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Performance evaluation of RBF- and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of S-A multifractal model and mineralization controls

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Abstract

Definition of the efficient ore-forming processes, which are considered as mineralization controls is a fundamental stage in mineral prospectivity modeling. In this contribution, four efficient targeting criteria of geochemical, geological and structural data related to porphyry-type Cu deposits in Varzaghan district, NW Iran, were integrated. For creation of multi-element geochemical layer, a two-stage factor analysis was firstly conducted on ilr-transformed data of 18 selected elements and it was found that factor 1 (F1) is the representative of Cu-Au-Mo-Bi elemental association in the study area. Then, the combined model of multifractal inverse distance weighting (IDW) interpolation technique and spectrum-area (S-A) fractal method of F1 as the significant mineralization-related multi-element geochemical layer was integrated with geological-structural evidence layers. For this purpose, two supervised machine learning algorithms, namely radial basis function (RBF) neural network and support vector machine (SVM) with RBF kernel were used for generating data-driven predictive models of porphyry-Cu mineral prospectivity. Comparison of the generated models demonstrates that the former is more successful in delineating exploration targets than the latter one.

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Acknowledgements

The authors would like to thank Prof. H. A. Babaie, Editor-in-Chief, and two anonymous reviewers for their constructive comments and edits.

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  1. Faculty of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Post Code: 15875-4413, Iran

    Reza Ghezelbash & Abbas Maghsoudi

  2. Geological Sciences, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa

    Emmanuel John M. Carranza

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  1. Reza Ghezelbash
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Correspondence to Abbas Maghsoudi.

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Ghezelbash, R., Maghsoudi, A. & Carranza, E.J.M. Performance evaluation of RBF- and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of S-A multifractal model and mineralization controls. Earth Sci Inform 12, 277–293 (2019). https://doi.org/10.1007/s12145-018-00377-6

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