Machine Learning Investigation of Clinopyroxene Compositions to Evaluate and Predict Mantle Metasomatism Worldwide
Machine Learning Investigation of Clinopyroxene Compositions to Evaluate and Predict Mantle Metasomatism Worldwide
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DOI：
10.1029/2021jb023614
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发表时间：
2022-05
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影响因子：
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通讯作者：
Ben Qin;Fang Huang;Shichun Huang;André Python;Yunfeng Chen;J. ZhangZhou
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作者：
Ben Qin;Fang Huang;Shichun Huang;André Python;Yunfeng Chen;J. ZhangZhou
单斜辉石的常量和微量元素组成记录了其物理化学演化，并已被广泛用于地幔交代作用的检测。经典方法通常依赖于一个或几个元素比，如Ca/Al，Mg/Fe，La/Yb和Ti/Eu，以确定岩石或矿物是否已被交代。这些方法已被证明在特定地点有用，但不是全球性的。在这项研究中，我们使用机器学习方法对地幔捕虏体中单斜辉石的化学成分进行分类，并研究它们与地幔交代作用的关系。我们汇编了8,713个单斜辉石样品（21,605次分析）的主量元素数据和1,235个单斜辉石样品（2,967次分析）的微量元素数据。如果根据岩相学证据明显受到明显交代作用的影响，则样本被标记为“阳性”，如果明显不受交代作用的影响，则样本被标记为“阴性”，或者如果两种情况都不适用，则不标记。然后，我们训练了一个XGBoost机器学习模型，该模型使用有限数量的元素比率，比传统方法实现了更高的准确性。我们的研究结果确定了许多具有高平均地幔交代概率的地点，并显示了在全球各地观察到的概率分布的变化。这些结果表明，交代作用可能是全球性的，但交代的概率与地球物理参数，如地壳厚度，岩石圈厚度，或地幔S波速度不相关。因此，交代作用的空间分布似乎主要是由未观察到的因素驱动的。
Clinopyroxene major and trace element compositions document their physicochemical evolution and have been widely used to detect mantle metasomatism. Classical methods typically rely on one or several elemental ratios such as Ca/Al, Mg/Fe, La/Yb, and Ti/Eu to determine whether rocks or minerals have been metasomatized. These methods have proven useful at specific sites, but not globally. In this study, we used machine learning methods to classify the chemical compositions of clinopyroxenes from mantle xenoliths and examine their relationship with mantle metasomatism. We compiled major element data from 8,713 clinopyroxene samples (21,605 analyses) and trace element data from 1,235 clinopyroxene samples (2,967 analyses). Samples were labeled “positive” if clearly affected by patent metasomatism based on petrographic evidence, “negative” if clearly unaffected by metasomatism, or were left unlabeled if neither case applied. We then trained an XGBoost machine learning model, which achieved higher accuracy than traditional methods using a limited number of elemental ratios. Our results identify numerous locations with high mean probabilities of mantle metasomatism and show variability in the probability distributions observed across locations worldwide. These results indicate that metasomatism may be globally widespread, but the probability of metasomatism is not correlated with geophysical parameters such as crustal thickness, lithospheric thickness, or mantle S‐wave velocity. Hence, the spatial distribution of metasomatism appears mainly driven by unobserved factors.