Enhancing machine learning thermobarometry for clinopyroxene-bearing magmas

In this study, we proposed a general workflow that aims to enhance the ML-based geothermobarometer modelling. Our workflow focuses on three key areas. Firstly, we developed a robust pre-processing pipeline that addresses data imbalance, feature engineering, and data augmentation. Secondly, we assessed modelling errors using a Monte Carlo approach to quantify the impact of analytical uncertainties on the final pressure and temperature estimates. Thirdly, we implemented a robust strategy to validate and test the ML models to avoid over- and under-fitting issues while correcting biases associated with the application of specific ML models (i.e., tree-based ensembles). To facilitate the use of our workflow, we have developed a web app ( https://bit.ly/ml-pt-web ) and a Python module ( https://bit.ly/ml-pt-py ). The robustness of this strategy has been tested on two calibrations: clinopyroxene (cpx) and clinopyroxene-liquid (cpx-liq). Our results show a significant reduction in errors compared to the baseline model, as well as good generalization ability on an independent external dataset. The Root Mean Squared Errors are 57 °C and 2.5 kbar for the cpx calibration, and 36 °C and 2.1 kbar for the cpx-liq calibration. Finally, our models show improved outcomes on the external dataset compared to existing ML and classical cpx and cpx-liq thermobarometers. • Novel and robust workflow strategy to enhance machine Learning-based geo-thermobarometers. • Incorporation of analytical error propagation in Machine-learning-based geo-thermobarometer models. • Error reduction for clinopyroxene Machine Learning thermobarometers. • Correction of the “regression to the mean” phenomenon associated with tree-based algorithms. • User-friendly interface for thermobarometry.