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Natural Resources Research

, Volume 15, Issue 1, pp 49–65 | Cite as

Mineral-Potential Mapping: A Comparison of Weights-of-Evidence and Fuzzy Methods

  • Telmo F. P. de Quadros
  • Jair C. Koppe
  • Adelir J. Strieder
  • João F. C. L. Costa
Article

Guidelines for mineral exploration normally are based on models for a given type of ore deposit. These guidelines usually are based on descriptive and metallogenetic data, and on expertise judgment. This paper presents a comparison between results produced by two of these methods: weights-of-evidence and fuzzy logic. The mineral favorability maps for gold exploration were produced in a Geographic Information Systems (GIS) environment and took into account five sources of data and information: (i) satellite images; (ii) a geochemical survey; (iii) an airborne geophysical survey; (iv) geo-structural mapping; and (v) ground elevation. These data and information were integrated through a conceptual model developed for gold mines and occurrences in the studied region. Both favorability maps highlighted the known gold occurrences and validated the approach. High gold potential areas highlighted by both maps show good correlation. But, the weights-of-evidence method delineated smaller highly favorable areas compared to the fuzzy logic map. On the other hand, the weights-of-evidence method produced higher biased probability within the favorable zones when compared with fuzzy logic methods favorable areas. New exploration targets were identified and should be further investigated.

KEY WORDS

Geo-mathematics fuzzy logic weights-of-evidence GIS mineral exploration ore deposit modeling. 

Notes

ACKNOWLEDGMENTS

The authors would like to thank CAPES, CNPq, and the staff of the Crystallex Inc. to support this research.

References

  1. Agterberg, F. P., 1989, Computer programs for mineral exploration: Science, v. 245, no. 4913, p. 76–81.CrossRefGoogle Scholar
  2. Agterberg, F. P., 1992, Combining indicator patterns in weights of evidence modeling for resource evaluation: Nonrenewable Resources, v. 1, no. 1, p. 39–50.CrossRefGoogle Scholar
  3. Agterberg, F. P., and Bonham-Carter, G. F., 1990, Deriving weights of evidence from geoscience contour maps for the prediction of discrete events, in Proc. 22 nd Intern. Symp. APCOM, (Berlin): Technical Univ., Berlin, v. 2, p. 381–396.Google Scholar
  4. Agterberg, F. P., and Bonham-Carter, G. F., 1999, Logistic regression and weights of evidence modeling in mineral exploration, in Computer Applications in the Mineral Industries: Proc. 28th Intern. Symposium APCOM'99, Colorado, USA, p. 583–590.Google Scholar
  5. Amaro, V. E., and Strieder, A. J., 1994, Análise de fotolineamentos e de padrões estruturais em imagens de satélite, in 38 Congresso Brasileiro de Geologia, Bal. Camboriú, SC., Brasil, Bol. Res. Exp., v. 1, p. 443–444.Google Scholar
  6. An, P., Moon, W. M., and Rencz, A., 1991, Application of fuzzy set theory for integration of geological, geophysical and remote sensing data: Can. Jour. Exploration Geophysics, v. 27, no. 1, p. 1–11.Google Scholar
  7. Bonham-Carter, G. F., 1991, Integration of geoscientific data using GIS, in Maguire, J. D., Goodchild, M. F., Rhind, D. W., eds., Geographical Information Systems: principles and applications: Longman, London, v. 1, p. 171–188.Google Scholar
  8. Bonham-Carter, G. F., 1994, Geographic Information Systems for geoscientists: Modeling with GIS: Pergamon, Ontario, Canada, p. 398.Google Scholar
  9. Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1988, Integration of geological datasets for gold exploration in Nova Scotia: Photogrammetric Eng. and Remote Sensing, v. 54, no. 11, p. 1585–1592.Google Scholar
  10. Bonham-Carter, G. F., Agterberg, F. P., and Wright, D. F., 1989, Weights of evidence modelling: A new approach to mapping mineral potential, in Agterberg, F. P., and Bonham-Carter, G. F. eds., Statistical Applications in the Earth Sciences: Geol. Survey Canada Paper 89–9, p. 171–183.Google Scholar
  11. Crosta, A. P., and Moore, J. McM., 1989, Enhancement of landsat thematic mapper imagery for residual soil mapping in SW Minas Gerais State, Brazil: a prospecting case history in greenstone belt terrain, in Proc. 7th (ERIM) Thematic Conference: Remote Sensing for Exploration Geology (Calgary), p. 1173–1187.Google Scholar
  12. Eastman, J. R., 1997, Idrisi for Windows User's Guide, Version 2.0: Clark University, MA, USA.Google Scholar
  13. Efimov, A. V., 1978, Multiplikativniyj pokazatel dlja vydelenija endogennych rud aerogammaspectrometriceskim dannym, in Metody rudnoj geofiziki. Lenigrad, Naucnoproizvodstvennoje objedinenie Geofizica Ed., p. 59–68.Google Scholar
  14. Fang, J. H., 1997, Fuzzy logic & geology: Geotimes, October, v. 42, no. 10, p. 23–26.Google Scholar
  15. Glikson, A. Y., 1997, Mineral-mapping in the north Pilbara Craton. A directed principal components of the band ratios method for correlating Landsat-5 Thematic Mapper spectral data with geology: AGSO-Australian Geol. Survey Organisation Research Newsletter, v. 26, p. 1–4.Google Scholar
  16. Gnojek, I., and Prichystal, A., 1985, A new zinc mineralization detected by airborne gamma-ray.Google Scholar
  17. Groves, D. I., Barley, M. E., Barnicoat, A. C., Cassidy, K. F., Fare, R. J., Hageman, S. G., Ho, S. E., Hronsky, J. M., Mikucki, E. J., Mueller, A. J., McNaughton, N. J., Perring, C. S., Ridley, J. R., and Veancombe, J. R., 1992, Sub-greenschist to granulite-hosted Archaean lode-gold deposits of the Yilgarn Craton: a depositional continuum from deep-sourced hydrothermal fluids in a crustal-scale plumbing systems: Geology Depart. (Key Centre) and University Extension, Univ. Western Australia Publ., v. 22, p. 366–374.Google Scholar
  18. Harris, D. P., and Pan, G., 1991, Consistent geological areas for epithermal gold-silver deposits in the Walker Lake Quadrangle of Nevada and California delineated by quantitative methods: Econ. Geology, v. 86, no. 1, p. 142–165.Google Scholar
  19. Harris, D., and Pan, G., 1999, Mineral favorability mapping: A comparison of artificial neural networks, logistic regression and discriminant analysis: Natural Resources Research, v. 8, no. 2, p. 93–109.CrossRefGoogle Scholar
  20. Kerrich, R., 1993, Perspectives on genetic models for lode gold deposits: Mineralium Deposit, v. 28, p. 362–365.CrossRefGoogle Scholar
  21. Lindgren, W., 1933, Mineral deposits (4th edn.): McGraw-Hill Book Co., New York, p. 930Google Scholar
  22. Loughlin, W. P., 1991, Principal component analysis for alteration mapping: Photogrammetric Engineering & Remote Sensing: v. 57, no. 9, p. 1163–1169.Google Scholar
  23. Mikucki, E. J., and Ridley, J. R., 1993, The hydrothermal fluid of Archaean lode-gold deposits at different metamorphic grades: Compositional constraints from ore and wallrock alteration assemblages: Mineral Deposits, v. 28, no. 6, p. 469–481.CrossRefGoogle Scholar
  24. Pan, G. C., 1993, Canonical favorability model for data integration and mineral potential mapping: Computers & Geoscience, v. 19, no. 8, p. 1077–1100.CrossRefGoogle Scholar
  25. Pan, G. C., 1996, Extended weights of evidence modeling for the pseudo-estimation of metal grades: Nonrenewable Resources, v. 5, no. 1, p. 53–76.CrossRefGoogle Scholar
  26. Pires, A. C. B., 1995, Identificação Geofisica de Áreas de Alteração Hidrotermal, Crixás-Guarinos, Góias: Revista Brasileira de Geociências, v. 25, no. 1, p. 61–68.Google Scholar
  27. Quadros, T. F. P., De, 1995, Geologia e gênese do depósito aurífero da Mina San Gregorio, Dissertação de Mestrado, Curso de Pós-graduação em Geociências, Universidade Federal do Rio Grande do Sul, p. 196.Google Scholar
  28. Quadros, T. F. P., Koppe, J. C., and Strieder, A. J., 1995, Dynamic shear development and mineralization at the San Gregorio Gold Mine, Uruguay, in Rossmanith, H. P., ed., Proc. II Intern. Conf. on Mechanics of Jointed and Faulted Rocks (Viena, Austria): A. A. Balkema Publ., Rotterdam (Netherlands), p. 347–353.Google Scholar
  29. Quadros, T. F. P. De, Koppe, J. C., and Strieder, A. J., 1997, Lineament analysis for gold deposit exploration in the Rivera Shear System: World Gold'97 Conf. (Singapore): Proc. World Gold'97 Conf.: Australasian Inst. Mining and Metalurgy, (AIMM) v. 1, p. 19–22.Google Scholar
  30. Reynolds, H. T., 1997, The analysis of cross-classifications, in TYDAC Research Inc., Explorer Spans.Google Scholar
  31. Saunders, D. F., Terry, S. A., and Thompsom, C. K., 1987, Test of National Uranium Resource Evaluation gamma-ray spetral data in petroleum reconnaissance: Geophysics, v. 52, no. 11, p. 1547–1556.CrossRefGoogle Scholar
  32. Singer, D. A., and Kouda, R., 1988, Integrating spatial and frequency information in the search for kuroko deposits of the Hokuroko District, Japan: Econ. Geology, v. 83, no. 1, p. 18–29.CrossRefGoogle Scholar
  33. Singer, D. A., and Kouda, R., 1996, Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroko District, Japan: Math. Geology, v. 28, no. 8, p. 1017–1023.CrossRefGoogle Scholar
  34. Singer, D. A., and Kouda, R., 1999, A comparison of the weights of evidence method and probabilistic neural networks: Natural Resources Research, v. 8, no. 4, p. 281–298.CrossRefGoogle Scholar
  35. Tydac, 1997, SPANS User Guide, Version 6.0. Tydac Technologies Inc. Ottawa, Ontario, Canada.Google Scholar
  36. Wright, D. F., and Bonham-Carter, G. F., 1996, VHMS favourability mapping with GIS-based integration models, Chisel Lake-Anderson Lake area, in Bonham-Carter, G. F., Galley, A. G., and Hall, G. E. M., eds., Extech I: A multidisciplinary approach to massive sulphine research in the Rusty Lake-Snow Lake Greenstone Belts, Manitoba; Geol. Survey of Canada. Bull. 426, p. 339–401.Google Scholar

Copyright information

© International Association for Mathematical Geology 2006

Authors and Affiliations

  • Telmo F. P. de Quadros
    • 1
  • Jair C. Koppe
    • 2
  • Adelir J. Strieder
    • 2
  • João F. C. L. Costa
    • 2
  1. 1.FEPAM–Fundação Estadual de Proteção Ambiental do Estado do Rio Grande do SulRua Carlos ChagasPorto AlegreBrasil
  2. 2.Mining Engineering DepartmentFederal University of Rio Grande do SulPorto AlegreBrasil

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