| 基于镁铁-超镁铁岩中单斜辉石主量元素含量的决策树集成算法对比 |
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| 引用本文： | 孙建鹍,杜雪亮,章宝月,王龙,金维浚,张旗,罗熊,朱月琴.基于镁铁-超镁铁岩中单斜辉石主量元素含量的决策树集成算法对比[J].地质通报,2019,38(12):1981-1991. |
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| 作者姓名： | 孙建鹍 杜雪亮 章宝月 王龙 金维浚 张旗 罗熊 朱月琴 |
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| 作者单位： | 北京科技大学计算机与通信工程学院, 北京 100083;自然资源部地质信息技术重点实验室, 北京 100037,兰州大学地质科学与矿产资源学院/甘肃省西部矿产资源重点实验室, 甘肃 兰州 730000,北京科技大学计算机与通信工程学院, 北京 100083,北京科技大学计算机与通信工程学院, 北京 100083,中国科学院地质与地球物理研究所, 北京 100029,中国科学院地质与地球物理研究所, 北京 100029,北京科技大学计算机与通信工程学院, 北京 100083;自然资源部地质信息技术重点实验室, 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： | 国家重点研究开发计划《基于“地质云”平台的深部找矿知识挖掘》（编号：2016YFC0600510）、国家自然科学基金项目《大数据环境下的滑坡危险性评估模型构建方法研究》（批准号：41872253）、国土资源部地质信息技术重点实验室课题《基于知识图谱深度优化技术的地质大数据智能检索服务应用研究》（编号：2017320）、中国地质调查局项目《国家地质大数据汇聚与管理》（编号：DD20190381A）、《资源环境重大问题综合区划与开发保护策略研究》（编号：DD20190463） |
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| 摘 要： | 依靠岩浆构造环境的地球化学成分认识岩浆形成过程是岩石地球化学中的重要应用。当前利用岩石地球化学成分判别构造环境的工作还不够深入。用4种基于决策树的机器学习方法对来自全球新生代洋岛玄武岩(OIB)、岛弧玄武岩(IAB)及大洋中脊玄武岩(MORB)等镁铁-超镁铁岩中单斜辉石的13种主量元素构成数据集进行了岩浆构造环境判别和主要特征排序。通过对比4种基于决策树的机器学习方法,验证了树类算法对于地球化学成分识别问题的有效性,并总结出4种方法在处理岩浆构造环境判别问题时的优劣:决策树算法判别过程更易于理解,但是其准确率欠佳;boosting算法中的AdaBoost和GBDT对于岩浆构造环境的鉴别准确度较高,但构造过程复杂;bagging集成算法随机森林在权衡性能和模型可理解性时不失为一个良好的选择。此外,还通过4种算法的特征重要性排序得出Cr_2O_3,TFeO,TiO_2,FeO和Al_2O_3是进行岩浆构造环境判别的重要成分。
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| 关 键 词： | 树算法 bagging算法 boosting算法 单斜辉石 岩浆构造环境判别 地球化学特征 |
| 收稿时间： | 2019/4/16 0:00:00 |
| 修稿时间： | 2019/7/23 0:00:00 |
A comparison of tree-based ensemble algorithms on the main element content of monoclinal pyroxene in mafic-ultramafic rocks |
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| SUN Jiankun,DU Xueliang,ZHANG Baoyue,WANG Long,JIN Weijun,ZHANG Qi,LUO Xiong and ZHU Yueqin.A comparison of tree-based ensemble algorithms on the main element content of monoclinal pyroxene in mafic-ultramafic rocks[J].Geologcal Bulletin OF China,2019,38(12):1981-1991. |
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| Authors: | SUN Jiankun DU Xueliang ZHANG Baoyue WANG Long JIN Weijun ZHANG Qi LUO Xiong and ZHU Yueqin |
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| Institution: | School of Computer and Communication Engineering, University of Science and Technology Beijing(USTB), Beijing 100083, China;Key Laboratory of Geological Information Technology, MNR, Beijing 100037, China,Key Laboratory of Mineral Resources in Western China(Gansu Province)/School of Earth Sciences, Lanzhou University, Lanzhou 730000, Gansu, China,School of Computer and Communication Engineering, University of Science and Technology Beijing(USTB), Beijing 100083, China,School of Computer and Communication Engineering, University of Science and Technology Beijing(USTB), Beijing 100083, China,Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China,Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China,School of Computer and Communication Engineering, University of Science and Technology Beijing(USTB), Beijing 100083, China;Key Laboratory of Geological Information Technology, MNR, Beijing 100037, China and Key Laboratory of Geological Information Technology, MNR, Beijing 100037, China |
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| Abstract: | Relying on the geochemical composition of the magma tectonic environment to understand the formation process of magma is an important application in rock geochemistry. While the current works to make full use of rock geochemical components for the tectonic setting discrimination are not enough. In this study, the authors utilized four tree-based machine learning methods to make magma tectonic environment discriminations and feature sorting on the 13 main ingredients of monoclinal pyroxene in maficultramafic rocks from global Cenozoic ocean island (OIB), island arc (IAB), and mid-ocean ridge (MORB). Through the comparison of the four tree-based machine learning methods, the authors proved the validity of the tree-based methods for the identification of geochemical components and derived the advantages and disadvantages of the four methods in dealing with the identification of rock tectonic environments:decision trees gain better comprehensibility but have lower recognition accuracy, boosting algorithms AdaBoost and GBDT have the best recognition accuracy but lower comprehensibility, and random forest is a better choice during trading off and comprehensibility performance. Besides, Cr2O3, TFeO, TiO2, FeO and Al2O3 are figured out as the most important ingredients for magma tectonic environment discriminations on this dataset. |
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| Keywords: | tree algorithm bagging algorithm boosting algorithm magmatic environment discrimination clinopyroxene geochemical characteristics |
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