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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

  • Reza Ghezelbash
  • Abbas MaghsoudiEmail author
  • Emmanuel John M. Carranza
Research Article
  • 38 Downloads

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.

Keywords

Porphyry-Cu deposits Prospectivity modeling Multifractal inverse distance weighting (MIDW) interpolation Spectrum-area (S-A) fractal RBF neural network SVM 

Notes

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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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Mining and Metallurgical EngineeringAmirkabir University of TechnologyTehranIran
  2. 2.Geological Sciences, School of Agricultural, Earth and Environmental SciencesUniversity of KwaZulu-NatalDurbanSouth Africa

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