tested as a core competency using the Quinn and Hilmer model


fto heads of departments was 11:14. It is believed that thestratified sample adheres to the requirements of the researchmethodology and can thus be considered as a fair represen-tation of the population of the whole mid-tier miningcompany. However, care should be taken not to assume thevvalidity of these results for all mining companies. The resultsof the investigation are discussed along the lines of the threeresearch propositions.

MMining tested as core competency

Figure 3 shows a consolidated summary of the findings ofresearch proposition 1; testing mining as a core competency.The majority (72%) of respondents indicated that theybelieve that owner-operated mining is a core competency forthe mid-tier geographic commodity specialist miningcompany. This number was then used as the cut-off point forthe testing of this fact through the Quinn and Hilmer model.

It can be seen that on all seven dimensions of the Quinnand Hilmer model, the response frequency is biased towardsowner-operated mining as a non-core competency (asmeasured against the 72% cut-off). The average of theresponses on the 7 dimensions is 35% in favour of corecompetency, i.e. 65% in support of the fact that mining is anon-core competency – a clear conflict with the managementteams’ articulated perceptions.

By way of example, Quinn and Hilmer state that acompany’s core competency must be important to thecustomers in the long run; in other words, companies shouldask themselves if their customers and shareholders care thatthey perform this function. Only 11% of respondentsindicated that this is the case for owner-operated mining (see dimension titled ‘Customer Impact (i) Yes or (ii) No’ inFigure 3), and as such this question presents strong evidencethat mining is a non-core competency.

H1H and H2HH are discussed in the light of the researchfindings on proposition 1, summarized in Figure 3.

H1H : The tested reality will show H1H is acceptedmining operations as a non-core competency for a mid-tier mining company.

Not a single dimension from the Quinn and Hilmer modeltested above the 72% cut-off level. In fact, five of the sevendimensions did not even receive a 50% response rate infavour of mining as a core competency. This supports whatwwas found in the literature, in particular the work of Hamel

f fand Prahalad (1996) that signifies that often businessfunctions (such as management, financing, and resourceacquisition) rather than technical functions (such as mining,geology, surveying, etc.) are core competencies of miningcorporates.

The literature showed that not all non-core competenciesshould be outsourced, but rather evaluated for outsourcing(Quinn and Hilmer, 1994; Leavy, 2004; Stacey, 1999). This,together with the fact that Hypothesis 1 is true, rendersresearch proposition 2 relevant, i.e. mining is proved to be anon-core competency, and it should be evaluated as acandidate for outsourcing, necessitating the development of amake-or-buy decision framework.

H2HH : There will be a difference in H2HH is acceptedperception and tested reality – miners will believe that mining is a core competency.

There is a clear conflict between the respondents’articulated opinion of owner-operated mining as a corecompetency (72% of respondents) and the evidence testedagainst the Quinn and Hilmer model (35% over the sevendimensions). This is to be expected, as miners will believethat the owner’s ability to operate a mining operation shouldbe the core competency of a mining company. However, theprevalence of so many mining companies already employinga model of full or partial outsourced mining clearly provesthat this is not always the case. The research conducted onproposition 1 in this study supports this fact conclusively.

Choosing between owner and contractor mining

Figure 4 shows a consolidated summary of the finding ofresearch proposition 2; choosing between owner andcontractor mining. The data has been ranked from factorsmost strongly supporting contractor mining (order winnersfor contractor mining) to factors most strongly supportingowner mining (order winners for owner mining). Theproportion of respondents that listed the factor in question asindecisive towards a particular model (titled ‘It Depends’ onthe questionnaire) is also indicated under the category‘Unsure (Order Qualifier)’. Figure 5 shows the same data asin Figure 4, but grouped according to decision factor category(Figure 1), as developed by combining the work of Freytag etal. (2012) and Quelin and Duhamel (2003).

A decision-making framework was developed using theorder winner / order qualifier structure. Table II summarizes

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Figure 4—Proportion of respondents classifying the decision factors as order winners for contractor and owner mining

fthe findings on research proposition 2 into a decision model.Care should be taken in the extrapolation of these findings toa different time and/or context, as order qualifiers and orderwwinners are highly dependent on market context, and willchange over time.

Hypotheses 3 to 7 are now discussed in the light of theresearch findings on proposition 2, as summarized in Figures4 and 5 and Table II.

H3HH :33 Operational cost-based factors H3HH is rejectedwill emerge as order qualifiers rather than order winners for a specific model (insourced or outsourced mining).

Elements of the hypothesis were proved true, but therewwas not sufficient evidence from the data to determine ifoperational cost-based factors will in all instances be an orderqualifier. With regard to the ‘Unit Cost’ factor, the hypothesisttis supported to be true, i.e. the response rates are not biasedtowards a particular mining sourcing model so as to indicatethe factor as an order winner for that model. However, withregard to the ‘Optimal Fixed/Variable Cost Ratio’, contractormining was shown to have this factor as an order winner.The finding on ‘Optimal Fixed/Variable Cost Ratio’ as anorder winner for contractor mining can be explained asfollows. For small to medium-sized mines the ‘OptimalFFixed/Variable Cost Ratio’ leans towards more variable costs,as it is difficult to dilute fixed cost on a low-volumeoperation. Clearly, contractor mining has a higher proportion

f fof variable cost, and this benefits small/medium sizedoperations in this regard. However, large to mega-sizedmining operations can effectively dilute fixed cost, which willlead to higher profit margins. Under these circumstances, the‘Optimal Fixed/Variable Cost Ratio’ leans to having morefixed costs, with owner mining thus a better option.

All the respondents came from mines that can beclassified as small/medium sized operations (producing 4 Mtor less per annum, with a LOM less than 5 years), whichwould be the reason why these respondents listed ‘OptimalFixed/Variable Cost Ratio’ as an order winner for contractormining. Keeping this in mind, ‘Unit Cost’ is included as anttorder qualifier in the decision-making model. ‘OptimalFixed/Variable Cost Ratio’ is included as an order winner forcontractor mining, specifically in the context within whichthis study was conducted.

H4HH :44 Capital efficiency-based factors H4HH is rejectediiwill be an order winner for contractor mining.

The data shows a very balanced result, with a largeproportion of respondents listing these factors as orderqualifiers, and the rest split between order winners in favourof the two mining sourcing models. This finding can besubstantiated as follows: contractor mining requires the mineowner to spend less capital, because the contractor will buildthe capital requirements into its rates, thereby effectivelyconverting capital expenditure into operational expenditure.Over the life of the mining project, this will not necessarily



Table II

Mining insourcing versus outsourcing decision model

Category from the lliterature Decision making factors Owner mining winner Qualifier Contractor mining winner

Operational cost • Composite unit cost of production X• Lowest fixed cost / variable cost ratio X

Capital efficiency • Managing potential variability in cash flow X• Enabling efficient allocation of capital investment X

Flexibility • Adapting with variability in mining rate X• Adapting to rapid change in mining technology X

Competence • Industry-wide shortage of mining skills X• Value chain integration X

Relationships • Industrial relations and union / community X• Alignment to changes in larger corporate strategy X

Figure 5—Decision factors grouped according to category


fhave any impact on the net present value (NPV) of theproject, unless there is a substantial difference in the costsstructure of the contractor, compared to that of the mineowner.

Furthermore, the decrease in capital expenditure does notguarantee that an alternative investment exists with a betterbusiness case, which would lead to an increase in capitalefficiency. When taking a portfolio view, spending capital onan owned fleet might be a better investment than spending iton a different project in the mine owner’s portfolio.

It is therefore deduced that capital efficiency-basedfactors are order qualifiers in the mining insourcing versusoutsourcing decision. This finding contradicts the work byQuelin and Duhamel (2003) and Kirk (2010), both of whomadvocate the decrease in capital expenditure as a strongargument, i.e. order winner, for outsourcing.

H5HH :55 Flexibility-based factors will be H5HH is rejectedan order winner for contractor mining.

H5HH could not conclusively be proved, i.e. there was notsufficient evidence from the data to determine if flexibility-based cost factors will in all instances be an order winner infavour of contractor mining. With regard to the ‘VariableMMining Rate’ factor, the hypothesis is supported to be true,i.e. the results show that contractor mining acquired a highfrequency of positive responses – enough to classify thefactor as an order winner for that model. This was to beexpected, and is supported by Kirk (2010), who listedvvariability in mining rate required as a key argument infavour of contractor mining. However, with regard to ‘MiningTechnology Change’, contractor mining has a slightadvantage over owner mining according to the researchrespondents, but not by a large enough margin to prove thatmodel has a clear advantage in absolute terms.

Changing technology will in the future become progres-sively more important in the mining industry, whichaccording to Holcomb and Hitt (2007) will increase theimportance of this decision factor. Currently, however,technology in the mining industry is fairly standard, due tothe industry being highly averse to change. Under theseconditions, it is to be expected that such a factor will be anorder qualifier (Hill, 2000), as was found in this study. It isconcluded that ‘Variable Mining Rate’ is an order winner forcontractor mining, and that ‘Mining Technology Change’ is anorder qualifier.

H6HH : Competence-based factors will H6HH is rejectedemerge as order qualifiers rather than order winners for a specific model (insourced or outsourced mining).

H6HH was proved partly true. The mining industry facesextreme difficulties with regard to attracting and retainingtalent. Mine owners and contractors experience this difficultymore or less to the same degree; they compete in the samelabour market and offer similar employee value propositions.It is thus no surprise that the respondents were divided intheir opinion on the ‘Shortage of Skills’ factor, with 33%listing this factor as an outright order qualifier. This findingis in contradiction with the work by Kirk (2010), whoadvocates contractor mining as a mitigation action that mine

fowners can take to overcome the industry-wide shortage ofskills. It is believed that the reason for this lies in thedifference in context within which the work was done –Kirk’s study was conducted on the contractor mining industryin Australia, which has a much more mature professionalservices sector than South Africa.

Owner mining did, however, emerge as having anadvantage in terms of ‘Value Chain Integration’. Therespondents believed that the integration of a miningcontractor into the larger resources extraction value chain willnot happen as smoothly as in the case of owner mining.Reasons cited for this include differences in incentivesbetween mine owner and contractor, as well as sub-optimalcommunication channels. These will again be discussed asCSFs (under research proposition 3). It is concluded that‘Value Chain Integration’ is an order winner for ownermining, and that ‘Shortage of Skills’ is an order qualifier.

H7H :77 Relationship-based factors will beHee 7H is acceptedan order winner for owner mining.

A large majority of respondents believe that an owner-operated mining operation is better equipped to manage itsrelationship with the community in which it operates, as wellas its relationship with the corporate to which it reports. Fromthis finding, as well as the focus group that was conducted asverification for the research instrument, it is concluded thatthe importance of the relationship-based factors cannot beoverstated, especially in the South African context. It is,however, a topic that is often overlooked, as was done byQuelin and Duhamel (2003), who focused mostly onoperational cost- and capital efficiency-based factors. It isconcluded that ‘Union/ Community Considerations’, as well as‘Alignment with Corporate Objectives/Strategy‘ ’ are orderwinners for owner mining.

Critical success factors for contractor mining

Figure 6 shows the consolidated findings on researchproposition 3 – CSFs for contractor mining. The differentmechanisms behind the CSFs (see Figure 2) are indicatedusing a colour scheme.

Figure 6 has been divided into four different regions:▲


Figure 6—Critical success factors for contractor mining grouped bymechanism

➤ Priority 1— fCSFs with high benefits requiring littleeffort to implement. These should be the focus of amanagement team entering a contractor miningarrangement

➤ Priority 2 (two regions)—CSFs with high benefits butrequiring a high-level effort to implement, as well asCSFs with low benefits but requiring a low-level effortto implement. These should be a secondary focus of amanagement team entering a contractor miningarrangement

➤ Priority 3—CSFs with low benefits requiring a highlevel of effort to implement. These can be deprioritizedfor implementation purposes.

It was found that the most important tools at the disposalof a mine owner’s team to manage a contractor miner are thesocial and output control mechanisms. Firstly, a mine ownermust understand the objectives and strategic intent behindoutsourcing the mining operation. If the objectives of such anoperational strategy are not understood, it is likely that theywwill not be achieved. It is believed that this is currently notthe case in the mid-tier geographic commodity specialistmining company, as indicated by the conflict found regardingresearch proposition 1. Secondly, communication betweenowner and contractor, as well as ongoing senior managementinvolvement, should be a priority. The mine owner shouldfocus on the output of the contractor, and leave the processby which that output is produced to the contractor to manage.

H8HH :88 Contract-based mechanisms will H8HH is rejectedbe high-priority critical success factors.

It was found that two contract-based mechanisms plottedin the ‘Priority 3’ category, and the remaining one in ‘Priority22’. This is an extremely interesting and counter-intuitivefinding. From follow-up discussions with some of therespondents, the following justification is put forward insupport of this finding: The respondents believe thatcontracts with contractor miners have become standardizedto such an extent that it has become very difficult tonegotiate any contract that falls outside this standard. Forthis reason, the level of effort required is deemed to be sohigh relative to the other CSFs that this mechanism isdeprioritized. This does not mean that the contract betweenmine owner and mining contractor is not important, in factthe ‘Fair and Mutually Beneficial Contract’ critical successttfactor ranked second on the benefits ranking scale. It does,however, show that the industry has cemented the legalaspects of an outsourced arrangement to such an extent thatit has become unfeasible to negotiate customized incentives,penalties, and flexibility into contracts.

H9: Social mechanisms will emerge H9HH is acceptedas high-priority critical success factors.

Two of the CSFs under this mechanism plotted under‘Priority 1’. One can understand that mining contractorsperform better under a spirit of partnership betweencontractor and client. Communication between owner andcontractor on all levels (including senior management) isparamount to a successful relationship. The one CSF thatstands out here is ‘Shared Values and Beliefs’, which isplotted as ‘Priority 3’. From follow-up discussions with

f fresearch respondents, the following justification is presentedfor this anomaly.

The ‘Shared Values and Beliefs’ CSF is believed to be thesingle most difficult CSF to put in context, both because acultural match is difficult to assess during the tender process,and because values and beliefs are virtually impossible tomanipulate once the contractor has been chosen. This CSF istherefore deprioritized; not because it is not important, butrather because it is extremely difficult to influence.

H10H :00 Output mechanisms will be H10H is acceptedprioritized relative to process control mechanisms.

Output-based mechanisms had two CSFs under ‘Priority1’ and the third under ‘Priority 2’, while the CSFs pertainingto process control mechanisms are all plotted under ‘Priority2’. One of the benefits most frequently cited for outsourcing(Harland et al., 2005; Kedia and Lahiri, 2007) is that itenables organizations to focus on core competencies, i.e. tosharpen their strategic focus. Mine owners therefore does notwish to micro-manage the contractor, i.e. control how theyconduct their business, as long as they deliver results to thelevel that was agreed.

It was expected that ‘Clear Goals and Objectives’ wouldfeature high on the priority list. That is exactly whathappened, with almost 80% of respondents listing this CSFunder their top four with regard to potential benefits. Thisechoes with findings from the literature, with Embleton andWright (1998), Gottschalk and Solli-Saether (2005), andMcIvor (2000) all emphazing that the outsourcingarrangement is likely to fail if the party outsourcing thefunction does not understand what it aims to achieve throughsuch an action.

Conclusions and recommendations

A decision-making framework was developed to facilitate thedecision between owner and contractor mining. Thiscontributes in a number of ways.

Firstly, most of the literature deals with outsourcing inthe manufacturing, services, or retail environment – thisstudy provides some theory and application relevant to themining industry.

Secondly, most of the literature lists factors to beconsidered in the decision-making process without detailinghow these factors should be assessed or prioritized. Byapplying the order winner/order qualifier framework, a modelis developed that can be used to structure the make-or-buydecision specifically for mining.

Finally, although numerous studies list critical successfactor (CSFs) for outsourcing, none could be found thatprioritize these to assist management in the development of afocused implementation roadmap. The CSFs for theoutsourced mining application were prioritized using abenefit/effort matrix that highlighted the critical elementsthat a mine owner management team should focus on tomitigate the risks of outsourcing a mining operation, therebyincreasing the probability of success.

It is recommended that the decision-making modeldeveloped under research proposition 2 (the mining sourcingdecision) be used to facilitate the make-or-buy decisionprocess for mining operations. It is believed that this will




result in a more structured approach and a holistic viewcompared to making the decision on a cost analysis only.This will result in a higher probability of making the correctdecision, especially on greenfield mining projects.

The opportunity exists to expand the study to a widerpopulation. This could include other mining companies,commodities, geographical contexts, and scales of operation.Also, since this study focused on the mining operation only.and excluded other core and peripheral activities in thecommodity value chain such as beneficiation and logistics,the opportunity exists to conduct a similar study to developmake-or-buy decision models for these activities.

AAcknowledgement

The authors would like to thank the Graduate School ofTechnology Management, University of Pretoria, for theopportunity to publish the results.

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Introduction

Almost all continental earthquakes areconfined to a crustal layer that varies inthickness between 10 km and 40 km,measured from the surface. Hence, continentalearthquakes do not occur in the mantle (Maggiet al., 2000). In many stable continentalregions, focal depths occur as a bimodal distri-bution within the upper third (0 km to 10 km)and/or lower third of the crust (20 km to35 km), while the middle crust (10 km to20 km) tends to be aseismic (Klose andSeeber, 2007). The distribution can vary interms of bimodal depth and strength, withsome stable continental regions showing verywwell-developed bimodal distributions (e.g. the

fNorth Alpine foreland basin in Europe), whileothers show only weak or no bimodal distri-bution (e.g. New Madrid seismic zone in thecentral USA). Klose and Seeber’s study (2007)found that many large earthquakes withmagnitudes MwMM from 4.5 to 8.0 have a focaldepth of less than 5 km, with almost 80% ofthe seismic moment density of shallow stablecontinental region ruptures being released inthe uppermost 7 km of the crust. It isnoteworthy that accurate focal depths haveimportant implications for seismic hazardestimations, specifically in respect of groundmotion predictions.

In a previous case study in North America,Ma and Atkinson (2006) found only weaksupport for a bimodal distribution. Forsouthern Ontario, Canada, and northern NewYork, USA, focal depths range from 2 km to15 km. In parts of western Quebec and alongthe Ottawa River Valley in Ontario, focaldepths range from 2 km to 25 km. Theseauthors noted that more than half of theearthquakes, in a cluster of activity nearManiwaki in western Quebec, Canada, aredeeper than 20 km. To the author’sknowledge, only one such study has beenperformed for tectonic earthquakes in SouthAfrica: Mangangolo et al. (2014) appliedsynthetic-to-recorded-waveform fits to datarecorded by the stations of the temporaryKaapvaal Craton array. The 50 broadbandstations of this array were installed on a 1° ×1° grid at 80 sites in central southern Africa(c.f. Nguuri et al., 2001). The study foundshallow hypocentres ranging between depthsof 5.6 km and 18.6 km, with an error in depthof approximately 3 km.

Focal depth is the most difficult parameterto determine for regional earthquakes andmine-related events when recorded by means

Focal depths of South African earthquakesand mine eventsby M.B.C. Brandt*

SynopsisFocal depths of 15 tectonic earthquakes and 9 mine-related events weredetermined for South Africa using data recorded by the South AfricanNational Seismograph Network. These earthquakes and events were re-located by means of the Hypocenter program using direct P-waves (P(( gP ) ,ggcritically refracted P-waves (P(( nPP ) , and first-arrival S-waves for themagnitude range 3.6 MLMM 4.4. Focal depths were first determined bymeans of the minimum root mean square (RMS) of the differences betweenthe measured travel times and those predicted using the velocity model.The depths for tectonic earthquakes had a 2 km D 10 km range and anaverage depth and standard deviation of 6.9 ± 2.3 km. Depths for mine-related events ranged over 0 km D 7 km, averaging 3 ± 2.3 km. Next,arrival times for the additional regional depth phases sPnPP , PmP,PP sPmP,PPand SmP were measured. Focal depths were re-determined for the re-located epicentres, with the minimum variance (i.e. spread) of thedifferences between the measured travel times and travel times predictedby means of the Wentzel, Kramer, Brillouin, and Jeffreys (WKBJ) methodfor synthetic waveform modelling. Depth ranges were 4 km D 7 km(average 5.9 ± 1.2 km) and 1 km D 4 km (average 2.4 ± 1.2 km) fortectonic and mine-related events, respectively. The derived depths wereverified for one tectonic earthquake with synthetic-to-recorded-waveformfits using the WKBJ synthetic seismogram software for the abovemen-tioned regional phases. The focal mechanism parameters for thisearthquake source were obtained from the National EarthquakeInformation Centre. Focal depths were estimated for nine stations byvisually comparing synthetic waveform phases with recorded waveforms,ranging from 5 km to 8 km.

Keywordsfocal depth, earthquake location, regional depth phases, waveformmodelling

* Council for Geoscience, Seismology Unit, Pretoria,South Africa.

© The Southern African Institute of Mining andMetallurgy, 2014. ISSN 2225-6253. Paper receivedrrApr. 2014; revised paper received Jun. 2014.


Focal depths of South African earthquakes and mine events

fof a sparse national seismograph network, unless there arestations very close to the epicentre (e.g. Havskov andOttemöller, 2010b). This difficulty is attributable to the largeepicentral distances (approximately 100 km to 1 000 km)versus the small focal depths (approximately 2 km to 10 km),causing a near-zero change in the observed travel times ofdirect seismic waves for the different focal depths. Thepractice of the South African National Seismograph Networkis to measure only the first-arrival seismic phases. Whenlocating the events and earthquakes, focal depths are fixed asfollows: explosions to 0 km, mine-related events to 2 km, andtectonic earthquakes to either 5 km or 10 km, depending onwwhich of these depths gives the best fit to the travel time data(Saunders et al., 2008). However, by following this procedureit may be surmised that both mine-related events and tectonicearthquakes are shallow and that South African seismicitydoes not follow the bimodal depth distribution observed inother stable continental regions.

The purpose of this study was to evaluate South Africanhypocentre depths. Figure 1a summarizes the epicentres andrecording stations of the data-set. Focal depths wereestimated by stations (triangles with station codes) thatrecorded waves originating from 15 tectonic earthquakes and9 mine-related events (stars) with a magnitude range 3.6MMLMM 4.4. Mine-related events were identified from theirlocation within a gold mining area. The focal depth obtainedby means of seismic phases that had travelled along raypaths (dashed lines) originating from the mine-related eventin the Klerksdorp gold mining area, shown in Figures 2a and2b, was determined by stations situated at Parys (PRYS),Kloof Mine (KLOOF), Koster (KSR), Schweizer-Reneke(SWZ), Silverton, Tshwane (SLR), Boshof (BOSA), Lobatse,Botswana (LBTB), Kokstad (KSD), Pongola (POGA),Upington (UPI), Mussina (MSNA), Somerset East (SOE),Calvinia (CVNA), and Ceres (CER). The focal depths obtainedusing waveform modelling for waves that had travelled alongray paths (dashed lines) originating from the tectonicearthquake near Augrabies, shown in Figures 7a and 7b,wwere determined using stations situated at Komaggas

(KOMG) and Calvinia (CVNA). Tectonic epicentres locatednear Leeu-Gamka, Pofadder, and Augrabies; mine-relatedevents located in the Free State (FS), Klerksdorp (KLE), andFar West Rand (FWR) gold mining areas. Details of theanalyses performed in Figures 2a, 2b, 7a, and 7b follow.

To estimate focal depths of earthquakes and mine-relatedevents, this study applies arrival times for various waves.These include:

➤ Direct P-waves (PgPP )gg➤ Critically refracted P-waves (P(( nPP )➤ The ascending S-wave converted at the surface to a

critically refracted P-wave (sPss nPP )➤ Reflected P-wave at the Moho discontinuity (PmP(( )PP➤ Ascending S-wave converted at the surface to a

reflected P-wave at the Moho discontinuity (sPmP)PP➤ A descending S-wave converted to a P-wave when

reflected at the Moho discontinuity (SmP).PP

(Figure 1b, with velocity structure in Table I). The authorused the SEISAN earthquake analysis software (Havskov andOttemöller, 2010a) to first pick the arrival times of direct P-waves, critically refracted P-waves, and first-arrival S-waves.The events were re-located using the Hypocenter software(Lienert et al., 1986). Focal depths were determined bymeans of the root mean square (RMS) of the differencesbetween the measured travel times and those predicted usingthe velocity model. Next, arrival times for additional regionaldepth phases sPnPP , PmP,PP sPmP, andPP SmP were measured.Focal depths were re-determined for the re-located epicentres,with the minimum variance (i.e. spread) of the differencesbetween the measured travel times and travel times predictedby means of the synthetic seismogram software introducedby Chapman (1978), which uses the Wentzel, Kramer,Brillouin, and Jeffreys (WKBJ) method. The focal mechanismparameters for one tectonic earthquake source had alreadybeen determined by the National Earthquake InformationCentre. Hence, the derived depths for this earthquake wereverified by means of synthetic-to-recorded-waveform fitsusing the WKBJ synthetic seismogram software for theabovementioned regional phases.

▲


Figure 1a—Map of epicentres and recording stations of the data-set. For details of map symbols, see text in the introduction

Method

RRe-location

Earthquake location is defined by the hypocentre (x0xx , y0, z0zz ),wwith x0xx the longitude, y0 the latitude, and z0zz the focal depthand origin time t0tt (c.f. Havskov and Ottemöller, 2010b). Tocalculate the location, the Hypocenter software applies aniterative method where the location problem is linearized(Lienert et al., 1986). The method is optimized for the bestgeneral epicentre, depth, and origin time accuracy. First, aguess is made in respect of the hypocentre (x, y, z) andzorigin time, t. If this guess is close enough to the truetthypocentre, the travel time residuals at the trial hypocentreare a linear function of the correction required in respect ofhypocentral distance. Successive iterations converge to thelocal minimum provided that the problem is well conditioned.The simplest case for earthquake location is a homogeneousmedium with direct waves, where the calculated travel times,titt

tra, at the ith station (ii xixx , yiyy , zizz ) are (e.g. Havskov andOttemöller, 2010b):

[1]

where v fis the medium velocity. Partial derivatives for x, y,and z can be estimated from Equation [1] to make thecorrections needed during the iterations. If the stations are atthe surface (zi = 0), the derivative for depth is:

[2]

Note that for the sparse national network, z << x-xi andy-yi; hence changes in focal depth lead to a near-zerocorrection in the travel times.

At distances of more than about 90 km, where both directP-wave, PgPP and/or the critically refracted wave, PnPP arrive at astation, the location algorithm has some sensitivity to depthowing to the steeply descending PnPP rays, although clear PnPPphases can usually be identified only at distances of morethan about 130 km (Figure 2a). The author re-measured thePgPP phase arrival times, added the PnPP phase arrival times, andre-located all the earthquakes and events using theHypocenter software. The crustal velocity model (Table I) wasused throughout this study. This model is a simplifiedversion of the structure determined by Wright et al. (2002),who derived a high-quality multi-layer model using thetemporary Kaapvaal Craton array. This study (and routinepractice with the South African National SeismographNetwork) avoids a multi-layered velocity model, specificallyat the expected depths of hypocenters between 0 km and10 km. A multi-layered model results in clustering of focaldepths at layer boundaries, which is caused by the disconti-nuities in the travel-time curves of the direct phase, PgPP , as a



Table I

Velocity structure of the diagram in Figure 1b used in earthquake location by the South African NationalSeismograph Network

Layer Layer P-wave S-wavethickness (km) velocity (km/s velocity (km/s)

C1 20 5.800 3.353C2 18 6.500 3.757M1 22 8.040 4.647

The P-wave to S-wave velocity ratio is 1: 3, as is commonly found (orassumed).

Figure 1b—Diagram of PgPP , PnPP , sPnPP , PmP, sPmP, and SmP rays travellingthrough the crust from the hypocentre (star) to the station (triangle).Symbols C1, C2, and M1 refer to the velocity layers in Table I. Thisstudy applied the arrival times of these rays to estimate the focaldepths of earthquakes and mine-related events

Figure 2a—Example of a PgPP – PnPP phase analysis. This mine-relatedevent that occurred on 18 April 2009 at 02:38 GMT with epicentre in theKlerksdorp gold mining area was re-located to determine the focaldepth. A diagram of PgPP and PnPP rays traveling through the crust from thehypocentre (star) to the station (triangle) is shown at the bottom right.Waveforms (black and blue traces) bandpass-filtered between 0.8 Hzand 8 Hz recorded by stations listed to the right of the data are overlainby Pg and Pn travel time curves (green and red lines with symbols)predicted by the velocity model in Table I for a focal depth of 2 km


f ffunction of depth at layer boundaries. The PgPP travel timesuddenly decreases when the hypocentre crosses a boundary(for example, the discontinuity at 20 km depth in Table I)since a larger part of the ray is suddenly in a higher velocitylayer, while the PnPP travel time continuously decreases as thedepth increases (Havskov and Ottemöller, 2010b).

The phase analysis shown in Figure 2b is an example forthe signals recorded by selected stations of the South AfricanNational Seismograph Network for the mine-related eventdisplayed in Figure 2a. In Figures 2a and 2b(1), the PnPP phaseovertakes the PgPP phase at a distance of approximately 150km. It is noteworthy that, while the PnPP phase arrival times(i.e. travel time curves) are sensitive to the different focaldepths of 2 km, 5 km, and 10 km, PgPP is insensitive tochanges in focal depth at these large hypocentral distances.Focal depth determination depends on accurate PgPP and PnPPtravel times and therefore small uncertainties in the epicentre,origin time, and crustal velocity model may result ininaccurate depth estimates. These inaccuracies show up asmisalignments between the phase arrival measurements andtravel time curves in Figure 2b. Misalignments are approxi-mately 2 to 10 km and ½ to 1 second, which is a typicalepicentre and origin time uncertainty for locations determinedby means of the South African National SeismographNetwork. To ensure a reliable depth estimate, several PgPP andPPnPP phase arrival measurements must be available.

f f fAfter the initial determination of the epicentre of a mine-related event or tectonic earthquake at a fixed depth of 2 kmor 5 km, respectively, the depth parameter is set free but noweighting can be applied to the depth parameter. The RMS ofthe differences (residuals) between the measured travel timesand those predicted by means of the velocity model in Table Iis calculated as a function of depth (Figure 3). Focal depth ismeasured at the minimum RMS. Depths, D, for tectonicearthquakes have a range of 2 km D 10 km and anaverage depth with a standard deviation of 6.9 ± 2.3 km.Depths for mine-related events range over 0 km D 7 km,averaging 3 ± 2.3 km. One tectonic earthquake and twomine-related events were rejected from the original data-setbecause their smallest values were reached at a depth of 0 kmwithout what looked like a minimum. Also note that theWKBJ synthetic seismogram software applied below cannotaccommodate a hypocentre at the surface.

Travel times with regional depth phases

Focal depth estimation may be further improved by includingso-called ‘depth phases’, which are sensitive to changes inthe focal depth. In this study the author measured additionalarrival times for the ascending S-wave converted at thesurface to a critically refracted P-wave, sPnPP ; reflected P-waveat the Moho discontinuity, PmP; ascending S-wave convertedPPat the surface to a reflected P-wave at the Moho discon-tinuity, sPmP; and a descending S-wave converted to aPPP-wave when reflected at the Moho discontinuity, SmP(Figure 1b). Figure 4a shows synthetic waveforms and

▲


Figure 2b(2)—PnPP (left) – PgPP (right) phase analysis of stations Lobatse,Botswana (LBTB), Pongola (POGA), and Upington (UPI) from Figure 2a.Recorded waveforms (thin traces) bandpass-filtered between 0.8 Hzand 8 Hz are overlain by PgPP and PnPP travel time curves predicted by thevelocity model in Table I for focal depths of 2 km (dashed line), 5 km(solid line), and 10 km (dash-dot line). Note that PgPP travel time curvesplot on top of one another. Phase arrival measurements are indicatedby vertical lines

Figure 2b(1) – Pg – Pn phase analysis of stations Koster (KSR),Schweizer-Reneke (SWZ), and Silverton, Tshwane (SLR) from Figure 2a.Recorded waveforms (thin traces) bandpass-filtered between 0.8 Hzand 8 Hz are overlain by Pg and Pn travel time curves predicted by thevelocity model in Table I for focal depths of 2 km (dashed line), 5 km(solid line), and 10 km (dash-dot line). Note that Pg travel time curvesplot on top of one another. Phase arrival measurements are indicatedby vertical lines

fcorresponding travel time curves for these regional depthphases at hypocentral distances between 25 km and 800 kmfor a focal depth of 7 km. The waveforms and travel timecurves were generated by means of the WKBJ syntheticseismogram software (Chapman, 1978) for the velocity modelin Table I. Note that the travel time differences between PnPPand sPnPP as well as PmP and sPmP are nearly constant overthe whole hypocentral distance range for which these phasescan be measured. In Figure 4b, synthetic waveforms andtravel time curves are shown for a hypocentral distance of325 km and focal depths that vary from 1 km to 15 km. Asbefore, focal depth is insensitive to PgPP arrival times, but issensitive to the arrival times of all the other phases. Note thatfocal depth is especially sensitive to the relative travel timedifferences between PnPP and sPnPP and the PmP and sPmPphases.

An example of a depth phase analysis is shown inFigure 5 for the signals recorded by selected stations of theSouth African National Seismograph Network for the mine-related event depicted in Figure 2a. The predicted arrivaltimes were calculated using the WKBJ synthetic seismogramsoftware. The number of available phases to analyse as wellas the misalignments between the measured and predictedphases had increased. This may be ascribed to an increasinguncertainty for the crustal velocity model, e.g. depth of theMoho for the reflected PmP phase and S-waves velocity(which is assumed to be 3

1 of the P-wave velocity) for theconverted SmP phase. An additional uncertainty wasintroduced by the phase measurements: PgPP and PnPP phasesshow up best on a signal bandpass filtered between 0.8 Hzand 8 Hz; hence this filter was used to re-locate the eventsand earthquakes in the previous section. Although phasessPnPP and PmP are usually clearer after they have been filtered



Figure 3—Root mean square (RMS) of the differences between themeasured travel times and those predicted using the velocity model inTable I, calculated as a function of depth for mine-related events (left)and tectonic earthquakes (right). Focal depth is measured at theminimum RMS. The average focal depths are indicated by dash-dotlines and standard deviations by dashed lines

Figure 4b—Synthetic waveforms (black and blue traces) at ahypocentral distance of 325 km bandpass-filtered between 0.5 Hz and1.5 Hz overlain by PgPP , PnPP , sPn, PmP, sPmP, and SmP travel time curves(coloured solid and dotted lines with symbols) predicted by the velocitymodel in Table I for focal depth of 1 km to 15 km

Figure 4a—Synthetic waveforms (black and blue traces) at hypocentraldistances from 25 km to 800 km bandpass-filtered between 0.5 Hz and1.5 Hz overlain by PgPP , PnPP , sPn, PmP, sPmP, and SmP travel time curves(coloured solid and dotted lines with symbols) predicted by the velocitymodel in Table I for a focal depth of 7 km. A diagram of the raystravelling through the crust from the hypocentre (star) to the station(triangle) similar to Figure 1b is shown at the bottom right

on


between 0.8 Hz and 3 Hz, this is not always the case. Forexample, the ‘incorrectly measured’ PmP phase arrival inFigure 5(1) for station PRYS was analysed using the 0.8 Hzto 8 Hz filter, which showed a clearer onset than for the lowerfilter. It should be noted that the PnPP phase onset for stationLBTB was not clear for the lower filter (Figure 5(2)).

To determine the focal depth, earthquake hypocentreswwere first calculated (i.e. re-located) for the depth range 1 kmto 15 km using the Hypocenter software. The correspondingpredicted arrival times of the depth phases were thencalculated using the WKBJ synthetic seismogram software.The introduction of additional measured phases, but withlarger errors, required a more robust cost function than theRMS to estimate the focal depth. Hence the author appliedvvariance as the cost function to this regional depth phasetravel time investigation (e.g. Steyn et al., 1999). Thevvariance of the travel time residuals estimates the spread ofthe measured and predicted travel time differences. A zerovvariance means that all the measured and predicted traveltimes are identical. A sample variance may be applied toestimate the variance of a continuous distribution from asample of that distribution: this is an unbiased estimator ofthe variance of the population from which the samplevvariance is drawn provided that the range consists ofindependent, identically distributed samples. The minimumsample variance (of the sample variances calculated fordifferent depths) of the travel time residuals of PgPP and depthphases PnPP , sPnPP , PmP,PP sPmP, andPP SmP would give an unbiasedestimate of the focal depth. The requirement is that theselection of depth phases must be large and representativeenough so that it is possible to discard uncertainties inrespect of epicentre, origin time, and crustal properties.

f fFigure 6 shows sample variance as a function of depth. Focaldepth is measured at the minimum sample variance. Depthranges and averages are 4 km D 7 km with 5.9 ± 1.2 kmand 1 km D 4 km with 2.4 ± 1.2 km for tectonic andmine-related events, respectively.

Waveform modelling

The author verified the focal depths of 8 km (re-location) and5 km (travel times with regional depth phases) for onetectonic earthquake that occurred on 18 December 2011 at18:07 GMT with its epicentre in the Augrabies area.Synthetic-to-recorded-waveform fits were applied using theWKBJ synthetic seismogram software for the abovementioneddepth phases (c.f. Ma, 2012). The focal mechanismparameters for this earthquake source needed to generate thesynthetic waveforms were obtained from the NationalEarthquake Information Centre. Unfortunately, no focalmechanism parameters could be calculated for any of theother earthquakes or events. Brandt and Saunders (2011)had obtained regional moment tensors in a previous studyusing the dense Kaapvaal Craton array, but the NationalNetwork is too sparse to obtain reliable fault plane solutionsfor single, large earthquakes. Before depth phase modellingcan begin, each synthetic seismogram is time-shifted withrespect to its recorded counterpart until the first-arrivalrecorded and synthetic phases (PgPP or PnPP ) are aligned. In thisway, uncertainties in the epicentre, origin time, and crustalvelocity model are smoothed out.

Examples of seismograms illustrating the method ofrecorded-to-synthetic-waveform fits are shown in Figures 7aand b. The focal depth is obtained by visually comparing

▲


Figure 5(2)—Depth phase analysis of stations Lobatse, Botswana(LBTB), Pongola (POGA), and Musina (MSNA) from Figure 2a.Theoretical phases PgPP , PnPP , sPn, PmP, and SmP predicted by the velocitymodel in Table I for a focal depth of 2 km are indicated by dashedvertical lines and the corresponding measured phases by solid verticallines. The waveforms were bandpass-filtered between 0.8 Hz and 3 Hz

Figure 5(1)—Depth phase analysis of stations Parys (PRYS), Schweizer-Reneke (SWZ), and Boshof (BOSA) from Figure 2a. Theoretical phasesPgPP , PnPP , sPn, and PmP predicted by the velocity model in Table I for afocal depth of 2 km are indicated by dashed vertical lines, and thecorresponding measured phases by solid vertical lines. The waveformswere bandpass-filtered between 0.8 Hz and 3 Hz

absolute and relative arrival times of the synthetic phasesignals with recorded phases. (Also see Figures 4a and 4b).All the phases could be identified after some additionalfiltering on the seismogram recorded at station KOMG(Figure 7a). Station CVNA at Calvinia, at almost the samehypocentral distance but situated south of the epicentre(Figure 1a), recorded only clear PnPP and sPss nPP phases; the otherphases could not be identified (Figure 7b). This may beattributed to the double couple source mechanism thatgenerates directional seismic waves. Ma and Atkinson(2012) found that usually only one or two stations (among arange of stations) can be used to determine focal depth.Useful stations usually also record only either clear PnPP andssPnPP or PmP and sPmP phases, although Ma and Atkinson(2012) did not apply different filters to their data as has beendone in this study. The author was able to identify PnPP andssPnPP phases on seismograms recorded by eight stations (e.g.for CVNA in Figure 7b) with modelled focal depths of 5 km or6 km using relative arrival times. This study obtained a depthof 8 km for station KOMG with an overall, general best fit forthe absolute arrival times of all the phases and relative timesfor phases PnPP and sPnPP as well as PmP and sPmP (Figure 7a).

Conclusions

The results confirm the assumption that focal depths of SouthAfrican earthquakes and mine-related events are shallow —wwithin the upper third (0 km to 10 km) of the crust.



Figure 7b—Example of a synthetic-to-recorded-waveform fit at stationCalvinia (CVNA) at a distance of 318 km for a tectonic earthquake thatoccurred on 18 December 2011 at 18:07 GMT with epicentre in theAugrabies area. Recorded waveforms (thick black trace) and syntheticwaveforms (thin black and blue traces) bandpass-filtered between 0.5Hz and 1.5 Hz are overlain by PgPP , PnPP , sPn, PmP, sPmP, and SmP traveltime curves (coloured solid and dotted lines with symbols) predicted bythe velocity model in Table I for a focal depth of 6 km. The syntheticseismograms are shifted by -1.47 seconds to align the recorded andsynthetic PnPP phases

Figure 6—Variance of the differences between the measured traveltimes and those predicted using the velocity model in Table I calculatedas a function of depth for mine-related events (left) and tectonicearthquakes (right). Focal depth is measured at the minimum variance.The average focal depths are indicated by dash-dot lines, and standarddeviations by dashed lines

Figure 7a—Example of a synthetic-to-recorded-waveform fit at stationKomaggas (KOMG) at a distance of 311 km for a tectonic earthquakethat occurred on 18 December 2011 at 18:07 GMT with epicentre in theAugrabies area. Recorded waveforms (thick black trace) and syntheticwaveforms (thin black and blue traces) bandpass-filtered between 0.5Hz and 1.5 Hz are overlain by PgPP , PnPP , sPn, PmP, sPmP, and SmP traveltime curves (coloured solid and dotted lines with symbols) predicted bythe velocity model in Table I for a focal depth of 8 km. Recorded signalsaround the PnPP and SmP phases, bandpass-filtered between 2 Hz and 4Hz (thick black dotted traces) are plotted on top of the waveforms.Synthetic seismograms are shifted by -0.71 seconds to align therecorded and synthetic PnPP phases


f fMine-related events are classified by operators of localunderground mine networks as fracture-dominated ruptureevents or development blast and friction-dominated slipevents or mining-induced events. Development blast eventstypically occur within 100 m of active mine tunnels, whereasmining-induced events may occur up to 200 m from tunnels(e.g. Spottiswoode and Linzer, 2005). A detailedunderground inspection of fault zones associated with themagnitude 4.6 Matjhabeng earthquake that occurred in 1999near Eland shaft at Welkom, Free State gold mining area,reported that the active faults could be accessed and slip anddisplacement measured (Dor et al., 2001). Gold mining isnow reaching depths of around 4 km and mine events arethought to be associated with active mining. This confirmsthe derived depth range of foci between 1 km D 4 km.

No evidence was found to support the hypothesis thatearthquake focal depths in South Africa follow a bimodaldistribution, with deeper hypocentres in the lower third of thecrust (20 km to 35 km), as had been determined for otherstable continental regions. However, this result should beinterpreted with circumspection, because of the small tectonicdata-set, and the fact that most of the earthquakes occurredin the Augrabies area.

The study justifies the routine practice used by the SouthAfrican National Seismograph Network of fixing the depth ofmine-related events to 2 km and tectonic earthquakes toeither 5 km or 10 km when locating these events by means offirst-arrival phases.

Focal depths determined when re-locating earthquakeswwith PnPP and PgPP phases are similar to the depths obtainedusing the more advanced techniques of travel times withregional depth phases and waveform modelling. Given theminimal effort involved in measuring additional PnPP and PgPPphases and re-locating an earthquake once the epicentre andfixed depth have been routinely determined (by means of thefirst-arrival phases), this technique may be applied regularlyto major earthquakes in the future.

AAcknowledgements

This research was funded as part of the operation and dataanalysis of the South African National Seismograph Network.Two anonymous reviewers made thoughtful suggestions toimprove the article. The author wishes to thank the Councilfor Geoscience for permission to publish the results. Zahn Nelundertook the language editing.

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Introduction

Crush pillars were introduced to the destressstopes of South Deep Gold Mine (South Deep)to improve efficiencies and face availability.The behaviour of crush pillars has beenstudied previously on the platinum mines(Watson, 2010), and the findings wereadapted to the different environment at SouthDeep. One of the most significant environ-mental differences was the larger closure ratewwith occasional rapid loading, typical of a deepmining environment. The quartzite rocks ofthe Witwatersrand gold mines are also morebrittle than rocks of the platinum mines, whichneeded to be incorporated in the designphilosophy. This paper describes the designmethodology as well as measurementsconducted to verify the design. Finally, theresults from a trial site are described.

The old and new mining methods arecompared to provide the reader with a sense ofthe positive financial impact that the pillarscan have on the mine.

Description of the mine

South Deep is a mechanized mine, extracting awide reef at depth. Such deep-level, wide reefshave not been mined elsewhere in the worldand the production layouts developed at themine are therefore unique. South Deep issituated approximately 45 km west ofJohannesburg and 20 km south of Randfonteinin the West Witwatersrand mining region(Figure 1). The entire mining area covers 4232 ha and extends for 9.5 km north-southand 4.5 km east-west at its widest points.

The South Deep orebody lies within theCentral Rand Group of the WitwatersrandSupergroup and is overlain by the Ventersdorplavas (Figure 2). The Ventersdorp Contact Reef(VCR) and Upper Elsburg reefs are of economicimportance.

The Upper Elsburg reefs subcrop againstthe base of the VCR, which is a major strati-graphic unconformity (Figure 3). Towards theeast the orebody diverges and thickens up toabout 130 m at the eastern extremity of themine boundary, with an increasing percentageof unpay quartzite middlings in the thickerregions. The dip and strike of the orebody varyacross the mine, but it generally dips to thesouth at between 10° and 14°. This dip angleis too steep for normal mechanized equipment.

The orebody is currently being mined atdepths of between 2500 m and 2700 m, andfuture mining is planned at 3400 m belowsurface. The virgin vertical stress is high andwill become higher as the depth of theoverburden increases.

Most of the conglomerate layers within theUpper Elsburgs are extremely strong, brittlerocks. In high-stress environments, theserocks store strain energy that can be released

Design and positive financial impact of crushpillars on mechanized deep-level mining atSouth Deep Gold Mineby B.P. Watson*, W. Pretorius*, P. Mpunzi†, M. du Plooy*, K. Matthysen*, and J.S. Kuijpers‡

SynopsisCrush pillars have been incorporated into a mechanized, low-profiletrackless system at South Deep Gold Mine. These pillars had to be designedto fail near the face and to ensure that pillar failure is contained within thepillar, to avoid bursting and the risk of high loads being generated duringa seismic event, respectively. PoweRite backfill bags were recommended tomaintain the integrity of the pillars; except in the main access drives,where the sidewalls were to be supported on 5.6 mm diameter weldmeshand yielding anchors. The results of a trial site investigation exceededexpectations, showing a residual pillar strength of about 37 MPa for anewly formed pillar and 8 MPa for a pillar subjected to seismicity and aclosure of more than 300 mm. The introduction of these pillars hasimproved the rock mass conditions because of the active nature of thesupport, compared to the previous passive backfill method. Importantly,the pillars have increased mining efficiencies and improved faceavailability. A potential cost saving to the mine of R140.9 million could berealized over a period of 10 years.

Keywordscrush pillars, de-stress mining, backfill.

* Gold Fields Ltd.† SRK Consulting (South Africa) (Pty) Ltd.‡ Centre for Mining Innovation CSIR.© The Southern African Institute of Mining and

Metallurgy, 2014. ISSN 2225-6253. Paper receivedrrNov. 2013; revised paper received Jun. 2014.


Design and positive financial impact of crush pillars on mechanized deep-level mining

vviolently in the form of rockbursts. Experience has shownthat the higher the walls of an excavation, the greater thetendency for buckling and violent failure.

Destressing philosophy

It was found that a narrow tabular cut could sufficientlydestress the orebody to allow normal massive miningtechniques to be conducted above and below the cut. This

fnarrow cut was backfilled in the past to limit beddingseparation in the hangingwall and to restrict rockburstdamage, due to its stiffness, areal support, and energyabsorption capacity. The new concept involves a combinationof crush pillars and backfill to achieve the same objectives.

Early elastic numerical modelling work (Smallbone,James, and Isaac, 1993) indicated that an optimum conven-tional destressing cut could be achieved with backfill in astoping width of 1.5 m to 2.0 m. The panels in the destresscut could generally be kept below the mining industry’saccepted average energy release rate (ERR) criterion of 30 MJ/m2 (Jager and Ryder, 1999) if the span was limited to250 m, by appropriately spaced stability pillars (Joughin andPethö, 2007). It was shown that the destress mining could‘cheat’ gravity by reducing the major stress component fromaround 70 MPa to about 30 MPa in the shadow area of thedestress stope (Figure 4). The stress conditions in the areamarked as the ‘window of massive mining opportunity’, inthe figure is similar to what could be expected at a depth of 1 200 m. The major stress is horizontal in this scenario.

Simple elastic modelling showed that the vertical stresscould be reduced to between 10 MPa and 20 MPa for adistance of 30 m above and below the stope. At a middlingdistance of 60 m, this stress increased to about 30 MPa. Thehorizontal stress in the north-south direction was shown tobe the highest, increasing from 20 MPa near the destressstope to 50 MPa at 50 m above and below this horizon. Thevirgin stress tensor used in the modelling was determinedfrom stress measurements using CSIR cells, carried out at adepth of 2 650 m below surface (Smallbone, James, andIsaac, 1993).

The horizontal mechanized method of mining that isemployed at the mine was developed at South Deep. Itinvolves layered horizontal destress cuts that overlap anddestress the large orebody target horizons (Figure 5). Accessto the destress cut is through a spiral decline, which isoptimally sited beneath a previously mined destressed area.Access drives are developed horizontally from the spiraldecline to each horizontal destress horizon. Each horizon ismined as a series of 5.0 m wide by 2.2 m high drives, whichneed to be sequenced optimally to mitigate rockburst andstress damage. This destress cut is subsequently integrated

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Figure 1—Locality map showing South Deep in relation to adjacentmines

Figure 2—Simplified 3D section showing the stratigraphy around thestrategic reefs

Figure 3—Generalized east-west section showing the stratigraphy ofthe orebody

Figure 4—Diagram showing a sectional view of the destress concept.The red and white lines show the vertical and horizontal stress trajec-tories, respectively

wwith the longhole stoping by utilizing the destress cut drivesas longhole drives (Figure 5). To achieve this objective, thedimensions of the destress drives are increased to 5 m × 5 mbehind the destress face by sliping the footwall. This layoutallows for selective mining in the massives, where there maybe sub-economic quartzite middlings.

The layout of the current destress mining cut is shown inFigure 6. Main access drives (MADs) are generally developedin the dip direction. Stope drives (SDs) are mined adjacent tothe MADs in a staggered configuration to maintain industry-acceptable lead-lag distances (Jager and Ryder, 1999). Stopeaccess drives (SADs) are created every 15 m by cutting 5 mholings through the crush pillars at appropriate locations.This ensures that cross-fracturing is avoided in the SADhangingwall. Separate cuts are spaced about 17 m vertically,dictated by the average width of the target grade.

Backfill bags from Reatile TimRite (PoweRite bags) areinstalled along the edge of the crush pillars to provideconfinement to the pillars and to stop pillar disintegration atlarge closures. The sidewalls of the MAD are supported on5.6 mm diameter weldmesh and yielding tendons.

Original destress method

In the past, a typical mining sequence would start byadvancing the MAD by 15 m and the first SAD (TOP SAD) by5 m on either side of the MAD (Figure 7). The first 10 m ofthe MAD would subsequently be backfilled, before the SDs oneither side of the MAD could be developed. The SDs wouldthen be backfilled as indicated in Figure 8 and the backfill inthe MAD mined out. The process described above would be

repeated every time the MAD was advanced, and an SD couldbe mined only after the adjacent SD or MAD was backfilled(Figurs 9).

PoweRite bags were used as bulkheads and to ensure



Figure 5—Schematic section view of the horizontal destress layout

Figure 6—Plan view of the current horizontal destress layout with crushpillars

Figurs 9—Plan view of the original horizontal destress layout afterseveral cycles of installing and removing backfill in the MAD

Figure 8—Plan view of the original destress method showing the firstphase of development (backfill mined back out of the MAD)

Figure 7—Plan view of the original destress method showing thebackfill requirement


f ftight filling adjacent to the MAD. However, the backfillbehind the bags was rarely tight-filled due to the horizontalinclination of the excavation.

The new layout involves the use of crush pillars withPoweRite bags, as shown in Figure 6.

AAdvantages of the crush pillar

The destress layout with crush pillars is depicted in Figure 6.The pillars allow mining to take place in the adjacent SDswwithout first having to backfill the previously minedexcavation. The implications are:

➤ No backfill required in the MAD, improving miningefficiencies

➤ Face availability is improved because SDs can be minedat an earlier stage in the process

➤ Reduced backfill dilution during longhole stoping.

In addition, the crush pillars are an active support andinhibit hangingwall unravelling, which is often observedwwhere the reef is replaced by backfill. If properly designed,these pillars do not create additional fractures in thehangingwall and overall stability is improved. Subsequent tothe destressing process, the crush pillars will be mined outwwith the longhole stopes. Therefore, the extraction efficiencyis not affected by leaving crush pillars.

Method comparison

Three design options were compared to determine thefinancial impact of crush pillars at South Deep (Figure 10).These options are described as:

➤ The original design – MADs that are initially mined,backfilled, and mined out a second time (backfill minedout) once the neighbouring SDs are mined andbackfilled (Design 1)

➤ Crush pillars on the MADs only – here, time is saved asthe MADs do not require re-mining (Design 2)

➤ Total crush pillar design – all MADs and neighbouringSDs are separated by crush pillars (Design 3).

After four weeks, Design 3 achieves 44% more squaremetres than Design 1 (Figure 11). During this stage, Design 1and Design 2 achieve similar square metres, as Design 2 isnot constrained by backfill yet. The variance between Design3 and Design 1 increases until week 19, when the difference

f fis 59% (Figure 12). A comparison of the three designs after aperiod of three months is provided in Figure 13.

While destress mining alone controls how much of thereserves are prepared (destressed) for stoping, othercomponents such as capital infrastructure to supply air androck-handling capabilities all combine to form a plan. Thisplan dictates the steady-state volumes that can be handled.

In the above case study, Design 1, Design 2, and Design 3reach steady states by weeks 29, 21, and 19, respectively(Figure 12). The effect of the ’free square metres’ from thecrush pillars in Design 3 can be seen in the differencebetween steady-state production outputs. The averageeffective square metres achieved in Design 3 is 18% greaterat steady state than for the other two designs.

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Figure 10—Isometric oblique view of the three design options

Figure 11—Isometric oblique view of the design comparisons after fourweeks

Figure 12—Progressive square metres destressed per week

Figure 13—Oblique view of the design comparison after three months

Financial impact of crush pillars

Savings associated with the crush pillar design can beseparated into three components:

➤ Reduction in backfill requirement➤ Reduced destress mining ➤ Productivity improvements.

The requirement for backfill is reduced in two areas:

a) In the establishment of a new destress cut, all MADshad to be filled and subsequently mined again due toa mining span constraint. With the advent of thecrush pillar method this is no longer a requirement.The savings associated with this activity reduce theamount of backfill placed in the destress mining by4.3% or 62 400 m3 in 10 years at a cost of R299 persquare metre.* This saves R18.7 million over the next10 years (Table I)

b) Less backfill is required as the crush pillar volume isno longer backfilled. This reduces the area to be filledby 15.4% at a saving of R66.9 million for the next 10years (Table II).

There is a reduction in the planned low-profile mining of15.4% (crush pillar area). This reduces the destress miningrequirement by 90 100 t or R55.3 million at R614 per ton*over a 10-year period (Table I).

Productivity is improved through the increased speed atwwhich a new destress cut is established through the reducedvvolume of mining required.

Backfill adds approximately 18% to the direct cost ofmining the destress. The total cost savings in backfill of 23%in the destress reduces the total direct cost of mining thedestress (including backfill) by 3.5% (from R725 per ton toR699 per ton).

Crush pillar design considerations

The main objective of crush pillar design was to ensure thatthe residual strength of the crush pillars is sufficient tomaintain hangingwall stability, and limit the demand on thetendon support. Pillar size was therefore designed with theresidual strength in mind. However, pillar bursts show thatthe peak strength and loading environment also need to beconsidered in the design. Larger pillars have a higher peakstrength. Therefore, a pillar that is cut too large is likely tofail violently in the back areas where the loadingenvironment is soft. In addition, dynamic loading during

seismic events is an important consideration in the deep-levelgold mining environment. Fracturing should therefore becontained within the pillar so that high load-generation andhangingwall deterioration is avoided during such an event.The investigations to determine a suitable pillar size includedback-analyses, numerical modelling, and undergroundinstrumentation.

Stable crush pillar loading environment

An opportunity to determine a limit on loading stiffness wasprovided when the width of a pillar/peninsular between twodestress panels was inadvertently reduced. Violent failureoccurred when a 2.2 m high face was advanced towards thenorth (Figure 14), reducing the pillar to a width of 5 m andlength of 6 m. The incident was modelled using MAP3D(map3D.com, 2013) to determine the loading stiffness of thestrata when the violent failure occurred. The results of theinvestigation are compared to a more comprehensive investi-gation performed on the platinum mines (Watson, Kuijpers,and Stacey, 2010) in Figure 15. In both instances (platinummines and South Deep), the laboratory-determined elasticconstants of the hangingwall and footwall rock types wereused in the numerical analysis. In the case of the South Deepmodel, the Young's Modulus and Poisson's Ratio were 70GPa and 0.25 respectively.



Table I

Cost savings associated with the crush-pillarmethod

Description of savings Savings R million

Backfill – no mining of backfill in MAD 18.7Backfill – reduced requirement in crush pillar 66.9Mining – Reduced destress volume to be mined 55.3

Total cost saving (for 10 years) 140.9

*2014 first Quarter actual costs used in the estimated savings

Figure 14—Plan view showing the burst pillar that was modelled usingMAP3D to determine an unacceptable loading stiffness

Figure 15—Acceptable and unacceptable strata stiffness of thehangingwall and footwall. The dotted line is parallel to the ‘Platinumunacceptable’ for comparison


ff fThe unacceptable strata stiffness for the platinum mineswwas determined from extensive underground measurements,wwhich are described by Watson (2010). The black loadingline (Figure 15) describes the unacceptable stiffness forSouth Deep, which was determined from the single pillarburst shown in Figure 14; where the pillar dimensions andface positions were reliably determined. The softness of theloading strata at South Deep was calculated to be about 4.7mm/GN. This unloading environment is slightly stiffer thanthe critical 5.0 mm/GN limit for the platinum mines. Theinvestigation suggests that the more brittle conglomeratepillars at South Deep may require a slightly stiffer loadingenvironment, for stable failure, than the platinum mines. Anumerical sensitivity analysis on pillars cut in a typical stopeconfiguration (Figure 16), using the same elastic constants asthe South Deep line in Figure 15, suggests that stable pillarfailure can occur only very near, or at, the face (Figure 17).The residual strengths of the surrounding crush pillars wereassumed to be zero in the model.

Material strength

Uniaxial and triaxial compressive strength tests (UCS andTCS) were conducted on the reef material at the trial site. Arepresentative example of the results is shown in Figure 18.The stress-strain curves are typical of Witwatersrandquartzites, with little nonlinear behaviour and sudden failurewwhich is characteristic of brittle rocks. The triaxial post-failure results, in the same figure, confirm the brittle nature.A typical set of Merensky Reef test results is shown for

f fcomparison in Figure 19. In this figure the post-failure resultsof the triaxial tests indicate a comparatively more ductilematerial. The differences in behaviour and strength show thatthe work done previously on the Merensky crush pillars(Watson, 2010) cannot be directly applied to South Deep.Further research was necessary.

Pillar behaviour

The crush pillars at South Deep are 10 m long, 1.5 m wide,and 2.2 m high. Although these pillars are not truly 2D, the‘perimeter rule’ (Equation [1]), described by Wagner (1974),may be used to compensate for pillars with finite length. Theequation suggests that the pillars at South Deep have aneffective width (we) that is only about 10% less than a full2D model.

[1]

Equation [1] accounts for square and rectangular pillarsby taking cognisance of pillar length (L). In essence, theequation compensates for the effects of the fracture zonearound a pillar.

A series of 2D plane strain FLAC (Itasca ConsultingGroup, Inc., 1993) models were constructed to investigatepillar behaviour in the context of a realistic loadingenvironment. This environment included the pillarfoundations that could sustain damage. For the purposes of

▲


Figure 17—Loading environment with distance from an advancing face

Figure 16—Plan view of the stope (red) and modelled ‘crush’ pillarsFigure 18—Laboratory test results on reef material from South DeepGold Mine. Triaxial confinement shown in the explanation

Figure 19—Laboratory test results on Merensky Reef from the BushveldComplex mines. Triaxial confinement shown in the explanation

fthe model, the foundation material properties were assumedto be the same as the pillar, which was a reasonable approxi-mation for all the instrumented pillars. The model includedthe pillar itself and the immediate hangingwall and footwall.

One important parameter that needed to be considered forpillar behaviour was material brittleness. In the models,brittleness is defined as the rate of stress decrease afterfailure. In these models, post-failure behaviour is controlledby cohesion loss. Therefore, a direct relation betweencohesion softening (strain softening) and material brittlenesscan be quantified. The internal friction angle and the dilationangle were not varied so as to avoid additional complications.

Figure 20 shows the Mohr-Coulomb parameters that wereused in the models. These parameters were calibrated fromunderground measurements of pillar stress and strain in theplatinum mines (Watson, Kuijpers, and Stacey, 2010).Unfortunately, such measurements were not available for theSouth Deep pillars.

Boundary conditions play an important role in the failuremechanism, as they affect horizontal confinement. In themodels (Figure 21), the vertical boundaries were not allowedto move in a horizontal direction (thus simulating a fullyreplicated set of pillars). The presence of discontinuities such

f ffas bedding planes, faults, and joints should also affectfailure, but this was not investigated in the models.

The models were used to evaluate the effect of pillarwidth on strength. Stope span was about five times the pillarwidth in the model (extraction ratio approx. 83%), and themodel height was more than eight times the pillar width. Themodel results for the Merensky Reef material (Figure 22)(Watson, Kuijpers, and Stacey, 2010) compared favourably tothe peak pillar strength formula determined for Merenskypillars (Watson et al., 2008).

The numerical modelling showed that very little pillarstrengthening occurs below a width to height (w/h) ratio of0.75. In addition, pillar punching and foundation failure islikely to initiate only at a w/h ratio of about 1.25 forMerensky Reef pillars. Generally, the larger crush pillars onthe platinum mines do not fail throughout, but the centres ofthese pillars punch into the surrounding strata. In essence,the system fails as shown in Figure 22.

Pillars at South deep are more likely to follow a morebrittle profile (as discussed previously). A subsequentsensitivity analysis on brittleness showed that the w/hratio at which punching initiates increases with brittleness(Figure 23). Thus failure would be safely contained within apillar if w/h ratios were kept below 1.25. To ensure that thepillars did not develop solid centres, a w/h ratio rangebetween 0.68 and 0.91 was recommended for these pillars.Figure 22 suggests that pillars with w/h ratios in this rangewould have a strength in line with the material UCS, i.e.



Figure 20—FLAC model material properties

Figure 21—Diagram showing the double symmetry FLAC model used inthe pillar and foundation investigations. The model was loaded alongthe bottom edge

Figure 22—Comparison between the FLAC modelling (red curves),Merensky Reef strength database (blue lines), and South Deep back-analysis (black line)

Figure 23—Sensitivity analysis on brittleness, FLAC modelling


approximately 200 MPa. Numerical modelling showed thatthere would be sufficient stress to ensure failure/crushing atthe face, even at relatively small spans, at the recommendedw/h ratios.

The pillar used in the South Deep strength back-analysis(Figure 14) had a w/h ratio of 2.3 and was 2.1 times thestrength of a similar Merensky pillar (Figure 22). This ratiocorresponds favourably to the UCS strength ratio between thetwo materials, suggesting a reasonable correlation betweenthe model results and the South Deep pillars. Figure 24shows the APS values in the pillar and foundation curve inFigure 22, multiplied by a factor of 2.1. The curve provides aprobable relationship between peak pillar strength and w/hratio for the pillars at South Deep.

Underground investigations

Having established the optimum pillar dimensions, a trial sitewwas created to monitor the pillar behaviour underground(Figure 25). Visual observations formed an important part ofthe investigations. A section of a pillar was removed toexpose the fracturing in the pillar (Figure 26). It was clearthat the pillars had failed properly as the fracturing extendedthroughout. The hangingwalls were also no more severelyfractured than where crush pillars were not used. Theseobservations suggested that the foundations were not beingdamaged by the pillars. No violent pillar failures occurredwwhen the pillars were cut to the stipulated w/h ratios, eventhough some dynamic closure occurred during seismicevents. However, small strain bursts occurred when thepillars were cut at widths in excess of 3 m (w/h of 1.4) and apillar burst occurred at a w/h ratio of 2.3 (Figure 14).

f fClose examination of fracturing within a properly crushedpillar showed curved fractures extending at least 1 m into thepillar sidewall (Figure 27) on the side of the advanced MADor SD. These fractures tend to create an hourglass-shapesidewall on that side of the pillar, while the lagging facecreated a more square sidewall. The implication being thatthe pillars are failing right at the lagging face, as designed.

The effectiveness of the crush pillar lies in its ability tocarry the load required to promote stability and break thespan between two adjacent excavations. It was thereforenecessary to establish the residual strength of the pillarsimmediately after formation and after large deformations hadtaken place. In addition, the effects of dynamic closure thatoccurs during a seismic event needed to be assessed. Threepillars were selected for stress and closure measurements.Two of these pillars are described in the paper: one newlyformed, and one subjected to closure in excess of 300 mm(including dynamic deformation during a seismic event).

It was necessary to establish an optimum height abovethe pillars where a point measurement could provide areasonable estimate of the average pillar stress (APS). In

▲


Figure 24—Pillar and foundation curve in Figure 22 multiplied by afactor of 2.1 and compared to the South Deep back-analysed pillarstrengths

Figure 25—Panoramic view of a crush pillar layout

Figure 26—Narrow edge of a crush pillar showing the fracturing in thepillar. A PoweRite backfill bag is shown on the right of the pillar

Figure 27—Typical fracturing in a pillar. Black lines highlight thefractures formed by the advanced face

order to determine this optimum height, use has been madeof Boussinesq equation ([2]). This equation quantifies theeffect of a point load on a free surface (half space). The effectof a pillar, with its particular stress distribution, can bequantified by a number of Boussinesq equations, eachrepresenting a specific area of the pillar. Since the applicationof the Boussinesq equation is restricted to a free surface, caremust be taken that neighbouring pillars do not affect thestress levels. This can be achieved by limiting the distanceabove or below the pillar where stresses are to be evaluated.Numerical modelling has shown that a distance of less than 3m results in an error of less than 1% for the crush pillarspans that are used at South Deep. Boussinesq equations canthus be used to provide a unique relationship between theAPS of a pillar and the stresses measured at a point above orbelow that pillar.

[2]

wwhere:σzzσ = stress at a point in spaceAAi = area of the grid ’i’ppzi = vertical stress carried by the grid ’i’.

A matrix of Boussinesq equations (Figure 28) was usedto evaluate typical stress profiles of solid and failed pillars.The profiles used in the analysis are shown in Figure 29, andthe results of the investigation are provided in Figure 30.

The blue curve in Figure 30 shows that for a 1 m widepillar and a measurement height of 2 m, the difference inpoint-stresses between pillars with different ‘realistic’ stressprofiles is negligible. However, the red and green curvessuggest that at the same height only about 20% of the APScould be measured. It was also established that the optimummeasurement height varies proportionately with the width of

the pillar. A good compromise between a reasonably largemeasurement and an inordinate error resulting from anunknown stress profile for a 1.5 m wide pillar is probablybetween heights of 1.5 m (1.0 in Figure 30) and 3 m (2.0 inFigure 30).

The first residual measurement was made at 2.71 mabove a 2.5 m to 3.0 m wide and 5.7 m long pillar (Figure31). This pillar had been newly formed with minimal closureat the time of the measurements; and the face position wasabout 10 m from the pillar. The analysis in Figure 30suggested an 11% error in the residual strength evaluations,but the point stress would be about 50% of the APS if the cellwas installed exactly over the centre of the pillar. However,the actual position was off-centre and required a dedicatedmatrix of Boussinesq equations to calculate the residualstrength.

The stress profile of the pillar in Figure 31 was estimatedfrom the single point-stress measurement, using a similarmatrix to Figure 28 and Equation [2]. Since there was onlyone reliable measurement, a trial-and-error approach wasapplied to likely stress profiles, until the measured stresslevel was simulated. The residual strength of Pillar 1 wascalculated from the stress profile in Figure 32 to be about 37 MPa.



Figure 28—Grid used in the matrix of Boussinesq equations

Figure 29—Typical stress profiles for solid and crush pillars, used in theanalysis shown in Figure 30

Figure 30—Optimum height of a point-stress measurement above apillar to determine average pillar stress

Figure 31—Pillar 1, which was instrumented soon after formation. Nodynamic closure had occurred


The second pillar was between 1.4 m and 2 m wide and7.9 m long (Figure 33). Stress measurements were conductedmore than six months after pillar formation and more than300 mm closure had taken place. Some of the above closurewwas recorded during a nearby seismic event. The residualstrength of the pillar was determined to be about 8 MPa, froma point measurement made at 2.5 m above the pillar.

A stress profile was estimated for Pillar 2 using a similarapproach to that used for Pillar 1. The profile is shown inFigure 34.

Closure measurements were made adjacent to Pillar 2 inthe MAD and SD. Unfortunately, the MAD was required forproduction and only the first part of the closure curve shownin Figure 35 was monitored here. A cubby was createdadjacent to the pillar, i.e. the backfill was mined out, to allowthe remaining measurements. It should be noted that themeasurements shown in the graph may be slightly overstatedbecause the backfill, normally adjacent to the crush pillars,wwas not present to carry some of the load or to confine thepillar. The pillar failure/crushing period can clearly be seen inthe data measured from the MAD. In addition, a nearby eventcaused about 26 mm ’dynamic’ closure on the pillar. Thelocation of the event that is most likely to have caused thisdynamic closure is shown in Figure 36.

AAnalytical solution for residual strength

A relatively complicated analytical solution was derived bySalamon (Ryder and Jager, 2002) to describe the stress distri-bution in a plastic pillar, based on a simple limit equilibriummodel. Equation [3] provides a relationship between APS andw/h ratio, assuming a friction angle of 30°. (The equationapplies to the stress values across the centre of the pillar.) Areasonable correlation between the measured residualstrength of Pillar 2 and the equation was obtained if acohesion of 1.6 MPa was assumed for the failed pillarmaterial (Figure 37). A similar high cohesion was suggestedby the research conducted on Merensky crush pillars(Watson, Kuijpers, and Stacey, 2010). The high residualstrength measured over Pillar 1 was probably because thesmall closure had not caused the pillar to reach its finalresidual strength at the time of the measurement.

[3]

fA series of FLAC models were run to substantiate theanalytical solution results. Several curves with differentdilation angles were modelled, and the results are provided inFigure 37 for comparison. The figure shows a reasonablecorrelation between the underground measurements (Pillar2), analytical solution, and the FLAC models.

▲


Figure 32—Estimated pillar stress profile from a single point-stressmeasurement over Pillar 1

Figure 33—Pillar 2, stress measurements were conducted six monthsafter formation. More than 300 mm closure, including some dynamicclosure, had taken place

Figure 34—Estimated pillar stress profile from a single point-stressmeasurement over Pillar 2

Figure 35—Closure measurements adjacent to Pillar 2

Previous investigations using FLAC models andunderground measurements on the platinum mines (Watson,Kuijpers, and Stacey, 2010) showed little increase in residualstrength above a w/h ratio of about 2.5. This is apparentlydue to the degree of foundation fracturing that occurs abovethis w/h ratio during pillar failure.

The residual strength estimate from Equation [3] andFigure 37 will need to be downrated slightly to account for itsfinite length.

Discussion

The use of crush pillars in a brittle quartzite environmentrequires disciplined mining. There is little tolerance for off-line mining, and pillars cut too wide are risky, with apropensity for bursting. However, if cut properly, the pillarseffectively break the span between individual SDs andbetween the MAD and SDs. This allows for more efficientmining since backfill does not need to be re-handled andfaces become available quicker than with the previous miningmethod.

Conclusions

The measurements and visual observations show that thecrush pillars at South Deep Gold Mine were properly

designed. Their residual strength and behaviour hasexceeded expectations, both under quasi-static and dynamicloading conditions. The concept has the potential to improvemining efficiencies in the destress stopes significantly. Inaddition, the establishment of a new stope is much fasterthan previously accomplished. Potentially the system couldsave the mine R140.9 million over a 10-year period, withoutconsidering the quicker build-up value.

Crush pillars have improved hangingwall conditionsbecause full relaxation of the strata does not take place overthe pillars, as in the backfill paddocks. In addition, distancesbetween pillars are limited to the cut-span, without the largerspans that develop in backfilled areas due to poor fillingpractice.

The only disadvantage of the crush pillar system is thatdisciplined mining is essential. There is little tolerance foroff-line mining, and pillar bursting is a threat if pillars are cuttoo large.

Subsequent to the investigations described in this paper,crush pillars have been rolled out across the mine.

Acknowledgements

Gold Fields is acknowledged for facilitating the success of theresearch work described in this paper. In particular, themanagement of South Deep are thanked for their assistance.

References

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(FLAC), Vers. 3.2. Minneapolis Minnesota USA.

JAGER, A.J. and RYDERRR , J.A. 1999. A Handbook on Rock Engineering Practice for

Tabular Hard Rock Mines. Safety in Mines Research Advisory Committee

(SIMRAC), Johannesburg, South Africa.

JOUGHIN, W.C. AND PETHÖ, S.Z. 2007. South Deep regional pillar modelling Part I

- Design of regional pillars at South Deep Gold Mine. Challenges in Deepand High Stress Mining. Potvin, Y., Hadjigeorgiou, J., and Stacey, T.Rgg(eds). Australian Centre for Geomechanics, Perth, Western Australia.

MAP3D. 2013. www.map3D.com.

RYDERRR , J.A. and JAGER, A.J. 2002. A Textbook on Rock Mechanics for Tabular

Hard Rock Mines, Safety in Mines Research Advisory Committee

(SIMRAC), Johannesburg, South Africa. pp. 174-278.

SMALLBONE, P.R., JAMES, J.V., and ISAAC, A.K. 1993. In situ stress measurements

and use in the design of a deep gold mine. Innovative Mine Design for the21st century. Bawden, W.F. and Archibald, J.F. (eds.). AA Balkema,

Rotterdam.

WAGNERWW , H. 1974. Determination of the complete load-deformation character-

istics of coal pillars. Proceedings of the 3rd International Congress on

Rock Mechanics, ISRM, Denver. vol. 2B. pp 1076–1082.

WATSONWW , B.P. 2010. Rock behaviour of the Bushveld Merensky Reef and the

design of crush pillars. PhD thesis, School of Mining Engineering,

University of Witwatersrand, Johannesburg, South Africa.

WATSONWW , B.P., KUIJPERSKK , J.S., and STACEY, T.R. 2010. Design of Merensky Reef

crush pillars. Journal of the Southern African Institute of Mining andMetallurgy, vol. 110, no. 10. pp. 581–591.

WATSONWW , B.P., RYDERRR , J.A., KATAKAKK , M.O., KUIJPERSKK , J.S., and LETEANE, F.P. 2008.

Merensky pillar strength formulae based on back-analysis of pillar failures

at Impala Platinum. Journal of the Southern African Institute of Miningand Metallurgy, vol. 108. pp. 449–461. ◆



Figure 36—Seismic events that occurred between 14 December 2012and 18 December 2012

Figure 37—Relationship between w/h ratio and residual strength.Cohesion = 1.6 MPa, angle of internal friction = 30°

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Experience Matters.

S13

924

Overview of coal mining problems

Safe and efficient coal extraction is oftencompromised by a variety of geological andmining-induced problems. In this paper, wewill focus on the five most commonlyoccurring problems and explain how theapplication of selected geophysical methodscan play a key role in addressing theseproblems. The problems considered here are asfollows:

➤ Delineation of old workings➤ Near-surface cavity detection and

evaluation of surface depressions➤ Detection of intrusive dykes and sills

(magnetic and non-magnetic)➤ Structural and in-seam continuity

disruptions (faults, lenses)➤ Dolomitic pinnacles/uneven basement.

Delineation of old workings

In areas where mining encroaches on apreviously mined area, the historic mine plansdo not always provide accurate informationregarding the extent of historic and existingdevelopments. Mining into old workings mayresult in hazards such as flooding ofadvancing workings. The development ofinfrastructure over old workings may also beundesirable, especially in areas that aresusceptible to surface subsidence. There istherefore a need for a non-invasive technologythat can accurately delineate old workings.

Near-surface cavity detection

Old workings in previously mined areascommonly deteriorate over time and roofsupport pillars or beams fail. This may lead tosubsidence or caving (also known as rat-holing) above the old workings. This cavingmay ultimately break through to the surface,or it could form undetected, near-surfacecavities; either way, presenting a clear mininghazard.

Detection of intrusive dykes and sills

Intrusive dykes and sills impact adversely onthe operational cost of coal extraction. Thesegeological structures typically disrupt thecontinuity of coal seams and may alsoadversely affect the properties of coal seamsthat occur in close proximity to the intrusions(Du Plessis, 2008). Intrusive bodies typicallyconstitute a relatively tough geologicalmaterial, such as dolerite, which presents agreat challenge for mining machines and can

The application of geophysics in SouthAfrican coal mining and explorationby M. van Schoor* and C.J.S. Fourie†

SynopsisCoal remains South Africa’s most abundant and cheapest source of energy,and there is an ever-increasing necessity for optimal and safe extraction ofthe remaining reserves. Increasing focus on cost-effective mining and zeroharm to the environment and miners has resulted in a shift in attitudetowards the application of geophysics in local coal mining and exploration.Furthermore, technological advances have contributed to geophysics beingembraced more readily by the coal mining industry, compared to a decadeor two ago. Predictably, the growing interest in geophysical technologieshas also created a need for education and training in the basic principlesand application of geophysical methods, as local coal mining companiesgenerally do not have in-house geophysicists. Consequently, the CoaltechResearch Organisation’s Geology and Geophysics working group forumcompiled a textbook aimed at addressing this need: to produce a guide forapplying geophysics to coal mining problems in South Africa. The targetaudience for such a book would be coal geologists, mine surveyors, mineplanners, and other mining staff with limited or no geophysicsbackground. This paper provides a very brief overview of the book bysummarizing key sections and selected examples. In doing so, the value ofgeophysics to solving a range of coal mining and exploration problems ishighlighted.

Keywordsgeophysics, coal, old workings, dykes, sills, faults.

* CSIR, Natural Resources and the Environment,Pretoria, South Africa.

† Environmental Water and Geological SciencesDepartment, Faculty of Science, Tshwane Univerisityof Technology , Pretoria, South Africa.

© The Southern African Institute of Mining andMetallurgy, 2014. ISSN 2225-6253. Paper receivedJun. 2013; revised paper received May 2014.


The application of geophysics in South African coal mining and exploration

damage equipment. To complicate matters, the occurrence,geometry, and area of influence of intrusive bodies aredifficult to predict ahead of mining, and unexpected intrusivebodies may negatively impact reserve and target estimates.

Structural and in-seam continuity disruptions (faults,llenses)

Geological faults disrupt and displace the seam horizon,wwhich impacts adversely on production and mining due to theneed to adapt or relocate workings. Regional-scale faults areoften known in advance, but small mine- or metre-scalefaults are often encountered only during active mining.Localized in-seam inclusions such as smaller doleriteintrusions and sandstone lenses also present a greatchallenge to early detection efforts because of their geometry.Such features may only be a few metres in diameter and theyalso typically do not have a linear trend or extend up towardsthe surface. The size-to-depth ratio of these features isrelatively small, making them difficult to detect usingconventional surface geophysical techniques, and in-mine orborehole-based geophysical methods may have to be used.

DDolomitic pinnacles/uneven basement

This problem occurs only where opencast mining activitiesare conducted in an area with a dolomitic basement that doesnot constitute a predictable, flat-lying horizon. Finger-likestructures (pinnacles) that protrude upward and disrupt thelateral continuity of the overlying coal seams and slumpstructures (potholes) present extreme difficulties for miningdue to the varying floor topography and the relative hardnessof the dolomite (Lanham, 2004).

Geophysical applicability considerations

There are a number of factors that determine the applicabilityof a geophysical method to a given problem. These factorsare:

➤ Physical property contrast (each geophysical methodtargets a different physical property)

➤ Range➤ Required resolution (mapping accuracy)➤ Geometry and scale of problem.

PProperty contrast

The fundamental requirement that governs the ability of anygeophysical method to provide a solution is that there mustbe a detectable contrast in some physical property betweenthe target and its surroundings (host rock) that can beexploited by geophysical measurements. For example, if thetarget is a subsurface void located in sedimentary rocks, thevvoid will have a lower density and higher resistivity than thehost rock. If, however, the void is filled with weatheredmaterial it may have a negligible density contrast with itssurroundings, but then it may be relatively conductivecompared to the host material.

RRange

Every geophysical system will have a characteristic minimumdetectable signal – below this level the system will essentiallydetect only noise. Furthermore, there is typically an inverse

relationship between the recorded signal strength and thedistance between the system and the target. The maximumpossible distance between the system and the target ofinterest is known as the range. The range is determined by avariety of parameters such as the physical properties of theintervening rock mass and the type and properties of thesource of the geophysical signal. As an illustration, at oneextreme a high-frequency ground penetrating radar (GPR)system might provide a range of only a couple of metres,while at the other extreme, a reflection seismic system couldachieve several hundred metres.

Required resolution (mapping accuracy)

Resolution technically refers to the smallest distance betweentwo target objects for which the geophysical method can stilldiscriminate between the two distinct objects. As with range,the resolution is determined by a variety of parameters, butprimarily the characteristics of the sourced signal isimportant. As an example, wave-based methods (e.g. GPR)have a trade-off between range and resolution, which can becontrolled to some extent through the operating frequency.Using a lower frequency increases the wavelength and lowersthe signal attenuation. The result is a longer range, but at thecost of a decrease in resolution.

Geometry and scale of problem

Access to the survey area and proximity to the target play animportant role in selecting possible geophysical solutions.Most traditional geophysical applications involve the takingof measurements on the surface. This approach is, however,not always good enough to achieve the desired depth ofinvestigation and mapping accuracy. For this reason it isoften necessary to exploit boreholes and undergrounddevelopments to get closer to the geophysical target. Somegeophysical methods also demand very specific or non-standard survey geometries; for example cross-holetomographic imaging technologies will require a pair of co-planar boreholes (or mining tunnels) that straddle the targetzone. Borehole radar reflection surveys will require a singleborehole drilled sub-parallel to a planar target horizon.

Based on the above considerations and requirements, andthe coal problems described earlier, Table I shows the basicapplicability guidelines for geophysical techniques.

Selected case studies

In this section, selected case studies, illustrating the applica-bility of geophysics to the previously described problemscenarios, are presented.

Delineation of old workings

Old workings are often filled with mine water, which resultsin a relatively high bulk electrical conductivity compared tothe virgin coal. This contrast in conductivity can be exploitedby the TDEM method as illustrated in Figure 1a. This trialsurvey was done over a known old workings boundary, andthe TDEM depth slice clearly shows the cross-over fromrelatively resistive virgin coal (cold colours) to conductivewater-filled workings (orange-red), located at a depth of justover 30 m.

▲


fFigure 1b illustrates the application of the microgravitymethod to the delineation of old workings. This example isfrom a coalfield in the UK; the blue polygons indicate zonesof contrasting (low) gravity. Subsequent drilling confirmedthe correlation between these low gravity anomalies and air-filled old workings occurring at a depth of approximately 12–14 m.

NNear-surface cavity detection

Figure 2a shows the result of a ground FDEM surveyconducted over an area where a known abandoned mineshaft was buried under spoils. The EM-31 grid survey clearlyimaged the highly resistive (low-conductivity) anomalyassociated with the buried void. In cases where the suspectedcavities are shallow and the overburden is not tooconductive, GPR would arguably be the solution of choicebecause the method lends itself to fast data acquisition rates.

f fFigure 2b shows an example from a Chinese coalfield whereGPR was used to successfully detect previously mined-outzones as well as ’rat-holing’ caused by such mining cavities.It should be noted that at many local sites affected by near-surface cavities, safety considerations may dictate that anygeophysical surveying should be done from aerial platformsrather than by ground surveys,. However, it is technicallyfairly challenging to conduct high-resolution surveys at fineenough line and station spacings and at low enough altitudesin order to achieve the required metre-scale resolution.

Detection of intrusive dykes and sills

The value of airborne magnetic and EM methods is wellknown for the mapping of regional-scale structures. Theexample presented in Figure 3 is the result of a CoaltechResearch Organisation project that was aimed at merging allavailable aeromagnetic data-sets for the Witbank Coalfield.



Table I

Geophysics applicability matrix for pertinent coal mining and exploration problems

Exploitable property Typical required Typical required Most applicable Workable survey contrast range resolution geophysical methods geometry

Delineation of old workings Air-filled: density, 0–50 m Accuracy < 5 m TDEM Grid surveys on surfaceresistivity, conductivity Micro-gravity

Water-filled: conductivity Resistivity/IP

Near surface cavity detection Voids: density, resistivity, 0–20 m 1–2 m Thermal imaging Grid surveys – ideallyconductivity, dielectric, thermal GPR on low-altitudeFilled: conductivity, resistivity, FDEM / TDEM airborne platform

dielectric Resistivity/IP

Detection of intrusive Magnetic susceptibility OR 0–200 m 3–5 m Magnetics 2D grid surveysdykes and sills electromagnetic properties FDEM/TDEM (usually airborne)

Structural and in-seam Magnetic, electromagnetic, 0–50 m 2–4 m Radio imaging Tomographic imaging orcontinuity disruptions resistivity, conductivity Borehole rada reflection surveys using

Seismic tomography in-seam boreholes /developments

Dolomitic pinnacles / Resistivity, electromagnetic 50–80+ m ~ 5 m FDEM/TDEM Grid surveys on surfaceuneven basement Resistivity imaging

TDEM: Time-domain electromagnetic method IP: Induced polarization methodFDEM: Frequency-domain electromagnetic method GPR: Ground penetrating radar

Figure 1—TDEM depth slice (left) and microgravity contour map (right) revealing the presence of previously mined zones (Styles, 2005)

(a) (b)


Over 40 individual data-sets were merged and the output wasused to perform lineament (dyke) and fault interpretations.

While airborne magnetics is generally well suited todetecting intrusions, the occurrence of non-magnetic dykes inSouth African coalfields is well documented. In such casesone needs to resort to the airborne EM method. Figure 4shows the result of a helicopter-based Dighem survey. TheEM method succeeded in detecting most of the previouslymapped magnetic dykes, as well as other dykes that were notevident on the corresponding magnetic image.

Structural and in-seam continuity disruptions

If it is possible to straddle a to-be-mined block with two linesof co-planar access; for example, two adjacent in-seam

developments or boreholes, it is possible to apply atomographic imaging approach to search for any localizedseam disruptions. The example shown in Figure 5 is from aUS coal mine where the radio imaging method (RIM) wassuccessfully applied between developments to map thecontinuity of coal seams within longwall panels. The extentof sandstone palaeochannels in the coal seam ahead ofmining could be inferred from the RIM survey results. Due to

▲


Figure 4—Dighem aeromagnetic image (top) and schematic of surveyarea (bottom) showing the location of magnetic and non-magneticdykes (Du Plessis and Saunderson, 2000)

Figure 3—Total field aeromagnetic image for the Witbank Coalfield (DuPlessis, 2006)

Figure 2—FDEM contour map (top) and GPR section revealing the presence of a buried mine shaft and near-surface cavities related to coal mining

(Donnelly and McCann, 2000)

(a)

(b)(Hu et al., 2012)

res

their relatively high conductivity the sandstone channels areassociated with an increase in radio wave attenuation, andthis is depicted by the warmer colours in the image.

DDolomitic pinnacles/uneven basement

The final case study example relates to the mapping ofuneven floor conditions – typically associated with dolomiticbasement structures. Unweathered dolomite usually has arelatively high electrical resistivity compared to overlyingshale, coal, and sandstone layers. Variations in the floortopography such as pinnacles and depressions can be imagedusing either the EM or resistivity method. Figure 6 shows anexample of the application of the 2D electrical resistancetomography (ERT) method to this type of problem. It shouldbe noted that the mapping accuracy of the surface throughthe ERT method decreases with increasing depth, and fordeeper basements it may be better to resort to the TDEMmethod.

Conclusions and recommendations

Geophysics can play a significant role in addressing a widerange of coal mining and exploration problems. The primaryadvantage of using geophysics is that it often provides a non-invasive way of obtaining quantitative information about thesubsurface geological structure and of potentially hazardousground conditions. The application of geophysics can thuscontribute to optimizing extraction and to mining safety:

well-planned geophysical surveys can provide advancewarning of any deviations from the anticipated coal seamcontinuity and of any geological or secondary features thatmay constitute a production or safety hazard. However, toextracting maximum useful information from geophysicalsurveys it is essential to select the most appropriate methodto apply to a given problem, as each method has its ownstrengths, weaknesses, and niche applications.

Finally, it should be noted that significant advances arecontinually made in various geophysical technologies. Forexample, enhancements in electronics, computer hardware,and software algorithms have enabled the acquisition andprocessing of significantly larger data-sets at much higherproductivity rates and with better accuracies than waspossible a decade or two ago. Consequently, advancedtechniques such as 3D data acquisition and 3Dforward/inverse modelling have become reality. It is thereforestrongly advocated that coal industry practitioners remainup-to-date with the latest developments in geophysicalresearch and development; this knowledge will aid them inthe optimal selection and application of geophysical methods.

References

DONNELLY, L.J. and MCCANN, D.M. 2000. The location of abandoned mine

workings using thermal techniques. Engineering Geology, vol. 57.

pp. 39–52.

DU PLESSIS, G.P. 2008. The relationship between geological structures and

dolerite intrusions in the Witbank Highveld coalfield, South Africa. MSc

thesis, University of the Free State, Bloemfontein.

DU PLESSIS, S.J. 2006. The merging of the aeromagnetic data of the Witbank

Coalfield. Coaltech Research Association, Johannesburg.

DU PLESSIS, S.J. and SAUNDERSON, R.D. 2000. The successful prediction of non-

magnetic dykes using a Dighem survey. Coal Indaba, Johannesburg,

15–16 November 2000. Fossil Fuel Foundation, Johannesburg.

HU, M-S., PAN, D-M., DONG, S-H., and LI, J-J. 2012. GPR response character-

istics of shallow loose coal seam. Geophysical and Geochemical

Exploration, vol. 36, no. 4. pp. 599–606.

LANHAM, A. 2004. New Vaal gears up to meet power demand. Mining Weekly, 3

September 2004. http://www.miningweekly.com/print-version/new-vaal-

gears-up-to-meet-power-demand-2004-09-03

STOLARCZYK, L.G., PENG, S., and LUO, Y. 2003. Imaging ahead of mining with

RIM-IV instrumentation and 3-D tomography software. 22nd

International Conference on Ground Control in Mining, Morgantown, WV,gg

5–7 August 2003.

STYLES, P. 2005. High resolution microgravity investigations for the detection

and characterisation of subsidence associated with abandoned coal, chalk

and salt mines. Post-Mining 2005, Nancy, France.

VANVV SCHOOR, M. and FOURIE, C.J.S. 2014 (eds.) (in press). A guide for applying

geophysics to coal mining problems in South Africa. Randomstruik. Cape

Town.

ZHOU, W., BECK, B.F., and STEPHENSON, J.B. 2000. Reliability of dipole-dipole

electrical resistivity tomography for defining depth to bedrock in covered

karst terranes. Environmental Geology, vol. 39, no. 7. pp. 760–766. ◆



Figure 5—Example of a successful in-seam RIM survey from Pittsburgh,USA (Stolarczyk et al., 2003)

Figure 6—2D resistivity image showing the topography of a dolomiticbasement (Zhou et al., 2000)

2015

28 September 2015 · Workshop29 September–1 October 2015 · Conference

2 October 2015 · Technical Visits

Misty Hills, Gauteng, South Africa

The Southern African Institute of Mining and Metallurgy (SAIMM), theCanadian Institute of Mining, Metallurgy and Petroleum (CIM) and theAustralasian Institute of Mining and Metallurgy (AusIMM) will jointlyconvene a World Gold Conference every two years. In 2015 it will be heldin Johannesburg, South Africa and hosted under the auspices of theSAIMM. Some important aspects of the current mining environment willprovide opportunities and threats for the industry in the foreseeable future,which include:

➺ Gold price volatility

➺ Minimal exploration success for the last 10 years and little immediateprospect for revolutionary success is leading to revisiting known old(sub-marginal) deposits

➺ Lower precious metal content

➺ Increasing refractoriness

➺ More energy efficient mining and processing

➺ Decreasing equity risk for juniors and mid-tiers

➺ Maximising long-term optionality

Incorporating geology, metallurgy and mining

For further details contact:SAIMM, Head of Conferencing,

Raymond van der BergTel: 27 (11) 834-1273/7

Facsimile 27 (11) 838-5923E-mail: [email protected] ·

Website: www.saimm.co.za

TOPICSWorld Gold 2015 will reflect on thesekey issues with greater focus onimproved efficiency and the latesttechnology in:

➺ geological mapping

➺ gold mining

➺ mineral processing, and

➺ extraction and refining

➺ human resources

➺ financial resources

➺ computer assisted explorationtargeting in gold exploration

➺ application of partial extractionmethods in gold exploration

➺ geochemical and/or mineralogicalhaloes as indicator for gold targets

➺ Black Smokers as explorationtargets for gold

➺ are there still mega gold depositswaiting to be discovered?

➺ Brownfields gold exploration, assuccess story.

Thus, we are focusing on moreenvironmentally friendly, resourceefficient and energy efficient miningand recovery methods.

PRE-CONFERENCEWORKSHOPSPre-conference workshops willbe organised in conjunction withWorld Gold 2015:

Geometallurgy Workshop

Gold Processing Workshop

SPONSORSHIPSponsorship opportunities are available.Companies wishing to sponsor or exhibitshould contact the Conference Co-ordinator. Conference Announcement

The Canadian Institute of Mining,Metallurgy & Petroleum

Building a Resilient Gold Mining Industry

Sponsor:

Conference Chair:Andries Swart

AngloGold Ashanti

The Journal of The Southern African Institute of Mining and Metallurgy OCTOBER 2014 ▲vii

20–24 October 2014 — 6th International Platinum ConferenceSun City, South Africa

E-mail: [email protected]

3–7 November 2014 —


12 November 2014 —


18–19 November 2014 —

19–20 November 2014 —

Emperors Palace, Hotel Casino Convention Resort, Johannesbur

11–13 March 2015 —


8–10 April 2015 — 5th Sulphur and Sulphuric Acid 2015ConferenceSouthern Sun Elangeni Maharani KwaZulu-Natal, South Africa


12–13 May 2015 —


27–28 May 2015 —


9–10 June 2015 —


14–17 June 2015 —

14–17 June 2015 —

16–20 June 2015 —

6–8 July 2015 — Copper Cobalt Africa IncorporatingThe 8th Southern African Base Metals ConferenceZambezi Sun Hotel, Victoria Falls, Livingstone, Zambia


13–15 July 2015 — Production of Clean SteelEmperors Palace, Johannesburg


15–17 July 2015 — Virtual Reality (VR) applications in themining industry


11–14 August 2015 —


28 September-2 October 2015 — Misty Hills Country Hotel and Conference Centre,Cradle of Humankind,


12–14 October 2015 —


28–30 October 2015 —


8–13 November 2015 —

Raj Singhal, E-mail: [email protected] or E-mail: [email protected], Website: http://www.saimm.co.za

INTERNATIONAL ACTIVITIES

▲

viii OCTOBER 2014 The Journal of The Southern African Institute of Mining and Metallurgy

Company AffiliatesThe following organizations have been admitted to the Institute as Company Affiliates

2014◆

6th International Platinum Conference20–24 October 2014, Sun City, South Africa

◆

12 November 2014,

◆

19–20 November 2014, Emperors Palace, Hotel CasinoConvention Resort, Johannesbur

2015◆

11–13 March 2015,

◆Sulphur and Sulphuric Acid 2015 Conference

8–10 April 2015, Southern Sun Elangeni Maharani KwaZulu-Natal, South Africa

◆

12–13 May 2015, Johannesburg, South Africa

◆

9–10 June 2015,

◆Copper Cobalt Africa IncorporatingThe 8th Southern African Base Metals Conference6–8 July 2015, Zambezi Sun Hotel, Victoria Falls,Livingstone, Zambia

◆Production of Clean Steel13–15 July 2015, Emperors Palace, Johannesburg

◆Virtual Reality (VR) applications in the mining industry15–17 July 2015

◆

11–14 August 2015,

◆

28 September-2 October 2015,

Forthcoming SAIMM events...

For further information contact:Conferencing, SAIMM

P O Box 61127, Marshalltown 2107Tel: (011) 834-1273/7

Fax: (011) 833-8156 or (011) 838-5923E-mail: [email protected]

F

Website: http://www.saimm.co.za

EXHIBITS/SPONSORSHIP

Companies wishing to sponsor

and/or exhibit at any of these

events should contact the

conference co-ordinator

as soon as possible

Integrated systems of support

+27 11 494 6000www.ncm.co.za

Applying Poka Yokesin the mining industry