Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping

Carranza, E.J.M., Hale, M., and Faassen, C. (2008) Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping. Ore Geology Reviews, 33 (3-4). pp. 536-558.

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Abstract

Data-driven prospectivity mapping can be undermined by dissimilarity in multivariate spatial data signatures of deposit-type locations. Most cases of data-driven prospectivity mapping, however, make use of training sets of randomly selected deposit-type locations with the implicit assumption that they are coherent (i.e., with similar multivariate spatial data signatures). This study shows that the quality of data-driven prospectivity mapping can be improved by using a training set of coherent deposit-type locations. Analysis and selection of coherent deposit-type locations was performed via logistic regression, by using multiple sets of deposit occurrence favourability scores of univariate geoscience spatial data as independent variables and binary deposit occurrence scores as dependent variable. The set of coherent deposit-type locations and three sets of randomly selected deposit-type locations were each used in data-driven prospectivity mapping via application of evidential belief functions. The prospectivity map based on the training set of coherent deposit-type locations resulted in lower uncertainty, better goodness-of-fit to the training set, and better predictive capacity against a cross-validation set of economic deposits of the type sought. This study shows that explicit selection of training set of coherent deposit-type locations should be applied in data-driven prospectivity mapping.

Item ID: 27367
Item Type: Article (Research - C1)
ISSN: 1872-7360
Keywords: cumulative frequency distributions; logistic regression; evidential belief functions; GIS; alkalic porphyry Cu–Au; structural controls; geophysics; geochemistry; British Columbia (Canada)
Date Deposited: 13 Jun 2013 01:47
FoR Codes: 04 EARTH SCIENCES > 0403 Geology > 040399 Geology not elsewhere classified @ 50%
01 MATHEMATICAL SCIENCES > 0103 Numerical and Computational Mathematics > 010399 Numerical and Computational Mathematics not elsewhere classified @ 50%
SEO Codes: 84 MINERAL RESOURCES (excl. Energy Resources) > 8401 Mineral Exploration > 840199 Mineral Exploration not elsewhere classified @ 100%
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