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

지화학자료를 이용한 금${\cdot}$은 광산의 배태 예상지역 추정-베이시안 지구통계학과 의사나무 결정기법의 활용

  • Hwang, Sang-Gi (Department of Civil & Geotechnical Engineering, Paichai University) ;
  • Lee, Pyeong-Koo (Geological & Environmental Hazards Division, Korea Institute of Geoscience and Mineral Resources)
  • 황상기 (배재대학교 토목환경공학과) ;
  • 이평구 (한국지질지원 연구원 지질환경재해 연구부)
  • Published : 2005.12.01


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 far 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 1 km spacing and eliminating my 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 oi 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.


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