science of targeting

Science of targeting: definition, strategies, targeting andperformance measurement

J. M. A. HRONSKY1* AND D. I. GROVES2

1BHP Billiton, Level 34 Central Park, 152 – 158 St Georges Terrace, Perth, WA 6000, Australia.2Centre for Exploration Targeting, School of Earth and Geographical Sciences, University of Western Australia,35 Stirling Highway, Crawley, WA 6009, Australia, and Redstone Resources Limited, Suite 3, 110 – 116 EastParade, East Perth, WA 6004, Australia.

Mineral exploration comprises three sequential steps: development of a business strategy, creation andapplication of a targeting model, and follow-up with direct detection in defined high-priority domains.The main geoscientific challenge is the conceptual targeting phase which can lower geological riskand ensure cost-effective direct-detection exploration. A fundamental tenet of conceptual targeting isthat ore deposits are part of much more extensive systems, and hence that targeting must be carriedout at global through province to district scales. The heterogeneous distribution of ore deposits andtheir power-law size frequency distribution in individual provinces leads to alternative ‘ElephantCountry’ and ‘First Mover’ strategies, both of which employ conceptual targeting, but at differentscales. The first stage of targeting science involves development of robust, multi-scale targeting modelsfor ore-deposit types, particularly larger examples. The targeting models can then be applied toidentify specific targets by interrogating databases compiled as layers of spatially referenced keythemes or parameters. At larger scales in immature terrains, a Hierarchical approach is commonly usedto progressively reduce terrains and identify targets, whereas a Venn-diagram approach, the basis ofmost GIS-based prospectivity analyses, is more commonly used in mature terrains where spatialdatabases are of higher, more homogenous quality. Target ranking is best achieved using amultiplicative probability approach in which it is required that all essential processes in a mineralsystem must have operated to form a significant ore deposit. In practice, one or more critical spatiallyreferenced parameters are used as proxies for the essential processes to develop a target score, whichis a semi-quantitative estimate of probability of the presence of a large ore deposit. Such target rankingcan be used in both proactive ground acquisition and reactive submittal-based project acquisition.Once targets have been defined and explored, it is important that there is critical feedback on therobustness of the targeting exercise such that new information is used to build superior databases and/or targeting models for future area-selection programs.

KEY WORDS: area selection, mineral exploration, ore deposit, targeting.

INTRODUCTION

Mineral exploration, comprising an initial targeting

stage (prediction) followed by a direct detection stage

(detection), does not enjoy steady-state success. Rather,

historically, there have been periods of accelerated

discovery of mineral resources followed by prolonged

periods where fewer major discoveries are made. This

has been due largely to the periodic development of

new techniques (e.g. ground geophysics, airborne

geophysics, satellite-based remote sensing methods,

low detection-limit geochemistry, new drilling meth-

odologies) that significantly improved direct detection,

with some, particularly the airborne techniques, also

indirectly aiding targeting via the generation of better

constrained spatial geological datasets.

The late 1990s and early 2000s represented a period

of significantly decreased discovery of world-class

deposits despite increased exploration expenditure

early in the period (Schodde 2004). This most likely

indicates that technologies currently deployed in ex-

ploration are reaching maturity and are increasingly

incapable of penetrating the deeper or more complex

cover below which the undiscovered deposits lie.

Clearly, there is a need to improve critical direct-

detection technologies (e.g. 3D integrated imaging, more

effective lower cost drilling, refined mobile-ion geo-

chemistry), but there is arguably a more pressing

need to develop more-sophisticated and predictive

conceptual-targeting methodologies to ensure that

the new improved technologies are applied in the

mineral districts with the greatest potential of hosting

*Corresponding author and present address: Western Mining Services (Australia) PL, Suite 26/17 Prowse Street, West Perth WA 6005,

Australia ([email protected]).

Australian Journal of Earth Sciences (2008) 55, (3 – 12)

ISSN 0812-0099 print/ISSN 1440-0952 online � 2008 Geological Society of Australia

DOI: 10.1080/08120090701581356

world-class ore deposits that meet modern metallurgical

and environmental standards.

This paper describes and discusses this targeting

process as a prelude to most of the following papers,

which illustrate varying approaches to conceptual

targeting, or methodologies that support it, for specific

metallic commodities or for particular mineralised

terrains.

SCIENCE OF EXPLORATION TARGETING

Hronsky (2004) argued that exploration targeting was

not simply the application of concepts, including both

genetic and deposit models, from economic geology

research, but was rather the integration of such

concepts with those derived from other fields, such as

geophysics, spatial analysis, mineral economics, deci-

sion science and probability theory, to deliver a busi-

ness outcome. It is thus a high-level scientific discipline

in its own right, being the critical conceptual phase in

the semi-quantitative prediction of the probability of ore

occurrence which leads to decisions on area selection. If

it is done poorly, it is irrelevant how efficient

and effective are the following detection stages of

exploration.

It is important to recognise that exploration targeting

lies at the pinnacle of scientific endeavour in applied

economic geology. This recognition provides a focus for

the type of outputs that are required from the dis-

ciplines, such as economic geology, which feed into the

science of exploration targeting. It also provides a

banner under which activities and tacit knowledge in

the field can be communicated and transferred to the

benefit of the mining and exploration industry. Recogni-

tion of conceptual targeting as a science discipline and

the training of high-quality personnel in its application

will be critical to the success of future, more scientific

and more cost- effective mineral exploration.

The following sections set out very briefly some of the

key elements and concepts in the generic discipline of

exploration targeting.

IMPORTANCE OF SCALE

The final stage of mineral exploration takes place within

a relatively small area that is effectively the deposit-

scale setting of the mineral deposit type sought by that

exploration. This relatively small area has been defined

by successive integrated phases of exploration targeting

from global through province to district scale (Figure 1),

progressively involving geodynamic, tectonic and litho-

structural concepts. It is a fundamental concept that the

relative efficiencies of both exploration targeting (pre-

diction) and detection are scale-dependent. Orebodies

tend to form as chaotic manifestations of much more

extensive systems that are inherently more predictable

at larger scales. As the scale increases, the number of

datasets that are required to make a robust scientific

decision on the prospectivity or potential endowment of

a terrain is also inherently lower. At the global scale,

the geodynamic and lithospheric setting within specific

time windows may be all that is required. For example,

orogenic-gold deposits (Groves et al. 1998) in Archean

greenstone belts would be selectively targeted in ca 2.7

Ga belts (Goldfarb et al. 2001) with short tectonic

histories and a thinned lithosphere (Bierlein et al.

2006). However, at the province scale, more specific

tectonic parameters and spatial datasets showing first-

order structures and lithostratigraphic packages will

be required. For example, the provinces most likely to

contain world-class orogenic-gold deposits should

have curvilinear crustal-scale shear zones, dominant

greenschist-facies domains and complex lithostrati-

graphic sequences, preferably with sedimentary

sequences overlying volcanic successions (Groves et al.

2003). From district to prospect scale, more specific

geological data that are spatially located with increasing

accuracy will be required if predictive concepts are to be

used. (Figure 1). Such data are seldom available at the

prospect scale prior to discovery. Thus, exploration

targeting (prediction) is progressively less effective with

decreasing scale and is replaced by detection at the

prospect scale (Figure 2a). As the targeted area is reduced

by decisions at a progressively smaller scale, the

flexibility decreases and costs increase (Figure 2b),

showing that the opportunity cost of poor initial target-

ing is high.

An important constraint on the quality of exploration

targeting decisions is the availability of high-quality

geoscientific data, particularly reliable regional-

geological map datasets supported by robust geochronol-

ogy at the global to province scale and high-resolution

geophysical and remotely sensed datasets at the district

to prospect scale. In developed countries, such as

Australia, Canada and USA, targeting is more effective

than in developing countries, particularly in Africa and

parts of Asia, where data availability is poor or data are

absent. However, datasets are commonly sufficiently

robust to define prospective exploration areas at the

global to province scales. One of the advantages of the

recognition of exploration targeting as a high-level

scientific discipline is that it allows definition of

the critical datasets required and hence can aid in

the formulation of strategic industry – government and

Figure 1 Hierarchy of scale-dependent targeting processes

and parameters to be considered in erection of targeting

models.

4 J. M. A. Hronsky and D. I. Groves

industry – academia alliances to progressively build

those datasets.

AREA SELECTION STRATEGIES

Area selection initially involves a number of business-

related constraints. These include political decisions on

which parts of the world are considered low to high risk

in terms of political and economic stability, which types

of ore deposits are considered suitable exploration

targets based on company philosophy, and the threshold

size that is acceptable for that ore deposit type given

that approximately two-thirds of all mineral wealth is

derived from a small number of world-class to giant

deposits (Schodde & Hronsky 2006). These decisions are

largely based on company size, and are commonly

company specific, so are not considered further. Only

the geological aspects of area selection are discussed

below.

A number of generic features of ore deposits have a

significant impact on the development of area selection

strategies that, in turn, impact on exploration targeting.

First, the spatial and temporal distribution of ore

deposits is extremely heterogeneous (Groves et al. 2005),

such that relatively few geological provinces are very

well endowed, whereas the majority contain only

sporadic mineral deposits. Second, the size-frequency

distribution of mineral deposits within any province

shows a power-law relationship. The orogenic-gold

deposits in the Kambalda – St Ives district are shown

as an example in Figure 3. Individual provinces also

show characteristic power-law size-frequency distribu-

tions. For example, the Abitibi gold province of Canada

and Eastern Goldfields gold province of Western Aus-

tralia have about an order of magnitude more deposits

at any given large size range than other Archean

greenstone belts globally (Goldfarb et al. 2001). Petro-

leum basins exhibit similar relationships, with the

Persian Gulf province also having about an order of

magnitude more producing wells of a given reservoir

size than in any North American basin. Third, the areal

extent of the geological halo, or ‘footprint,’ of an ore

deposit represents the size of the ore system and is

generally proportional to the size of the ore deposit

within it. The implication is that the larger deposits are

generally discovered first in any district or province if

all other factors are broadly equivalent. The history of

discovery of the komatiite-associated Ni – Cu sulfide

deposits of the Kambalda region, a newly recognised

class of deposit in 1966 (Woodall & Travis 1969), is shown

as an example in Figure 4. Similar patterns are

recognised throughout Australia, for example in the

historically important Broken Hill and Mt Isa base-

metal provinces, the Kalgoorlie gold province, and the

Hamersley iron province.

The two main conclusions that can be drawn from

the above are: (i) the best chances of discovery of a

world-class deposit are to explore where world-class

deposits are already known; and (ii) the largest deposits

are generally discovered in the early phases of mineral

exploration of a terrain. These conclusions provide the

basis for the two basic area-selection strategies that are

most successfully employed in mineral exploration: the

‘Elephant Country’ strategy and the ‘First Mover’ (or

‘Fast Follower’) strategy.

The Elephant Country strategy essentially exploits

the characteristic size-frequency distributions of metal-

logenic provinces. To be successful, several criteria

need to be met. First, the targeted ore-deposit type must

be one that actually clusters in provinces. Most deposits

do, but there are some important exceptions such as

Broken Hill-type base-metal mineralisation which tends

to occur in one, or at most three, extremely large,

isolated deposits in a terrain with otherwise small,

relatively insignificant deposits (Walters 1996). Second,

the known endowment characteristics of the province

must satisfy corporate objectives in that it is a high-risk

strategy to expect to discover a deposit that is much

larger than has already been discovered in the province.

Third, the targeted province must have some residual

potential such as extension of prospective environments

under inadequately tested transported cover. Finally,

the exploration group must be able to perceive some

competitive advantage in the province (e.g. improved

deposit and/or regolith model or more sensitive geo-

chemical or geophysical technique) as Elephant Coun-

try provinces are normally highly competitive in terms

of ground acquisition.

Figure 2 Contrast between global- to district-scale targeting

and project-scale exploration. (a) Relative effectiveness of

prediction based on targeting model and direct-detection

exploration. (b) Relative flexibility and cost of prediction

based on targeting model and project exploration illustrat-

ing the high cost of poor targeting.

Science of targeting 5

The First Mover (or Fast Follower) strategy seeks to

exploit the concept that the largest deposits will be

discovered in the earliest exploration phase, and there-

fore by the first exploration groups to move into the

province. To be successful, two criteria need to be

fulfilled. First, the exploration group must have the

scientific capability to recognise new exploration en-

vironments with high endowment potential together

with the ability to rapidly acquire ground within them.

Second, they must be assured that the targeted province

is genuinely immature in terms of its exploration

history, either because it has not been explored

previously, due to its geographic position or commer-

cial/political issues such as native title, or because the

introduction of new deposit models or exploration and/

or metallurgical technologies presents new opportu-

nities. For example, the province may be under deep and

conductive cover that could not be penetrated by pre-

existing geochemical or geophysical methods, or

known deposits could have been unresponsive to known

metal-extraction techniques. An example of how new

concepts and technology can revitalise a terrain is

shown for porphyry copper deposits in Chile in Figure 5.

Exploration companies must choose whether they

wish to employ one or both of these strategies. In some

cases, the choice of ore deposit type that is targeted will

dictate strategy. The Elephant Country approach re-

quires the company to have financial capability and

commercial skills to acquire ground in a competitive

environment and to have enhanced competence in

exploration technology applied to direct detection.

Conceptual targeting plays a role only in selection of

the best possible, yet affordable, ground at the district

scale in Elephant Country. In contrast, the key driver

for the First Mover (or Fast Follower) strategy is high-

calibre conceptual targeting at the global to province

scale (Figure 1) to recognise the geological opportunity

in an immature terrain, although this will commonly

be integrated with the political skills required to

recognise increasing stability, and have lower risk,

Figure 3 Endowment (current re-

serves plus cumulative produc-

tion) of orogenic-gold deposits in

the St Ives region of Western

Australia showing typical power-

law size frequency of deposits at

the district scale. Source: WMC

Resources 2000 company data.

Figure 4 Endowment (current re-

serves plus cumulative produc-

tion) of komatiite-associated

nickel – copper deposits in the

Kambalda region of Western Aus-

tralia against the year of discov-

ery, illustrating that the larger

deposits tend to be discovered

early in the exploration history

of a region. Source: WMC Re-

sources 2000 company data.


in a developing country or one emerging from political

or social instability. Thus, conceptual targeting plays a

vital role in both strategies but at different scales.

DEVELOPMENT OF TARGETING MODELS

Targeting models are commonly subdivided into con-

ceptual or process-based, and empirical models, but in

reality the two model types are interrelated. Conceptual

models are commonly based on data inducted from

empirical patterns, and most empirical models are

influenced to some degree by conceptual criteria. The

important general principles in developing targeting

models are set out below.

As developed further below, it is critical to recognise

that different targeting parameters are important at

different scales and to use them at the appropriate scale

in targeting models. If targeting models use data from

ore-formation process models (genetic models), it is

essential that a critical component be defined as a proxy

for the process that can be represented in commonly

available exploration datasets. In other words, the

critical component of the process must be able to be

defined in spatial datasets such as those used to produce

geological maps or aeromagnetic images. It is important

that the datasets be of as uniform quality as possible so

that the areas of superior data quality do not unduly

influence the targeting model, particularly at the

province to district scale. The targeting model should

also focus on those parameters that are essential to the

presence of mineralisation and, as far as possible,

discriminate them from other less-critical parameters,

that are allowable but not essential. Empirical mineral

occurrence data are clearly important to confirm or

deny the existence of the appropriate parameters to

produce the deposit type sought in exploration. How-

ever, it is important that minor mineral occurrences do

not unduly influence the development of the model.

A common weakness of many targeting models,

particularly those of more academic derivation, is that

they focus on prediction of the presence or absence of

mineralisation of a particular deposit type rather than

the probability of occurrence of large economically

important ore systems. However, it is vital for the

mineral industry that the models address factors that

discriminate large from small systems. Again, this

comes back to the scale of conceptual thinking, as

discussed further below. It is noticeable at the global to

province scale that the majority of significant mineral

deposits, particularly the world-class examples, have an

association with major, deep-seated structures that are

probably trans-lithospheric as well as trans-crustal in

scale, as many also lie on, or near, lithospheric

boundaries (Groves et al. 2005). This relationship

appears to be independent of whether the deposit is

syngenetic (either synmagmatic or synsedimentary) or

epigenetic, or the particular deposit type. It is also

apparent that a particular trans-lithospheric structure

may control the distribution of more than one deposit

type over multiple periods of mineralisation. An ex-

cellent example is provided by the so-called Carlin and

Battle Mountain trends of Nevada, USA, where Eocene

Carlin-type deposits are simply the most economic

deposit type in a province of synsedimentary Au – Zn –

Ba, porphyry Cu – Au, epithermal Au – Ag, disseminated

Au and hot-spring Au – Ag deposits that span the time

range from Devonian to Holocene due to reactivation

along major trans-lithospheric structures (Emsbo et al.

in press). A most important consideration is that these

controlling structures are normally not obvious at the

scale of the deposit where most detailed academic

research takes place.

It is clear also that all significant ore bodies are

associated with a tectonothermal system which is

orders of magnitude larger than the immediate deposit

environment. Such a system may be manifested by

major periods of crustal growth (e.g. orogenic-gold

Figure 5 Year of discovery of porphyry copper – gold deposits in Chile, illustrating that the new concepts or new technologies

can rejuvenate discovery in a mature Terrain. Note that the earliest discoveries tend to be some of the largest. Source: WMC

Resources 1999 company data.


deposits: Goldfarb et al. 2001), widespread magmatism of

a specific age in a specific subduction environment (e.g.

porphyry Cu – Au deposits: Billa et al. 2004), or a

particular type of basin-filling package in a specific

structural setting (e.g. SEDEX deposits: Large et al.

2005). Although the genetic links may be somewhat

equivocal, it is nonetheless vital to recognise the

importance of the system concept (Wyborn et al. 1994)

in developing robust targeting models. An important

aspect is to recognise that the first-order parameters in

the targeting model may not be defined or even

discussed in research papers on the deposit type because

of the nature of most economic geology research.

TARGET IDENTIFICATION

Once a targeting model has been developed, it can then

be applied to identify specific targets within a province

or district. In order to achieve this, key targeting themes

or parameters must be compiled as layers of informa-

tion that represent key components of the targeting

model and can be depicted spatially. For most ore

deposit types that are targeted, these will comprise one

or more layers from geodynamic, tectonic, structural,

lithostratigraphic, and metamorphic and igneous pet-

rology datasets, together with available data on known

occurrences of the deposit type sought in the targeting

exercise.

There are two basic end-member approaches to the

process of target identification: the Hierarchical and

Venn-diagram systems (Figure 6). In practice, many

targeting exercises will represent a hybrid between the

two, because the hierarchical approach is commonly

used from the global to province scale, whereas the

Venn-diagram approach is more commonly used at the

district scale.

The hierarchical approach is based on the concept

that most targeting models comprise a series of critical

parameters that are manifested at different scales. Thus,

geodynamic parameters may be used to identify the

segment of a continent or country that is most

prospective (A in Figure 6); tectonic and crustal-scale

structural data may be used to define the most

prospective province (B in Figure 6); and a greater

variety of geological parameters (e.g. structural, basi-

nal, lithostratigraphic, igneous and metamorphic) may

be used to define the most prospective district (C in

Figure 6). The hierarchical approach is more generally

applied in a First Mover strategy when the initial area

of interest is initially extensive and/or the targeting

group has to compile most of the key targeting themes

themselves from primary data where there are poor

spatial datasets, for example in developing countries.

An important concept when targeting in such areas with

sparse data is that of permissible parameters in that if

there is no negative information on the presence of a key

factor, it should be considered neutral in any decision-

making process.

The Venn-Diagram approach is most commonly used

in areas of relatively high exploration maturity with

good quality and relatively uniform data, and is hence

normally linked to the Elephant Country strategy. It

essentially views target identification as the process of

locating areas where there is a conjunction of a number

of critical parameters in the targeting model: the area of

overlap of parameters D, E and F in Figure 6. This

philosophical approach underpins the methodology of

GIS prospectivity mapping (Agterberg 1974; Bonham-

Carter 1994; Brown et al. 2000), several examples of

which are presented in this thematic issue.

TARGET RANKING

The process of target ranking is essentially an extension

of the target identification process but is separated here

for clarity and to emphasise its potential importance.

Traditionally, target ranking has involved the assign-

ment of scores to each of a suite of parameters

considered essential to permissive, and then summing

these scores to provide a total target score. Normally,

the scores are weighted for the perceived relative

importance of each targeting parameters, and perhaps

for the degree of confidence in the assignment of the

score.

More recently, however, an alternative approach has

been proposed by workers such as Henley (1997)

and Lord et al. (2001), who advocate a multiplicative

probability-based approach similar to that used in the

petroleum industry. The probability-based approach to

target ranking is based on distilling the targeting model

down to parameters that are the proxies of critical

processes in the generation of the targeted deposit style.

This approach is described in detail by Kreuzer et al. (in

press). For example, epigenetic hydrothermal deposits

can be described in a minerals system (Wyborn et al.

1994) as involving a source, fluid conduit, trap and cap

or outflow system. In the probability-based approach,

each of these processes must have operated, and each

should be independent of the other. An example of such

a system is shown in Figure 7 for an orogenic-gold

system. In this case, the critical parameters that are

the proxies for the process must be recognised and

allocated to that part of the minerals system. An

example showing the critical proxies for the critical

Figure 6 Schematic diagram illustrating the two end-

member approaches to target definition: the hierarchical

and Venn-diagram methodologies. The former is commonly

used in immature terrains, whereas the latter is used in

mature terrains where it forms the basis for most GIS-based

prospectivity analyses.


parts of an orogenic-gold system are shown in Table 1.

This allocation of proxies or parameters to specific

processes within the minerals system allows formula-

tion of a ranking system that demands that critical

proxies for every process must be present for an area to

be prospective. The ranking system must give equal

weight to the presence of each process, rather than to

each parameter or proxy, and care must be taken that

the parameters are not measuring the same part of the

process and hence are overemphasised in the ranking

procedure. A probability that the critical process

operated in the minerals system is estimated, either

using 1.0 if it is proven to be present, 0 if it proves to be

absent, or 0.5 if there are no data to make a decision, or

using a fuzzy-logic system with ranking of favourability

where more than one critical parameter or proxy

represents one critical process in the system.

There are a number of advantages to this approach. It

clearly defines the critical processes in the targeting

model and the equivalent parameters that proxy for

those processes. It does not excessively penalise targets

where one or more parameters that represent a critical

process are permissive without definitive evidence that

it is present. If a critical process (e.g. source, fluid

Figure 7 Schematic diagram illus-

trating a minerals system model

for orogenic-gold deposits show-

ing source, fluid conduit, deposit

trap and cap or seal (adapted from

Groves et al. 1998). The key para-

meters that are proxies for these

critical processes are shown in

Table 1.

Table 1 Mineral systems model for orogenic-gold deposits.

Key parameter Proxies for parameter

Critical process—Thermal energy and ore-fluid source

Thinned lithosphere Short terrane history (Bierlein et al. 2006)

Major crust-forming event Geochronological data

Orogenic-gold mineralisation Known gold occurrences

Critical process—Plumbing fluid systems

Deep fluid conduits Crustal-scale shear zones preferably with lamprophyres

Focused fluid flow High-strain shear zones in low-strain belts

High-damage zone in lower order faults Jogs, thrust duplexes, fault intersections

Critical process—Trap (depositional) site

Structural trap Locked-up anticline, thrust-tip

Rheological trap Rheologically indexed rock types—contacts with high contrast

Chemical trap Reactive rocks with high Fe or C

Critical process—Fluid seal (cap)

Stratigraphic cap Impermeable rocks over structurally permeable rocks (e.g. sedimentary cap)

Structural cap Impermeable thrust stacks

Critical process—Outflow zone

Fluid dispersion Metal dispersion halos

The table shows the principle of critical processes and some of their key parameters and proxies which can be detected in geological

maps or from geological or geophysical databases normally available to a targeting team in an exploration company. It is illustrative

and is not intended to be a complete list of parameters and proxies at all scales. Adapted from Groves et al. (2000).


conduit, trap or cap) is missing in the minerals system,

the probability of success of that target is zero because

one of the critical processes will rank 0 in the analysis.

It is thus more likely to produce a more realistic and

robust numerical separation between the scores for

potential world-class targets and weaker targets than

other methods. The methodology is also transparent and

has a high scientific integrity.

A weakness of the approach is that it is sensitive to

the nature of the minerals system model that is

developed and hence, in turn, sensitive to the choice

of critical processes and their proxies in terms of key

parameters for the targeting model. Minerals systems

are inherently more complex than petroleum systems,

where the three process steps of source – transport –

trap can be applied universally. The key is to define

those key parameters that are essential for the devel-

opment of a large ore deposit, while preserving some

parameters that are permissive in case there are data

gaps, particularly in immature terrains. In general,

this approach is recommended because it is an

effective and robust way of organising and interrogat-

ing data, it is an effective way of communicating the

targeting outcome, and it allows for a consistent and

transparent approach across multiple targeting exer-

cises in difficult terrains. However, it can never be

truly quantitative, because the number of critical

processes and related key parameters is ultimately a

somewhat arbitrary choice and hence affects the total

probability; few of the key parameters are likely to be

truly independent of one another, as it is necessary for

the methodology; and it is erroneous to assume that

lack of data equates to an equal probability of presence

or absence of a key parameter in spatially heteroge-

neous datasets: it is statistically more likely to be

absent.

As in all scientific endeavours, the quality of the

outcome will depend on the collective knowledge of the

targeting team and the robustness of the targeting

models they produce based on their understanding of

the critical components of minerals systems for a

variety of ore deposit types. The team should also

recognise the common pitfalls in ranking that are

independent of the quality of the targeting model

employed. First, it is important that the ranking

scheme is not biased toward mature exploration areas

of greater data density and therefore excessively

discriminates against areas with high potential endow-

ment but limited data. This typically happens when too

much emphasis is placed on known mineralisation

without adequately considering its context in the total

targeted terrain. Second, it is vital to separate model

parameters from the data that were used to derive

them. For example, the presence of magnetite-rich

bodies may be a critical parameter in several mineral

systems (e.g. hydrothermal iron ore, iron-oxide Cu – Au

or BIF-hosted orogenic-gold deposits), and may be

recognised by the presence of magnetite and/or a

magnetic anomaly and/or a gravity anomaly.

However, it should be treated as a single key para-

meter, not three independent parameters (magnetite,

magnetic anomaly, gravity anomaly) in the ranking

process.

PERFORMANCE MEASUREMENT AND FEEDBACKLOOPS

One of the major deficiencies of currently practiced area

selection is the general lack of follow-up to rigorously

estimate how effective it has been in improving the

probability of discovery of a large ore deposit. Such

quantification of the targeting exercise and team

performance is required to measure the degree of

success, and hence provide a quality control that leads

to improvement in the targeting process.

Rigorous application of the probability-based

approach, outlined above, is an important step towards

this achievement but requires robust calibration with

empirical case-history data. In reality, the exploration

industry seldom allocates resources to post-mortems of

targeting exercises, largely because of the transience of

staff, particularly during boom periods, the competing

lure of the next opportunity, and the relative difficulty

in accessing the relevant historical data. As a conse-

quence, the industry has very little information on

which are the most robust methodologies in exploration

targeting. Case histories in the public domain are

strongly biased towards those of successful exploration

programs, and almost invariably credit success to a

mixture of good teamwork, successful detection and

serendipity, with little emphasis on targeting models

and their value in the process (Australian Mineral

Foundation 1997, 2001).

However, if there are to be major improvements in

targeting capability, there is an urgent need to employ

better quantification of targeting performance. After

targets are generated, it is vital that information

obtained from testing the targets is fed back into the

targeting model and supporting databases. This is

particularly important in less-developed countries

where information obtained from field assessments of

province-scale target areas may have a substantial

impact on both the database and model. As exploration

moves more under cover, targeting models become an

essential tool to focus exploration, increase the geologi-

cal probability that any individual targeted prospect

will become an economic deposit, and hence reduce the

risk:reward ratio.

PROACTIVE CONCEPTUAL TARGETING VSREACTIVE TARGET EVALUATION

Pragmatically, there are two end-member approaches to

acquiring exploration properties. The first is essentially

the process described above, involving compilation of

databases over the area of interest, development of a

targeting model, generation of targets, and proactive

acquisition of ground over the highest ranked targets.

The second is to focus on evaluating properties sub-

mitted by other parties, commonly junior explorers,

followed by acquisition of the most prospective of those

submitted. Commonly, the targeting process is only

considered to be an integral part of the first approach.

However, this is somewhat of a misconception, since

there has to be a logical mechanism to prioritise

submittals. In order to robustly rank the submittals


and capture value for the company, a superior approach

to a submittal-based ground-acquisition program is to

have previously completed a targeting exercise for the

province or district of interest. In this way, any

submitted property can be evaluated more effectively

within the regional context, which largely controls the

potential of the mineral systems, and hence the potential

endowment of the submitted prospect, itself. Hence, all

acquisitions should be based on a framework provided

by the conceptual targeting process.

CONCLUSIONS

The targeting process discussed above is shown sche-

matically as a flow sheet in Figure 8. Following business

decisions that relate to the individuality of the mining

and/or exploration company, area selection is an

essential part of the mineral supply process. Although,

in the past, targeting success has been attributed

commonly to serendipity or favourable organisational

structure, world-class to giant ore deposits are predicted

to be more difficult to discover as districts with shallow

regolith became increasingly mature, and conceptual

targeting will assume increasing importance in the

future. This paper outlines the principles that should be

applied in area selection so that it becomes a better

understood, more effective, and more valued segment of

exploration business. Targeting needs to be recognised

in its own right as a scientific endeavour that integrates

information from the various geoscience disciplines,

translates critical processes and parameters from that

Figure 8 Summary of the targeting process from the business interface through developing the targeting model and spatial

databases to defining and testing targets, with a subsequent feedback loop to improve the targeting model and information in

the databases.


information into spatially registered data layers or

themes, and interrogates those in terms of robust

targeting models to produce at least semi-quantitative

ranking of target areas at the global, province and

district scale. The science of targeting should underpin

all future exploration endeavours in order to reduce

geological risk and provide more cost-effective project

exploration using direct detection technologies and

methodologies.

ACKNOWLEDEGMENTS

JMAH acknowledges many former colleagues in WMC

Exploration Division and present colleagues at BHP-

Billiton, particularly Richard Schodde and Nicholas

Hayward, for contributions to the concepts presented in

this paper. DIG also acknowledges the benefits of

discussions with colleagues from Redstone Resources

and Newmont, particularly Stephen Gardoll and Musie

Gebre-Mariam, who have helped to bring targeting

concepts into better focus. The authors both acknowl-

edge helpful review comments by Mike Etheridge.

REFERENCES

AGTERBERG F. P. 1974. Automatic contouring of geological maps

to detect target areas for mineral exploration. Mathematical

Geology 6, 373 – 395.

AUSTRALIAN MINERAL FOUNDATION 1997. New Generation Gold Mines

‘97: Case Histories of Discovery, Conference Proceedings.

Australian Mineral Foundation, Glenside.

AUSTRALIAN MINERAL FOUNDATION 2001. New Gen Gold 2001, Con-

ference Proceedings. Australian Mineral Foundation, Glenside.

BIERLEIN F. P., GROVES D. I., GOLDFARB R. J. & DUBE B. 2006.

Lithospheric controls on the formation of provinces hosting

giant orogenic gold deposits. Mineralium Deposita 40, 874 – 886.

BILLA M., CASSARD D., LIPS A. L. W., BOUCHOT V., TOURLIERE B.,

STEIN G. & GUILLOU-FROTTIER L. 2004. Predicting gold-rich

epithermal and porphyry systems in the central Andes with

a continental-scale metallogenic GIS. Ore Geology Reviews 25,

39 – 47.

BONHAM-CARTER G. F. 1994. Geographic Information Systems for

Geoscientists—Modelling with GIS (Computer Methods in the

Geosciences 13). Pergamon Press, New York.

BROWN W. M., GEDEON T. D., GROVES D. I. & BARNES R. G. 2000.

Artificial neural networks: a new method for mineral pro-

spectivity mapping. Australian Journal of Earth Sciences 47,

757 – 770.

EMSBO P., GROVES D. I., HOFSTRA A. H. & BIERLEIN F. P. The giant

Carlin gold province: a protracted interplay of orogenic, basinal

and hydrothermal processes above a lithospheric boundary.

Mineralium Deposita (in press).

GOLDFARB R. J., GROVES D. I. & GARDOLL S. 2001. Orogenic gold and

geologic time: a global synthesis. Ore Geology Reviews 18, 1 – 75.

GROVES D. I., CONDIE K. C., GOLDFARB R. J., HRONSKY J. M. A. &

VIELREICHER R. M. 2005. Secular changes in global tectonic

processes and their influence on the temporal distribution of

gold-bearing mineral deposits. Economic Geology 100, 203 – 224.

GROVES D. I., GOLDFARB R. J., GEBRE-MARIAM M., HAGEMANN S. G. &

ROBERT F. 1998. Orogenic gold deposits: a proposed classification

in the context of their crustal distribution and relationship to

other gold deposit types. Ore Geology Reviews 13, 7 – 27.

GROVES D. I., GOLDFARB R. J., KNOX-ROBINSON C. M., OJALA J.,

GARDOLL S., YUN G. & HOLYLAND P. 2000. Late-kinematic timing

of orogenic gold deposits and significance for computer-based

exploration techniques with emphasis on the Yilgarn Block,

Western Australia. Ore Geology Reviews 17, 1 – 38.

GROVES D. I., GOLDFARB R. J., ROBERT F. & HART C. J. R. 2003. Gold

deposits in metamorphic belts: overview of current understand-

ing, outstanding problems, future research and exploration

significance. Economic Geology 98, 1 – 29.

HENLEY R. W. 1997. Risky business: the essential blending of

financial and scientific skills in the modern resources sector.

Window on New Zealand 1997: New Zealand Minerals and Mining

Conference Proceedings, pp. 29 – 33.

HRONSKY J. M. A. 2004. The science of exploration targeting. In:

Muhling J. ed. SEG 2004. Predictive Mineral Discovery Under

Cover, pp. 129 – 133. University of Western Australia, Centre for

Global Metallogeny, Publication 33.

KREUZER O. P., ETHERIDGE M. A., MCMAHON M. E. & HOLDEN D. J.

Probabilistic ore systems modeling: a new tool for quantitative

risk analysis and decision-making in exploration. Economic

Geology (in press).

LARGE R. R., BULL S. W., MCGOLDRICK P. J., WALTERS S., DERRICK G.

M. & CARR G. R. 2005. Stratiform and strata-bound Zn – Pb – Ag

deposits in Proterozoic sedimentary basins, Northern Australia.

Economic Geology 100th Anniversary Volume, pp. 931 – 964.

LORD D., ETHERIDGE M. A., WILLSON M., HALL G. & UTTLEY P. J. 2001.

Measuring exploration success: an alternative to the discovery-

cost-per-ounce method of quantifying exploration success.

Society of Economic Geologists Newsletter 45, 1, 10 – 16.

SCHODDE R. C. 2004. Discovery performances of the western world

gold industry over the period 1985 – 2003. In: PACRIM 2004

Proceedings, pp. 367 – 380. Australasian Institute of Mining and

Metallurgy, Melbourne.

SCHODDE R. C. & HRONSKY J. M. A. 2006. The role of world-class

mines in wealth creation. In: Dogget M. D. & Parry J. R. eds.

Wealth Creation in the Minerals Industry: Integrating Science,

Business, and Education, pp. 71 – 90. SEG Special Publication 12.

WALTERS S. J. 1996. An overview of Broken Hill-type deposits.

University of Tasmania, CODES Special Publication 1, 1 – 10.

WOODALL R. & TRAVIS G. A. 1969. The Kambalda nickel deposits,

Western Australia. 9th Commonwealth Mining & Metallurgy

Congress, 2, pp. 517 – 533.

WYBORN L. A. I., HEINRICH C. A. & JAQUES A. L. 1994. Australian

Proterozoic mineral systems: essential ingredients and map-

pable criteria. In: Australasian Institute of Mining and Metal-

lurgy Annual Conference Proceedings, pp. 109 – 115.

Received 25 August 2006; accepted 28 April 2007


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