| 基于PCA-SVM算法对稀土元素与稀土判别指标耦合数据集的铀矿床分类 |
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| 引用本文： | 刘云鹏,郭春影,秦明宽,吴玉,裴柳宁.基于PCA-SVM算法对稀土元素与稀土判别指标耦合数据集的铀矿床分类[J].吉林大学学报(地球科学版),2021,51(3):723-733. |
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| 作者姓名： | 刘云鹏 郭春影 秦明宽 吴玉 裴柳宁 |
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| 作者单位： | 1. 中核集团核工业北京地质研究院, 北京 100029;2. 中核集团铀资源勘查与评价技术重点实验室, 北京 100029;3. 自然资源部东北亚矿产资源评价重点实验室(吉林大学), 长春 130061 |
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| 基金项目： | 深地资源勘查开采专项（2017YFC0602600）；"十三五"国防预先研究项目（3210402）；中国核工业地质局地勘费科研项目（地D1802）；国土资源部科技创新团队培育计划项目（201708）；自然资源部东北亚矿产资源评价重点实验室开放课题（DBY-KF-19-18） |
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| 摘 要： | 不同类型铀矿床的沥青铀矿/晶质铀矿具有不同的稀土元素组成，其组成可作为判别铀矿床类型的重要指标。采用基于Python语言的主成分分析（principal component analysis，PCA）与支持向量机（support vector machines，SVM）结合的分类模型，对收集到的全球已知6种类型铀矿床的216组沥青铀矿/晶质铀矿稀土元素数据进行研究。以216组数据为训练集，通过数据清洗、特征缩放、PCA特征提取、网格搜索和交叉验证参数寻优构建SVM分类模型，对24组同变质型胡家峪晶质铀矿进行智能识别。研究结果显示：仅使用稀土元素的14维训练集最优模型判定胡家峪晶质铀矿类型的测试准确率为0.4%；由稀土元素、稀土总量、轻重稀土比、铕异常组成的17维训练集最优模型的测试准确率为75.0%，较14维训练集提高74.6%，模型泛化能力强；而通过传统稀土元素配分曲线、w（ΣREE）-（LREE/HREE）N图解不能有效判定胡家峪晶质铀矿类型。本次研究表明，PCA-SVM算法对增有传统稀土判别指标数据集进行挖掘可有效厘定铀氧化物成因类型，效果明显优于单纯的稀土元素数据集以及传统的稀土配分曲线、w（ΣREE）-（LREE/HREE）N图解。
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| 关 键 词： | 铀氧化物 稀土元素 传统稀土判别指标 主成分分析 支持向量机 分类 |
| 收稿时间： | 2020-12-11 |
Classification of Uranium Deposits Based on PCA-SVM Algorithm for Coupling Data Set of Rare Earth Elements and Rare Earth Discrimination Indexes |
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| Liu Yunpeng,Guo Chunying,Qin Mingkuan,Wu Yu,Pei Liuning.Classification of Uranium Deposits Based on PCA-SVM Algorithm for Coupling Data Set of Rare Earth Elements and Rare Earth Discrimination Indexes[J].Journal of Jilin Unviersity:Earth Science Edition,2021,51(3):723-733. |
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| Authors: | Liu Yunpeng Guo Chunying Qin Mingkuan Wu Yu Pei Liuning |
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| Institution: | 1. Beijing Research Institute of Uranium Geology, CNNC, Beijing 100029, China;2. Key Laboratory of Uranium Resource Exploration and Evaluation Technology, CNNC, Beijing 100029, China;3. Key Laboratory of Mineral Resources Evaluation in Northeast Asia (Jilin University), Ministry of Natural Resources, Changchun 130061, China |
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| Abstract: | The pitchblende/uraninite of different types of uranium deposits has different composition of rare earth elements, which can be used as an important index to distinguish the types of uranium deposits. Using the Python language-based classification model combined with principal component analysis (PCA) and support vector machines (SVM), the data of 216 groups of pitchblende/uraninite rare earth elements collected from six known uranium deposits worldwide were studied. With the 216 groups of data as the training set, the SVM classification model was constructed through data cleaning, feature scaling, PCA feature extraction, grid search and cross-validation for parameter optimization, and 24 groups of syn-metamorphic Hujiayu uraninite were intelligently identified. The test accuracy of the 14-dimensional training set optimal model to determine the type of Hujiayu uraninite using only rare earth elements is 0.4%,and the test accuracy of the optimal model of the 17-dimensional training set composed of rare earth elements, total rare earth elements, ratio of light and heavy rare earth elements,and europium anomalies is 75.0%, an improvement of 74.6% over the 14-dimensional training set. The model has a strong generalization ability. But through traditional rare earth element distribution curve and the w(ΣREE)-(LREE/HREE)N diagram, the type of Hujiayu uraninite cannot be determined effectively. This study shows that the PCA-SVM algorithm can effectively determine the genetic type of uranium oxides by mining the data set with the addition of traditional rare earth discriminating indicators, and the effect is significantly better than the pure rare earth element data set and the traditional rare earth distribution curve, w(ΣREE)-(LREE/HREE)N diagram. |
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| Keywords: | uranium oxide rare earth elements traditional rare earth discriminant index principal component analysis support vector machine classification |
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