Support vector machine for multi-classification of mineral prospectivity areas
Support vector machine for multi-classification of mineral prospectivity areas
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DOI：
10.1016/j.cageo.2011.12.014
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发表时间：
2012-09-01
影响因子：
4.4
通讯作者：
Bahroudi, Abbas
中科院分区：
文献类型：
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作者：
Abedi, Maysam;Norouzi, Gholam-Hossain;Bahroudi, Abbas
在矿产远景图的编制中，采用支持向量机（SVM）的监督分类方法对斑岩铜矿进行了预测。综合地质、地球物理和地球化学主题的不同数据层，以评价位于伊朗克尔曼省的Now Chun斑岩铜矿存款，并编制矿产勘探的远景图。支持向量机方法，一种数据驱动的模式识别方法，有一个正确的分类率为52.38%的21个钻孔分为五类。研究结果表明，支持向量机的能力作为一个监督学习算法工具的预测映射的矿产前景。对前景进行多层次的精细研究，可以提高远景图的分辨率，降低钻探风险。(C)2012爱思唯尔有限公司保留所有权利。
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.