Recover the Water Content of Mid-Ocean Ridge Basalts by a Machine Learning Method

Abstract

Water's impact on the physicochemical attributes of mantle rocks makes it a pivotal factor in mantle evolution. Mid-ocean ridge basalts (MORBs) are essential for analyzing the upper mantle's composition, yet many global MORB samples lack direct water content assessment. The common method, using a correlation between H2O and trace elements like Ce to estimate MORB water contents, often presume a constant H2O/Ce ratio. Sometimes this assumption is unreliable due to the heterogeneity in H2O/Ce ratios, even within short ridge segments. For addressing this gap, we utilize compositional data from 1,467 global MORB glasses with measured water contents to develop a Random Forest Regression model. This machine learning-based model can predict water concentrations of MORB glasses based on major and trace element data, without the need for a fixed H2O/trace element ratio. Our model accurately recovers water contents of MORB glasses, showing comparable precision to traditional analytical methods. Applying this model to 1,931 MORB glass samples has significantly expanded the global MORB water content database, revealing the widespread presence of high-water MORBs. Importantly, this innovative approach enables the exploration of water content in MORBs from regions previously without such data, like the Chile Ridge and the Pacific-Antarctic Ridge. Moreover, it allows us to deduce variations of water contents of MORB sources by applying the model to transform fault samples, thereby offering novel insights into the dynamics of the mantle.