| 耦合PCA-SVM算法的金矿矿床规模预测分析研究 |
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| 引用本文： | 刘承照,韩帅,李明超,朱月琴. 耦合PCA-SVM算法的金矿矿床规模预测分析研究[J]. 地学前缘, 2019, 26(4): 138-145. DOI: 10.13745/j.esf.sf.2019.7.7 |
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| 作者姓名： | 刘承照 韩帅 李明超 朱月琴 |
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| 作者单位： | 天津大学水利工程仿真与安全国家重点实验室,天津300354;中国地质调查局发展研究中心,北京100037;自然资源部地质信息技术重点实验室,北京100037 |
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| 基金项目： | 天津市杰出青年科学基金项目(17JCJQJC44000);国家优秀青年科学基金项目(51622904)；国家重点研发计划项目(2016YFC0600510) |
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| 摘 要： | 判断矿床(点)的类型是矿床勘探中的重要内容,传统预测金矿成矿规模的方法不仅耗时耗力,而且所需的经济成本较大。为提高矿床规模的勘探效率和准确度,揭示元素与金矿成矿规模的潜在联系,文中提出了耦合主成分分析(principal component analysis,PCA)和支持向量机(support vector machine,SVM)算法的预测分析PCA-SVM(principal component analysis-support vector machine)方法。该方法先通过主成分分析提取数据中的主要特征,再将主要特征带入支持向量机算法,从而训练出最优分类器以预测金矿成矿规模。文中共使用了3 812个金矿样本数据用于学习训练和预测分析,训练准确率为92.3%,测试准确率为88.7%,分别比直接使用支持向量机算法高出14.3%和17.1%。基于PCA-SVM的预测模型,不仅消除了人为主观因素的影响,而且有效提高了勘探过程中矿床预测的准确率和矿床勘探的效率,为地质勘查工作提供依据。
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| 关 键 词： | 金矿矿床 规模预测 主成分分析 支持向量机 耦合算法 |
| 收稿时间： | 2018-09-21 |
Prediction and analysis of gold deposit sizes based on coupled PCA-SVM algorithm |
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| LIU Chengzhao,HAN Shuai,LI Mingchao,ZHU Yueqin. Prediction and analysis of gold deposit sizes based on coupled PCA-SVM algorithm[J]. Earth Science Frontiers, 2019, 26(4): 138-145. DOI: 10.13745/j.esf.sf.2019.7.7 |
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| Authors: | LIU Chengzhao HAN Shuai LI Mingchao ZHU Yueqin |
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| Affiliation: | (State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300354,China;Development Research Center of China Geological Survey,Beijing 100037,China;Key Laboratory of Geological Information Technology,Ministry of Natural Resources,Beijing 100037,China) |
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| Abstract: | Identification of ore deposit types is an important part of mineral exploration. Traditional methods for predicting deposit size are time-consuming, laborious and costly. In order to improve prospecting efficiency and accuracy and reveal potential relation between chemical composition and the size of gold mineralization, we propose here an integrated approach using the Principal Component Analysis (PCA) and Support Vector Machine (SVM) algorithms. In this approach, we first extract the major features of samples using PCA, and we then train a set of SVM classifiers by these features to predict deposit sizes. In this study, we collected and analyzed 3812 gold mine samples from Beishan, Gansu region to establish a PCA-SVM model with the training accuracy of 92.3% and the test accuracy of 88.7%, which were 14.3% and 17.1% higher, respectively, than using SVM. We demonstrated that the PCA-SVM method not only can eliminate subjective factors, but also can improve the accuracy of identifying ore deposits as well as prospecting efficiency, thus to provide reliable support for decision making. |
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| Keywords: | gold deposits size prediction Principal Component Analysis (PCA) Support Vector Machine (SVM) coupled algorithm |
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