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Recognition of mineralized belts and lithologic patterns in the Silver City-South Mountain region, Idaho, in terms of geochemical reconnaissance data

  • Yuwei Li
Article

Abstract

It seems unreasonable to use one population to fit the distribution of an element, and then to determine a threshold to separate anomalous data from background data in an analysis of geochemical data. Statistically, anomaly, background, and other geological categories may be represented by different component populations overlapping one another. Therefore, anomaly, background, and other geological categories should be distinguished from one another by distributions rather than by thresholds. This paper uses a method of decomposition of mixtures to identify observed distributions of five elements, obtained in a geochemical reconnaissance of the Silver City-South Mountain region, Idaho, into component populations. Observations have been assigned to populations and mapped; finally, these populations have been interpreted leading to recognition of both mineralized belts and lithologic patterns.

Key words

nonlinear regression discriminant analysis decomposition of mixtures geochemistry resource assessment 

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References

  1. Asher, R. R. 1968, Geology and mineral resources of a portion of the Silver City region, Owyhee County, Idaho: Idaho Bureau of Mines and Geological Pamphlets, 138, 106 p.Google Scholar
  2. Bennett, I. I., Earl H., and Galbraith, J. H., 1975, Reconnaissance geology and geochemistry of the Silver City-South Mountain region, Owyhee County, Idaho: Idaho Bureau of Mines and Geological Pamphlets, 162, 88 p.Google Scholar
  3. Clark, I., 1977, ROKE, a computer program for nonlinear least-squares decomposition of mixtures of distributions: Comput. Geosci., v. 3, p. 245–256.Google Scholar
  4. Day, N. E., 1969, Estimating the components of a mixture of normal distributions: Biometrika, v. 56, p. 463–474.Google Scholar
  5. Li, Y., 1984, Spatial pattern recognition by decomposition: Jour. Math. Geol., v. 16, no. 3, p. 217–236.Google Scholar
  6. Li, Y., Yu, J., and Xie, X., 1980, Cluster prediction of mineral resources: Sciences de la terre: Infor. Géol., no. 15, p. 73–85.Google Scholar
  7. McCammon, R. B., 1969, FORTRAN IV program for nonlinear estimation: Computer Contribution 34, University of Kansas, Kansas 20 p.Google Scholar
  8. McCammon, R. B., Bridges, N. J., McCarthy, J. H., Jr., and Gott, G. B., 1979, Estimate of mixed geochemical populations in rocks at Ely, Nevada: Geochemical Exploration 1978,in Watterson, J. R. and Theobald, P. K. (Eds.), Proceedings of the 7th International Geochemical Exploration Symposium: Association of Exploration Geochemists, p. 385–390.Google Scholar
  9. Panze, A. J., 1971, Geology and ore deposits of the Silver City-De Larmar-Flint Creek region, Owyhee County, Idaho Unpublished Ph.D. Thesis: Colorado School of Mines, 150 p.Google Scholar
  10. Sinclair, A. J., 1976, Applications of probability graphs in mineral exploration: Association of Exploration Geochemists, Special volume 4, 95 p.Google Scholar
  11. Sinding-Larsen, R., 1975, A computer method for dividing a regional geochemical survey area into homogeneous subareas prior to statistical interpretation: Geochemical exploration 1974,in Elliott, L. L., and Fletcher, W. K. (Eds.), Proceedings of the 5th International Geochemical Exploration Symposium: Association of Exploration Geochemists, p. 191–217.Google Scholar
  12. Sorenson, R. E., 1927, The geology and ore deposits of the South Mountain Mining District, Owyhee County, Idaho: Idaho Bureau of Mines and Geological Pamphlet No. 22, 47 p.Google Scholar
  13. Switzer, P., 1980, Extensions of linear discriminant analysis for statistical classification of remotely sensed satellite imagery: Jour. Math. Geol., v. 12, no. 4, p. 367–376.Google Scholar
  14. Switzer, P., Kowalik, W. S., and Lyon, R. J. P., 1982, A prior probability method for smoothing discriminant analysis classification map: Jour. Math. Geol., vol. 14, p. 433–444.Google Scholar

Copyright information

© Plenum Publishing Corporation 1985

Authors and Affiliations

  • Yuwei Li
    • 1
    • 2
  1. 1.Institute of Mineral DepositsChinese Academy of Geological SciencesBeijingChina
  2. 2.Department of Geological SciencesNorthwestern UniversityEvanstonUSA

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