Compositions and Classification of Fractionated Boninite Series Melts from the Izu–Bonin–Mariana Arc: A Machine Learning Approach

Abstract

Abstract Much of the boninite magmatism in the Izu–Bonin–Mariana arc is preserved as evolved boninite series compositions wherein extensive fractional crystallization of pyroxene and spinel have obscured the diagnostic geochemical indicators of boninite parentage, such as high Mg and low Ti at intermediate silica contents. As a result, the usual geochemical discriminants used for the classification of the broad range of parental boninites are inapplicable to such highly fractionated melts. These issues are compounded by the mixing of demonstrably different whole-rock and glass analyses in classification schemes and petrological interpretations based thereon. Whole-rock compositions are compromised by entrainment of variable proportions of crystalline phases resulting in inconsistent differences from corresponding in situ glass analyses, which arguably better reflect prior melt compositions. To circumvent such issues, we herein present a robust method for the classification of highly fractionated boninite series glasses. This new classification leverages the analysis of trace elements, which are much more sensitive to evolutionary processes than major elements, and benefits from the use of unsupervised machine learning as a classification tool. The results show that the most fractionated boninite series melts preserve geochemical indicators of their parentage, and highlight the pitfalls of interpreting whole-rock and glass analyses interchangeably.