地学前缘 ›› 2019, Vol. 26 ›› Issue (4): 33-44.DOI: 10.13745/j.esf.sf.2019.7.8

• 岩石大数据研究 • 上一篇    下一篇

三类构造背景辉长岩单斜辉石主量元素和微量元素的数据分析研究

章宝月,孙建鹍,罗熊,金维浚,王龙,杜雪亮,陈万峰,杜君,张旗,朱月琴   

  1. 1. 北京科技大学 计算机与通信工程学院, 北京 100083
    2. 中国科学院 地质与地球物理研究所, 北京 100029
    3. 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
    4. 中国地质调查局 发展研究中心, 北京 100037
  • 收稿日期:2018-04-26 修回日期:2018-05-26 出版日期:2019-07-25 发布日期:2019-07-25
  • 通讯作者: 罗熊(1976—),男,教授,计算机科学与技术专业。
  • 作者简介:章宝月(1996—),男,物联网工程专业。
  • 基金资助:
    国家重点研发计划项目“基于‘地质云’平台的深部找矿知识挖掘”(2016YFC0600510)

Data analysis of major and trace element of gabbro clinopyroxene from different tectonic setting

ZHANG Baoyue,SUN Jiankun,LUO Xiong,JIN Weijun,WANG Long,DU Xueliang,CHEN Wanfeng,DU Jun,ZHANG Qi,ZHU Yueqin   

  1. 1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
    2. Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    3. Key Laboratory of Mineral Resources in Western China(Gansu Province), School of Earth Sciences, Lanzhou University, Lanzhou 730000, China
    4. Development and Research Center, China Geological Survey, Beijing 100037, China
  • Received:2018-04-26 Revised:2018-05-26 Online:2019-07-25 Published:2019-07-25
  • Supported by:
     

摘要: 判别岩浆岩产出的构造环境已经成为岩石学、地球化学及其地球动力学研究的重要内容。作为岩浆岩中的一种喷出岩,玄武岩被视为判别构造环境的最佳成员。对其中单斜辉石的研究,由于其数据本身的利用程度有限而效果欠佳。理论上,不同构造环境的辉长岩也会存在一定差异。为此,利用机器学习算法研究全球新生代辉长岩的单斜辉石势在必行。本文主要针对岛弧(IAB)、洋岛(OIB)及大洋中脊(MORB)3种构造背景辉长岩的单斜辉石进行特征筛选和数据分类。从GEOROC数据库中,经数据收集与清洗,我们分别获得岛弧辉长岩单斜辉石数据385条,洋岛辉长岩单斜辉石数据756条,大洋中脊辉长岩单斜辉石数据5 500条。其中绝大部分为主量元素数据,其余为微量元素数据。在特征提取部分,我们选用卡方检验判断特征独立性,F检验估计两个随机变量之间的线性依赖程度,互信息法捕获其他种类的统计相关性。3种检验方法互相印证,得出了统计学可靠的重要分类特征。在数据分类过程中,本文对比了K-近邻、决策树和支持向量机3种主流机器学习分类算法在辉长岩数据上的表现。研究表明,对于上述3种构造背景,Al2O3、TiO2为最有区分度的辉长岩单斜辉石主量元素成分,Sr为最有区分度的微量元素成分。另外,对于3种构造背景的辉长岩单斜辉石主量元素和微量元素数据,机器学习模型分类准确率均达94%。

 

关键词: 单斜辉石, 辉长岩, 岛弧, 洋岛, 大洋中脊, 机器学习

Abstract: Recently, it has become an important part of petrological and geochemical research including the field of geodynamics to identify tectonic discrimination in magmatic rocks formation. Traditionally, basalt is considered the best member of rocks to identify tectonic settings. However, the use of clinopyxene has not been effective in this aspect due to limitations in data usage; and in theory gabbro is not consistent in its characterization under different tectonic environments. For this reason, it is imperative to explore the use of machine learning algorithms in studying global clinopyroxene gabbro. Here, we mainly focus on feature screening and data classification of clinopyxene gabbros from three structural backgrounds: island arc (IAB), oceanic island (OIB) and mid-ocean ridge (MORB). From the GEOROC database, after data collection and processing, we identified 385 entries of island arc, 756 ocean island and 5500 mid-ocean clinopyxene gabbros. Most data were of main elements and the rest trace elements. During feature extraction, we used chi-square test to judge feature independence, F-test to estimate the linear dependence between two random variables, and mutual information method to capture other kinds of statistical correlations. Statistically reliable data features were obtained using these three methods. During data classification, we compared the performances of three mainstream machine learning classification algorithms, namely K-Nearest Neighbor, Decision Tree and Support Vector Machine on gabbro data. The results show that for clinopyxene gabbros in above three tectonic settings, Al2O3 and TiO2 were the most distinctive main elemental compositions in clinopyroxene gabbro for differentiation, while Sr was the most discriminating trace element. The backgrounds classification accuracy of the machine learning model on data of main and trace element both reached 94%.

Key words: clinopyroxene, gabbro, island arc, oceanic island, mid-ocean ridge, machine learning

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