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Classification of alteration zones based on whole-rock geochemical data using support vector machine

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Journal of the Geological Society of India

Abstract

One of the most important steps in the mineral resource estimation and detailed exploration of porphyry copper deposit is separating the alteration zones as a control parameter of the copper grade. The most popular method for this separation is petrological investigations (other methods are not much popular), but the method lacks the ability to predict alteration zones of the un-sampled points. In this paper a new method has been proposed which is based on the support vector machine (SVM) classification of the analyzed whole rock samples and it has been used in Sungun porphyry copper deposit to separate potassic, phyllic and transition alteration zones. To apply the SVM method, use has been made of the radial basis function (RBF) as the kernel function and to obtain the optimal values for the SVM parameters (γ and C being the most important); the grid search method has been used. The best values for γ and C that have had good performance in the training and test steps are 0.0625 and 32, respectively. Results have revealed that the SVM classification (used in this study) can effectively separate the alteration zones in the Sungun deposit. Specifically, the accuracy of this method has been 75% which proves that the support vector machine can offer an inexpensive, fast and robust classification technique and it can be a valid alternative to the well established methodologies in this area.

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Authors and Affiliations

  1. Department of Mining and Metallurgy Engineering, Amirkabir University of Technology, Tehran, Iran

    Maliheh Abbaszadeh & Ardeshir Hezarkhani

  2. Department of Mining Engineering, University of Kashan, Kashan, Iran

    Saeed Soltani-Mohammadi

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  1. Maliheh Abbaszadeh
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  2. Ardeshir Hezarkhani
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  3. Saeed Soltani-Mohammadi
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Correspondence to Maliheh Abbaszadeh.

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Abbaszadeh, M., Hezarkhani, A. & Soltani-Mohammadi, S. Classification of alteration zones based on whole-rock geochemical data using support vector machine. J Geol Soc India 85, 500–508 (2015). https://doi.org/10.1007/s12594-015-0242-3

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  • DOI: https://doi.org/10.1007/s12594-015-0242-3

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