Mapping Global Lithospheric Mantle Pressure‐Temperature Conditions by Machine‐Learning Thermobarometry

Abstract Comprehending the temperature distribution within Earth's lithospheric mantle is of paramount importance for understanding the dynamics of Earth's interior. Traditional mineral‐based thermobarometers effectively constrain temperature and pressure for particular compositions, but their application is limited at the global scale. Here, we trained machine‐learning (ML) algorithms on 985 published high‐temperature and high‐pressure experiments for use as thermometers and barometers to overcome the limitations of classic methods. We compared our ML models to classic thermobarometers to assess the accuracies of predicted pressures and temperatures. The comparison shows that the ML models outperform classic methods and better fit various mineral pairs. Global application of the ML models unveils mantle conditions beneath cratons. Furthermore, depths to the lithosphere‐asthenosphere boundary (LAB) calibrated based on the ML thermobarometry results are generally deeper by ∼40 km than those derived geophysically, implying the existence of melt‐bearing or hydrated mineral zones at the LAB.