Econ. Environ. Geol. 2005; 38(6): 663-673

Published online December 31, 2005

© THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY

Prediction of the Gold-silver Deposits from Geochemical Maps - Applications to the Bayesian Geostatistics and Decision Tree Techniques

SangGi Hwang1* and PyeongKoo Lee2

1Department of Civil & Geotechnical Engineering, Paichai University, Daejeon 302-735, Korea
2Geological & Environmental Hazards Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 305-350, Korea

Correspondence to :

SangGi Hwang

sghmap@pcu.ac.kr

Received: July 19, 2005; Accepted: November 29, 2005

Abstract

This study investigates the relationship between the geochemical maps and the gold-silver deposit locations. Geochemical maps of 21 elements, which are published by KIGAM, locations of gold-silver deposits, and 1:1,000,000 scale geological map of Korea are utilized for this investigation. Pixel size of the basic geochemical maps is 250m and these data are resampled in 1km spacing for the statistical analyses. Relationship between the mine location and the geochemical data are investigated using bayesian statistics and decision tree algorithms. For the bayesian statistics, each geochemical maps are reclassified by percentile divisions which divides the data by 5, 25, 50, 75, 95, and 100% data groups. Number of mine locations in these divisions are counted and the probabilities are calculated. Posterior probabilities of each pixel are calculated using the probability of 21 geochemical maps and the geological map. A prediction map of the mining locations is made by plotting the posterior probability. The input parameters for the decision tree construction are 21 geochemical elements and lithology, and the output parameters are 5 types of mines (Ag/Au, Cu, Fe, Pb/Zn, W) and absence of the mine. The locations for the absence of the mine are selected by resampling the overall area by 1km spacing and eliminating any resampled points, which is in 750m distance from mine locations. A prediction map of each mine area is produced by applying the
decision tree to every pixels. The prediction by Bayesian method is slightly better than the decision tree. However both prediction maps show reasonable match with the input mine locations. We interpret that such match indicate the rules produced by both methods are reasonable and therefore the geochemical data has strong relations with the mine locations. This implies that the geochemical rules could be used as background values of mine locations, therefore could be used for evaluation of mine contamination. Bayesian statistics indicated that the probability of Au/Ag deposit increases as CaO, Cu, MgO, MnO, Pb and Li increases, and Zr decreases.

Keywords gold-silver deposit, prediction of mining location, geochemical maps, Bayesian statistics, decision tree technique

Article

Econ. Environ. Geol. 2005; 38(6): 663-673

Published online December 31, 2005

Copyright © THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY.

Prediction of the Gold-silver Deposits from Geochemical Maps - Applications to the Bayesian Geostatistics and Decision Tree Techniques

SangGi Hwang1* and PyeongKoo Lee2

1Department of Civil & Geotechnical Engineering, Paichai University, Daejeon 302-735, Korea
2Geological & Environmental Hazards Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 305-350, Korea

Correspondence to:

SangGi Hwang

sghmap@pcu.ac.kr

Received: July 19, 2005; Accepted: November 29, 2005

Abstract

This study investigates the relationship between the geochemical maps and the gold-silver deposit locations. Geochemical maps of 21 elements, which are published by KIGAM, locations of gold-silver deposits, and 1:1,000,000 scale geological map of Korea are utilized for this investigation. Pixel size of the basic geochemical maps is 250m and these data are resampled in 1km spacing for the statistical analyses. Relationship between the mine location and the geochemical data are investigated using bayesian statistics and decision tree algorithms. For the bayesian statistics, each geochemical maps are reclassified by percentile divisions which divides the data by 5, 25, 50, 75, 95, and 100% data groups. Number of mine locations in these divisions are counted and the probabilities are calculated. Posterior probabilities of each pixel are calculated using the probability of 21 geochemical maps and the geological map. A prediction map of the mining locations is made by plotting the posterior probability. The input parameters for the decision tree construction are 21 geochemical elements and lithology, and the output parameters are 5 types of mines (Ag/Au, Cu, Fe, Pb/Zn, W) and absence of the mine. The locations for the absence of the mine are selected by resampling the overall area by 1km spacing and eliminating any resampled points, which is in 750m distance from mine locations. A prediction map of each mine area is produced by applying the
decision tree to every pixels. The prediction by Bayesian method is slightly better than the decision tree. However both prediction maps show reasonable match with the input mine locations. We interpret that such match indicate the rules produced by both methods are reasonable and therefore the geochemical data has strong relations with the mine locations. This implies that the geochemical rules could be used as background values of mine locations, therefore could be used for evaluation of mine contamination. Bayesian statistics indicated that the probability of Au/Ag deposit increases as CaO, Cu, MgO, MnO, Pb and Li increases, and Zr decreases.

Keywords gold-silver deposit, prediction of mining location, geochemical maps, Bayesian statistics, decision tree technique

    KSEEG
    Apr 30, 2024 Vol.57 No.2, pp. 107~280

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