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Data Analysis Strategy for Lithogeochemistry: An Approach to the Study of Down-hole Profiles

by Michel Mellinger1

'Ihe Saskatchewan Research council's Geochemistry and Data Analysis project emphasizes the developnent of expertise in basic methodology and the application of lithogeochemistry to mineral exploration. Further developnents in the area of corrputer graphics applied to the analysis of geochemical data are noted here. In addition, results obtained in two areas of investigation are briefly reported.

Graphics Software for Data Analysis

The computer environment in which graµ1ics software for data analysis is developed at SRC involves the integrated use of a mainframe computer for the handling of data management and processing, and of a stand-alone graphics workstation for the interactive graphical analysis of data and of results frcm data analysis (Mellinger, 1982).

The software that was designed and developed performs the following functions:

1) data transfer from the mainfraire environment to the graphics workstation;

2) data file management, including the creation of data sub-files and the quick examination of data files using si.rrple statistics and contingency tables;

3) detailed univariate analysis of the data using histograms;

4) detailed bivariate analysis of the data using bivariate plots;

5) detailed multivariate analysis of the data using factor plots and their associated information.

A series of SRC reports presenting the software design and usage of these computer programs is intended to be published shortly (Smith and Mellinger, in prep.).

A Data Analysis Strategy for Lithogeochemistry

The analysis of geochemical data involves the interpretation of data in terms of the many parameters that influence the geochemical behaviour of the elements. In order to

lResources Sector, Saskatchewan Research council, Saskatoon.

arrive at a relevant interpretation of the data, the geochemist must use some data analysis strategy. The strategy that has been developed at SRC for lithogeochemistry is based on a multivariate approach, integrating petrogenetic information with chemical data.

The elements that may be analyzed in a rock sample are classified either as najor or trace elements. Because we are interested in the study of element behaviour during geological processes, najor and trace elements are defined following the paragenetic-thenrodynamic approach of Korzhinskii (1959). ~1ajor elements are those which are essential constituents of the minerals present in the samples under study; they are 'framework' elements. Trace elements are those which occur in small quantity and which do not cause the appearance of new mineral phases; they are 'substitution' elements. '!he main parameters for major and trace element behaviour are surrmarized in Fig. 1, together with the expected direct causes for variations in their concentration in rocks.

The general data analysis and interpretation strategy that proceeds from such considerations is represented schematically in Fig. 2. Major and trace elements, selected on the basis of the petrography of the samples under study, are investigated

Major elements Trace elements

(Definition)

"framework" elements "substitution" elements

(Behavior Parameters)

chemical potentia I abundance/ availabillty mineral stability interaction with crystal

structures

(Direct Causes for Variations in Data)

variations in mineralogy variations in mineralogy & in availability (rock speclalizatlon) & in crystallization sequence (geological process signature)

Figure 1 - The distinction between major and trace elements in lithogeochemistry.

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Study variation patterns in data

/ ~ For major elements For trace elements

Varialloa, ,! mioeralog~ j •=• !,oelall,atioa

Geological process signature

Figure 2 - A general strategy for the analysi s and interpretation of lithogeochemical data.

separately for data variation patterns. A descriptive factor analysis method known as correspondence analysis is used (Benzecri et al., 1980). A factor space is obtained for each group of elements.

The major element factor space displays data structures related to mineralogical variations in the samples. At this stage, petrographic knowledge of the samples is required, so as to ensure relevant interpretation of the data structures with respect to petrography and to the geological processes that affected the samples.

The trace element factor space displays data structures arising from various causes. Firstly, variation patterns related to mineralogical (major element) variations are identified. In order to do this, the major elements are treated as supplementary variables and are projected into the trace element factor space; major element variations can then be located in this factor space and be related to sorre of the trace element variation patterns. Similarly, treating trace elements as supplementary variables when calculating the major element factor space helps clarify the degree and the nature of the overlap between trace and major element variation patterns. Secondly, the portion of the trace element factor space that cannot be correlated with mineralogical variations is interpreted in tenns of rock specialization and of the geochemical signature of the relevant geological processes. Here again, useful inf ormation is generated only if the geochemist has a good knowledge of the geological history of the area under investigation; in this case, trace element geochemistry provides unique information.

A more complex description of this data analysis strategy, including the use of various data coding schemes and an example of application, has been sul:xnitted for publication elsewhere (Mellinger, 1983a).

The Multivariate Analysis of IbWn-hole Profiles

When studying chemical analyses of drill core samples from exploration drill.holes, the explorationist is faced with several difficulties: (1) the chemical data may require some processing before useful information can be identified; (2) down-hole variations may result from a combination of background (primary) and alteration (secondary) characteris tics; and (3) comparing and characterizing down-hole alteration profiles may be quite subjective.

'Ihe suggested approach for dealing with these difficulties is sumnarized in Fig. 3, using major elements. Firstly, variation patterns in the chemical data are extracted using correspondence analysis (Fig. 3, step a). This results in a set of factor coordinates, each of which is related to specific variations in the mineralogy of the samples under study (see above). I.et us assurre that, in the present case, factor F3 quantifies hydrothermal alteration (e.g. the illitejkaolinite ratio in samples from the Athabasca Group in northern Saskatchewan). In the next step, down-hole profiles of the F3 coordinate are calculated (Fig. 3, step b) for both background (B) and non-background (X) drillholes. As only true alteration profiles are sought, r esidual down-hole profiles are calculated as a series of positive or negative values (hatched areas between the Band X profiles, in Fig. 3). 'Ihe resulting data table consists of

~amp le #I

s.om ple #-n

[ wit19h1 'Y. do ta J

+ [down-hoJe an~roho.r, profiles]

Fl·· .r;;i sa mp le *' I I

sample#, LJ [,C] ctor coordi na te$ J

f tac tor iaJ p lane

[ a1tera1ion p ro fil e r,-pe:s]

· · F6

Figure 3 - Suggested approach to r the characterization of down­hole alteration profiles (see text for explanatio ns).

drillholes "measured for" successive residual values located at regular intervals along the drillhole axis. This new data table is sul::imitted to correspondence analysis (Fig. 3, step c) , which produces a topology of the types of down-hole alteration profiles in one or more factorial plots. en a factorial plot, each drillhole is represented as a symbol, and each depth interval at which a residual value was calculated is represented by two variables, one for µ::>sitive residuals and the other for negative residuals.

This approach was applied to exploration data from the southeastern part of the Athabasca Basin (Mellinger, 1983b). It was successful in characterizing various types of down-hole alteration profiles related to the illitization and kaolinitization of sandstone samples from the Athabasca Group.

References

Benzecri, J.P. et al. (1980): L'Analyse des Donnees - Vol. 2: l'Analyse des Correspondances (3e edition); ().mod (Par is) , 632 p.

Korzhinskii, D.S. (1959): Physico-chernical Basis of the Analysis of the Paragenesis of Minerals; Consultant Bureau Inc., New York, 142 p.

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tEllinger, M. (1982) : New develoµnents in SRC:'s lithogeochemistry program; in Sununary of Investigations 1982, Sask. Geol. Surv., Misc. Rep. 82-4, p. 60-63.

Mellinger, M. (1983a): The application of correspondence analysis to the study of lithogeochemical data: general strategy and the usefulness of various data coding schemes; Sask. Res. Counc., Puhl. R-740-6-A-83 (submitted for publication in the Proceedings of the 10th IGES, Helsinki, 1983).

Mellinger, M. (1983b) : The evaluation of lithogeochernical data using multivariate analysis: an application to the exploration for uranium deposits in the Athabasca Basin of Saskatchewan (canada); Sask. Res. Counc., Puhl. R-740-7-D-83 (sul::imitted for publication in the Proceedings of~ '84, London, 1984).

Smith, J,W.J. and llEllinger, M. (in prep.): Graphics Software for Cata Analysis (a series of SRC publications); Sask. Res. Counc. , to be published 1983/84. ·