Geochemical Discrimination and Characteristics of Magmatic Tectonic Settings: A Machine‐Learning‐Based Approach

Ueki Kenta; Hino Hideitsu; Kuwatani Tatsu

摘要

Abstract >Geochemically discriminating between magmatism in different tectonic settings remains a fundamental part of understanding the processes of magma generation within the Earth's mantle. Here we present an approach where machine learning (ML) methods are used for quantitative tectonic discrimination and feature selection using global geochemical data sets containing data for volcanic rocks generated in eight different tectonic settings. This study uses support vector machine, random forest, and sparse multinomial regression (SMR) approaches. All these ML methods with data for 24 elements and five isotopic ratios allowed the successful geochemical discrimination between igneous rocks formed in eight different tectonic settings with a discriminant ratio better than 83% for all settings barring oceanic plateaus and back‐arc basins. SMR is a particularly powerful and interpretable ML method because it quantitatively identifies geochemical signatures that characterize the tectonic settings of interest and the characteristics of each sample as a probability of the membership of the sample for each setting. We also present the most representative basalt composition for each tectonic setting. The new data provide reference points for future geochemical discussions. Our results indicate that at least 17 elements and isotopic ratios are required to characterize each tectonic setting, suggesting that geochemical tectonic discrimination cannot be achieved using only a small number of elemental compositions and/or isotopic ratios. The results show that volcanic rocks formed in different tectonic settings have unique geochemical signatures, indicating that both volcanic rock geochemistry and magma generation processes are closely connected to the tectonic setting. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“main”> <标题类型=“main”>抽象</ title> >在不同构造环境中的岩浆之间的地球化学歧视仍然是理解地球内部内部岩浆生成过程的基本部分。在这里，我们提出了一种方法，其中机器学习（ML）方法用于定量构造鉴别和特征选择，使用包含八个不同构造设置中产生的火山岩数据的数据集。本研究使用支持向量机，随机林和稀疏多项回归（SMR）方法。所有这些具有24个元素和五位同位素比例的ML方法允许在八种不同构造环境中形成的火岩之间的成功地球化学辨别，其判别比例优于83％，所有设置禁止海洋高原和后弧盆地。 SMR是一种特别强大和可解释的ML方法，因为它定量地识别出地地球化学签名，其表征了每个样本的构造环境和每个样本的特征，作为每个设置的样本成员资格的概率。我们还为每个构造设置呈现最代表性的玄武岩组合物。新数据为未来的地球化学讨论提供了参考点。我们的结果表明，每个构造设置需要至少17个元素和同位素比例，表明不能仅使用少量的元素组合物和/或同位素比来实现地球化学构造鉴别。结果表明，在不同构造环境中形成的火山岩具有独特的地球化学签名，表明火山岩地球化学和岩浆生成过程密切连接到构造环境。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-23285/'>《Geochemistry, geophysics, geosystems》</a> <b style="margin: 0 2px;">|</b><span>2018年第4期</span><b style="margin: 0 2px;">|</b><span>共21页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Ueki Kenta&option=202" target="_blank" rel="nofollow">Ueki Kenta;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Hino Hideitsu&option=202" target="_blank" rel="nofollow">Hino Hideitsu;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Kuwatani Tatsu&option=202" target="_blank" rel="nofollow">Kuwatani Tatsu;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Department of Solid Earth GeochemistryJapan Agency for Marine‐Earth Science and TechnologyYokosuka Kanagawa Japan;</p> <p>Graduate School of Systems and Information EngineeringUniversity of TsukubaTsukuba Ibaraki Japan;</p> <p>Department of Solid Earth GeochemistryJapan Agency for Marine‐Earth Science and TechnologyYokosuka Kanagawa Japan;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/163.html" title="地球物理学">地球物理学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=machine learning&option=203" rel="nofollow">machine learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=feature selection&option=203" rel="nofollow">feature selection;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=geochemical discrimination&option=203" rel="nofollow">geochemical discrimination;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=tectonic settings&option=203" rel="nofollow">tectonic settings;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=magmatism&option=203" rel="nofollow">magmatism;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=data‐driven&option=203" rel="nofollow">data‐driven;</a> </p> <div class="translation"> 机译：机器学习;特征选择;地球化学鉴别;构造环境;魔法学;数据驱动; 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