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Intelligent geochemical exploration modeling using multiclass support vector machine and integration it with continuous genetic algorithm in Gonabad region, Khorasan Razavi, Iran

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Abstract

Mineral exploration modeling can be used to predict locations which exploratory criteria confirm possibility of mineral resources/reserves presence. Due to scientific progresses, existence of a large amount of data and also to prevent personal bias, using of artificial intelligence algorithms is essential. In addition, linear geostatistical techniques will no longer be responsible. Given that dataset has been consisted of geochemical sampling points, multiclass support vector machine algorithm (SVM) is used for mineral prospectivity mapping. SVM is performed with two approaches single and integration it with continuous genetic algorithm (CGASVM) to define hyperparameters of SVM instead of trial and error selection. At the first stage, preprocessing of the data is done and fuzzy factor scores maps are drawn then the anomalies of each factor are determined. After that in order to obtain intelligent modelling, multiclass SVM and CGASVM algorithms are written and implemented in MATLAB; also, to evaluate modelings capability, relevant indicators are used. The achieved results show a high degree of accuracy in classifying test data in both exploratory modellings compared to traditional methods that are also consistent with field studies. These methods can be used to intelligently explore minerals and subsequently to determine mineralization zones. The abovementioned algorithms are applied to avoid consuming time and labor and where the result of sampling cannot be determined directly.

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Acknowledgements

The authors would like to thank Mr.Vajiollah Jafari CEO of Iran minerals production and supply company for constructive comments and suggestions.

Also, the authors would like to express their gratitude to Dr. Feridon Ghadimi assistant professor, department of mining engineering, Arak University of Technology, for reviewing the manuscript and providing helpful comments.

This article is the results section of the research related to Miss Mandana Tahmooresi’s dissertation that has been done in Mahallat Branch, Islamic Azad University.

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Authors and Affiliations

  1. Mining Engineering-Mineral Exploration, Mining Department, Mahallat Branch, Islamic Azad University, Mahallat, Iran

    Mandana Tahmooresi

  2. Mining Department, Mahallat Branch, Islamic Azad University, Mahallat, Iran

    Behnam Babaei & Saeed Dehghan

Authors
  1. Mandana Tahmooresi
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  2. Behnam Babaei
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  3. Saeed Dehghan
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Correspondence to Behnam Babaei.

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Responsible Editor: Domenico M. Doronzo

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Tahmooresi, M., Babaei, B. & Dehghan, S. Intelligent geochemical exploration modeling using multiclass support vector machine and integration it with continuous genetic algorithm in Gonabad region, Khorasan Razavi, Iran. Arab J Geosci 14, 1012 (2021). https://doi.org/10.1007/s12517-021-07306-w

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  • DOI: https://doi.org/10.1007/s12517-021-07306-w

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