Machine learning approaches in testing and enhancing tectonic setting discrimination of peridotites

This study demonstrates the evolution from traditional geoscientific methods to data-driven approaches for determining the tectonic setting of peridotites using their chemical composition. It introduces an application of Machine learning (ML) methods, including unsupervised learning and dimensionality reduction, to identify the tectonic setting of peridotite samples using their mineral chemistry. Integrating mineral chemistry analysis with ML overcomes inefficiencies in processing the vast datasets generated by electron probe micro-analyzers (EPMA). Peridotites from the abyssal (oceanic lithosphere) and fore-arc (subduction) zones provide important insights into the composition and dynamics of the upper mantle, such as melt generation and mantle convection processes, interactions between tectonic plates, and oceanic crust generation. This study evaluates the efficacy of established mineral chemistry diagrams in discerning the tectonic settings of peridotite samples from ophiolitic fragments. Initial evaluations revealed that several conventional diagrams could classify abyssal and fore-arc peridotites, but significant misclassifications occurred, necessitating improved methods. We propose new diagrams utilizing spinel and olivine compositions against the pyroxenes' Al 2 O 3 content, showing enhanced discrimination capabilities. Further, an ML approach was implemented, successfully categorizing abyssal and fore-arc settings with high accuracy (92–96 % based on a 20 % test subset) using olivine and spinel compositional data, outperforming traditional methods. This work highlights the value of ML workflows in supporting petrological analysis for accurate tectonic setting classification. • The paper introduces a new algorithm for automating mineral chemistry data processing. • A new method focusing on the essential phase's composition for the mantle rock generation setting. • The algorithm can discriminate between the abyssal and fore-arc settings more successfully than the traditional methods. • We proposed a new set of diagrams for peridotite classification for the first time, too.