Geochemical discrimination and characteristics of magmatic tectonic settings; a machine learning-based approach

TL;DR

This study employs machine learning techniques to accurately classify volcanic rocks into eight tectonic settings based on their geochemical signatures, revealing key elemental and isotopic markers for each setting.

Contribution

The paper introduces a machine learning framework that effectively discriminates tectonic settings using geochemical data, highlighting the importance of multiple elements and isotopic ratios.

Findings

Support vector machine, random forest, and SMR achieve over 83% accuracy.

SMR identifies key geochemical signatures for each tectonic setting.

At least 17 elements and isotopic ratios are needed for accurate discrimination.

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 datasets 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 20 elements and 5 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.