期刊
COMPUTERS & GEOSCIENCES
卷 46, 期 -, 页码 272-283出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2011.12.014
关键词
Mineral prospectivity mapping; SVM method; Multi-classification; Porphyry copper; Now Chun deposit
资金
- Department of Mining Engineering, University of Tehran [81059601/1/3]
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. (C) 2012 Elsevier Ltd. All rights reserved.
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