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Data-Driven Evidential Belief Modeling of Mineral Potential Using Few Prospects and Evidence with Missing Values

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Abstract

Data-driven evidential belief (EB) modeling has already been demonstrated for mineral prospectivity mapping in areas with many (i.e., >20) deposits/prospects (i.e., with indicated/inferred resources). In this paper, EB modeling is applied to a case-study area measuring about 920 km2 with 12 known porphyry-Cu prospects and with evidential data layer containing missing values. Porphyry-Cu prospectivity of the same area has been modeled previously using weights-of-evidence modeling, which serves as reference for evaluating the results of EB modeling. Initially, EB modeling was used to quantify spatial associations of the known porphyry-Cu prospects with various geological features perceived to be porphyry-Cu mineralization controls. Spatial associations of the known porphyry-Cu prospects with geochemical data layers with missing values were also quantified. Then, geological and geochemical data layers found to have positive spatial associations with the known porphyry-Cu prospects were used as predictors of porphyry-Cu prospectivity. The results show that EB modeling is as efficient as WofE modeling in predictive modeling of mineral prospectivity in areas with as few as 12 prospects and with evidential data layers containing missing values.

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Acknowledgments

The author would like to thank two anonymous reviewers for their constructive comments and to Associate Editor Renguang Zuo for editorial handling.

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  1. School of Earth and Oceans, James Cook University, 1 James Cook Drive, Townsville, QLD, 4811, Australia

    Emmanuel John M. Carranza

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  1. Emmanuel John M. Carranza
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Correspondence to Emmanuel John M. Carranza.

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Carranza, E.J.M. Data-Driven Evidential Belief Modeling of Mineral Potential Using Few Prospects and Evidence with Missing Values. Nat Resour Res 24, 291–304 (2015). https://doi.org/10.1007/s11053-014-9250-z

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