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Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit, NW Iran

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Abstract

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods.

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Acknowledgments

We would like to thank the Zar Kuh mining company, the owner of the Sari-Gunay gold deposit, for providing the geochemical data. We acknowledge Prof. Renguang Zuo, Associate Editor of NRR, for expert handling of our manuscript and we thank two anonymous reviewers for their constructive comments.

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Authors and Affiliations

  1. Department of Mining Engineering, Isfahan University of Technology, Isfahan, 8415683111, Iran

    Hamid Geranian, Seyed Hassan Tabatabaei & Hooshang H. Asadi

  2. Centre for Exploration Targeting, Australian Research Council Centre of Excellence for Core to Crust Fluid Systems (CCFS), School of Earth and Environment, The University of Western Australia, Crawley, WA, 6009, Australia

    Hooshang H. Asadi

  3. Department of Earth and Oceans, James Cook University, Townsville, QLD, Australia

    Emmanuel John M. Carranza

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  1. Hamid Geranian
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Correspondence to Seyed Hassan Tabatabaei.

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Geranian, H., Tabatabaei, S.H., Asadi, H.H. et al. Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit, NW Iran. Nat Resour Res 25, 145–159 (2016). https://doi.org/10.1007/s11053-015-9271-2

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