Data-driven predictive modeling of mineral prospectivity using random forests: a case study in Catanduanes Island (Philippines)

Carranza, Emmanuel John M., and Laborte, Alice G. (2016) Data-driven predictive modeling of mineral prospectivity using random forests: a case study in Catanduanes Island (Philippines). Natural Resources Research, 25 (1). pp. 35-50.

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Abstract

The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out using data from Catanduanes Island (Philippines) to investigate further (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with few (i.e., <20) mineral occurrences and (b) if RF modeling can handle predictor variables with missing values. We found that RF modeling outperforms evidential belief (EB) modeling of prospectivity for hydrothermal Au–Cu deposits in Catanduanes Island, where 17 hydrothermal Au–Cu prospects are known to exist. Moreover, just like EB modeling, RF modeling allows analysis of the spatial relationships between known prospects and individual layers of predictor data. Furthermore, RF modeling can handle missing values in predictor data through an RF-based imputation technique whereas in EB modeling, missing values are simply represented by maximum uncertainty. Therefore, the RF algorithm is a potentially useful method for data-driven predictive modeling of mineral prospectivity in regions with few (i.e., <20) occurrences of mineral deposits of the type sought. However, further testing of the method in other regions with few mineral occurrences is warranted to fully determine its usefulness in data-driven predictive modeling of mineral prospectivity.

Item ID: 43175
Item Type: Article (Research - C1)
ISSN: 1573-8981
Keywords: regression trees; missing data; hydrothermal Au–Cu deposits; Catanduanes (Philippines); GIS
Date Deposited: 10 Mar 2016 03:31
FoR Codes: 01 MATHEMATICAL SCIENCES > 0103 Numerical and Computational Mathematics > 010399 Numerical and Computational Mathematics not elsewhere classified @ 70%
04 EARTH SCIENCES > 0403 Geology > 040307 Ore Deposit Petrology @ 30%
SEO Codes: 84 MINERAL RESOURCES (excl. Energy Resources) > 8401 Mineral Exploration > 840199 Mineral Exploration not elsewhere classified @ 80%
89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890201 Application Software Packages (excl. Computer Games) @ 20%
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