Geochemical Discrimination and Characteristics of Magmatic Tectonic Settings: A Machine‐Learning‐Based Approach. (26th April 2018) - Ueki, Kenta; Hino, Hideitsu; Kuwatani, Tatsu
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. Plain Language Summary: This paper presents the new results of a machine‐learning‐based approach to the geochemical tectonic discrimination of volcanic rocks. We used three different machine learning methods for quantitative tectonic discrimination and feature selection using data for volcanic rocks generated in eight different tectonic settings. The results show that volcanic rocks formed in different tectonic settings have unique geochemical signatures. Key Points: Machine learning techniques can discriminate between magmas formed in different tectonic settings This approach yields the geochemical characteristics of magmatism in different tectonic settings Representative basalt compositions and geochemical signatures for each tectonic setting are given