ISSN 1006-3021 CN11-3474/P
Published bimonthly started in 1979
关键词:machine learning  geochemical anomalies  Artificial Neural Network  Random Forest  Support Vector Machine
基金项目:国家自然科学基金面上项目“基于地质先验模型的区域大比例尺三维地质建模关键技术研究”(编号: 41672330);国家重点研发计划“深地资源勘查开采”重点专项课题“深部矿产三维可视化预测评价软件系统研发”(编号: 2017YFC0601501);中国地质调查局地质调查项目“全国矿产资源潜力动态评价”(编号: DD20190193)
李苍柏 中国地质科学院矿产资源研究所, 自然资源部成矿作用与资源评价重点实验室
肖克炎 中国地质科学院矿产资源研究所, 自然资源部成矿作用与资源评价重点实验室  
李楠 中国地质科学院矿产资源研究所, 自然资源部成矿作用与资源评价重点实验室  
宋相龙 中国地质科学院  
张帅 中国地质科学院矿产资源研究所, 自然资源部成矿作用与资源评价重点实验室
王凯 中国地质科学院矿产资源研究所, 自然资源部成矿作用与资源评价重点实验室
楚文楷 中国地质科学院矿产资源研究所, 自然资源部成矿作用与资源评价重点实验室
曹瑞 中国地质科学院矿产资源研究所, 自然资源部成矿作用与资源评价重点实验室
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A Comparative Study of Support Vector Machine, Random Forest and Artificial Neural Network Machine Learning Algorithms in Geochemical Anomaly Information Extraction
      Geochemical prospecting is conducted through finding anomalies as well as explaining and evaluating anomalies. Therefore, geochemical anomaly identification has a certain indicative effect on the locating and quantitative predicting of mineral resources. In the background of big data era, the machine learning method doesn’t need the distribution form that obeys the normal distribution, and has the characteristics of nonlinear and strong generalization. Thus, it is gradually applied to the quantitative prediction of mineral resources, such as Neural Network, Support Vector Machine, Bayesian Network, Random Forest, Restricted Boltzmann Machine, and Extreme Learning Machine. Based on previous studies, this paper proposes the hypothesis that the effects of different supervised machine learning methods on geochemical anomaly information extraction in different regions are inconsistent. Through the design theory experiment, the results of different algorithms in different data distributions aren’t consistent. On such a basis, the geochemical anomaly detection of the Xianghualing tin polymetallic ore deposit in Hunan Province and the Hezuo gold ore deposit in Gansu Province were taken as examples, and the resulting geochemical anomalies in the two regions were extracted and identified by Artificial Neural Network, Random Forest and Support Vector Machine. In the study area of Xianghualing, the results of Artificial Neural Network were relatively good, while in the study area of Hezuo, the results of Random Forest were relatively good, which verified the above hypothesis. The geological significance of the method and its reliability and practicability were discussed by generating geochemical anomaly maps in the two study areas. In addition, the geochemical anomaly information extraction process based on a variety of supervised machine learning methods was also improved, which provides a certain theoretical basis for software development.
LI Cang-bai,XIAO Ke-yan,LI Nan,SONG Xiang-long,ZHANG Shuai,WANG Kai,CHU Wen-kai,CAO Rui.2020.A Comparative Study of Support Vector Machine, Random Forest and Artificial Neural Network Machine Learning Algorithms in Geochemical Anomaly Information Extraction[J].Acta Geoscientica Sinica,41(2):309-319.
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