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

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 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.

References

  1. 강필종 (1995) 한국 지질도, 축척 1:1,000,000. 한국자원연구소, 성지문화사
  2. 신성천 (2001) 한국 지구화학 지도책(1:700,000), 한국지질자원연구원
  3. Breiman, L., Friedman J.H., Olshen R. and Stone C. (1984) Classification and Regression Trees, Wad-sworth International Group, California
  4. Harris, J.R., L. Wilkinson, J. Broome, and S. Fumerton, (1995) Mineral exploration using GIS-based favour-ability analysis, Swayze greenstone belt, northern Ontario, in Proceedings of 1995 Canadian Geomatics, Ottawa, Ontario Canada
  5. Hwang, S.G., Nguyen Q.P. and Lee P.K. (2005) Reproducibility of a regional geological map derived from geochemical maps, using data mining techniques: with application to Chungbuk province of Korea. Environmental Geology, in press
  6. Wright, D.E and Bonham-Carter G.E (1996) VHMS favourability mapping with GIS-based integration models, Chisel Lake-Anderson Lake area, in : Bonham-Carter, Galley, and Hall (eds.): EXTECHLA mul-tidisciplinary approach to massive sulfide research in the Rusty Lake-Snow Lake greenstone belts, Manitoba. Geolocical Survey of Canada, Bulletin, v. 426, p. 339-376
  7. Quinlan, J.R. (1986) Induction of decision trees. Machine Learning 1, v.1, p. 81-106
  8. Bolviken, B. (1986) Geochemical atlas of northern Fen-noscandia, scale 1:4.000.000. Geological Survey of Sweden, 19pp, 155 maps
  9. Shannon, C.E. and Weaver W (1949) The Mathematical Theory of Communication, University of Illinois Press
  10. Bonham-Carter, G.E, Agterberg, EP. and Wright, D.E (1989) Weights of evidence modelling: a new approach to mapping mineral potential. In: Agterberg, ER, Bonham-Carter, G.E, (Eds.), Statistical Applications in the Earth Sciences. Geological Survey of Canada Paper, 89-9, p. 171-183
  11. Bonham-Carter, G.E (1994) Geographic Information Systems for geoscientists, modeling with GIS. Pergamon Press, Oxford, 398pp
  12. Webb, J.S., Thornton I., Howarth R.J. and Thompson M. (1978) The Wolfson Geochemical Atlas of England and Wales. Clarendon Press, United Kingdom, 69pp
  13. Kass, G.V (1980) An exploratory technique for investigating quantities of categorical data. Applied Statistics 29 v. 2, p. 119-127
  14. Quinlan, J.R. (1993) C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann Publishers
  15. Lahermo, E, Vaananen E, Tarvainen T. and Salminen R. (1996) Geochemical Atlas of Finland, Part 3: Environmental geochemistry - Stream waters and sediments. Geological Survey of Finland, Espoo, 149p
  16. IGS. (1978) Geochemical atlas of Gt. Britain: Shetland Islands. Institute of Geological Sciences, London
  17. Meyer, W.T., Theobald EK., Bloom H. (1979) Stream sediment geochemistry. In: Hood EJ. (ed) Geophysics and geochemistry in the search for metallic ores. Geol Surv Can. Econ. Geol. Rep., v.31, p 411-434
  18. Carranza, E.J.M. and Hale, M. (1999) Geological-constrained probabilistic mapping of gold potential, Baguio District, Philippines. Geocomputation 99, July 2528, Fredericksburg, Virginia, Conference Volume on CD-ROM
  19. Debes J.D. and Urrutia R. (2004) Bioinformatics tools to understand human diseases. Surgery 135, v. 6, p. 579-585
  20. Asadi H.H. and Hale M. (2001) Apredictive GIS model for mapping potential gold and base metal mineralization in Takab area, Iran. Computer & Geosciences v.27, p. 901-912 https://doi.org/10.1016/S0098-3004(00)00130-8