Machine Learning for Identification of Primary Water Concentrations in Mantle Pyroxene

Abstract

AbstractThe approach of estimating the H2O content of basaltic magmas via clinopyroxene (cpx) phenocrysts is a potentially effective way to glimpse the deep Earth water cycle. However, it is difficult to ascertain using traditional geochemical methods whether hydrogen (H) measured in cpx phenocrysts represents a primary signature that can ultimately inform estimates of the mantle water content. In this study, we conducted machine learning on the major element compositions and H2O content of cpx phenocrysts (1904 samples in total). Using the support vector machine (SVM), we defined a classifier (overall accuracy >92%) that can separate cpx that have undergone H diffusion, and thus modification of their original water content, from those that have not experienced H diffusion. Our trained SVM model has broad implications for understanding the primary water content of magma, the variations in water content during magma evolution, and the water cycle in the deep Earth.