Extracting the geochemical characteristics of magmas in different global tectono-magmatic settings using sparse modeling

Abstract

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In this study, key geochemical features of magmas formed in eight different tectono-magmatic settings (mid-ocean ridges, oceanic islands, oceanic plateaus, continental flood basalt provinces, intra-oceanic arcs, continental arcs, island arcs, and back-arc basins) are presented that were obtained using a machine-learning-based statistical model. We analyzed geochemical data for volcanic rocks compiled from the global geochemical databases based on statistical model fitting. We used the sparse modeling approach, with which we can objectively identify a small number of fundamental features from a large number of observations. This approach allowed us to identify a small number of representative geochemical features from a total of 857 variables, including major and trace element concentrations, isotope ratios, and all possible ratios and multiplications of elements. Based on the statistical analysis, we present a small number (2–4) of key geochemical features for each tectono-magmatic setting. The extracted geochemical features and associated diagrams can be used to examine geochemical similarities and differences between tectono-magmatic settings and to identify the geochemical characteristics of unknown samples. Based on the extracted geochemical characteristics, we discuss the processes that may lead to the formation of magmas in different tectono-magmatic settings. Our statistical analysis shows that the geochemical signatures of magmas vary with the tectono-magmatic setting, as do the geochemical processes involved in magma generation.

Keywords

• tectono-magmatic settings
• machine learning
• geochemical features of magmas
• magma generation processes
• feature extraction