Prediction of CO2 content in mid-ocean ridge basalts via a machine learning approach

Abstract One of the primary locations of mafic magma production on Earth is the global mid-ocean ridge system. The basalts erupted along ridges probe the upper mantle and can be used to explore the deep carbon cycle. However, mid-ocean ridge basalts (MORBs) degas heavily during magma ascent. Some incompatible-trace-element–depleted and –enriched MORBs avoid heavy degassing, and show a narrow range of CO2/Ba, which have been used to reconstruct the pre-eruptive CO2 content of primitive MORB. With an increasing amount of data, however, it has become apparent that the CO2/Ba ratios of MORBs vary significantly. We compiled a data set of the geochemical compositions of MORB glasses and melt inclusions that are not degassed significantly and used a supervised machine learning model to accurately predict CO2 contents of individual samples from the concentrations of selected elements. This approach reveals that predicted CO2 contents and CO2/Ba ratios of global MORBs are highly variable, highlighting the significance of mantle heterogeneity, which can be attributed to the interactions with deep-sourced plumes or recycled crust (oceanic crust with or without sediments). Our findings underscore the potential of machine learning as a powerful tool for investigating the intricate interplay between carbon, mantle composition, and Earth's long-term geological processes.