Support vector machine for multi-classification of mineral prospectivity areas

Authors:
Maysam Abedi;Gholam-Hossain Norouzi;Abbas Bahroudi
Affiliations:
Department of Mining Engineering, College of Engineering, University of Tehran, Iran;Department of Mining Engineering, College of Engineering, University of Tehran, Iran;Department of Mining Engineering, College of Engineering, University of Tehran, Iran
Venue:
Computers & Geosciences
Year:
2012

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Abstract

In this paper on mineral prospectivity mapping, a supervised classification method called Support Vector Machine (SVM) is used to explore porphyry-Cu deposits. Different data layers of geological, geophysical and geochemical themes are integrated to evaluate the Now Chun porphyry-Cu deposit, located in the Kerman province of Iran, and to prepare a prospectivity map for mineral exploration. The SVM method, a data-driven approach to pattern recognition, had a correct-classification rate of 52.38% for twenty-one boreholes divided into five classes. The results of the study indicated the capability of SVM as a supervised learning algorithm tool for the predictive mapping of mineral prospects. Multi-classification of the prospect for detailed study could increase the resolution of the prospectivity map and decrease the drilling risk.