Data-driven evidential belief modeling of mineral potential using few prospects and evidence with missing values

Carranza, Emmanuel John M. (2015) Data-driven evidential belief modeling of mineral potential using few prospects and evidence with missing values. Natural Resources Research, 24 (3). pp. 291-304.

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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 km^2 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.

Item ID: 40147
Item Type: Article (Research - C1)
ISSN: 1573-8981
Keywords: spatial association; uncertainty due to missing values; porphyry copper; GIS
Date Deposited: 04 Sep 2015 03:04
FoR Codes: 04 EARTH SCIENCES > 0403 Geology > 040307 Ore Deposit Petrology @ 20%
08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems > 080603 Conceptual Modelling @ 20%
08 INFORMATION AND COMPUTING SCIENCES > 0802 Computation Theory and Mathematics > 080205 Numerical Computation @ 60%
SEO Codes: 84 MINERAL RESOURCES (excl. Energy Resources) > 8401 Mineral Exploration > 840199 Mineral Exploration not elsewhere classified @ 100%
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