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Machine learning thermobarometry: Methods, applications, and a benchmarking protocol
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DOI：10.1016/j.lithos.2026.108665.png)
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Abstract
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• A Modular Benchmarking Protocol (MBP) is proposed for ML thermobarometry. • Coexisting melt compositions reduce T/P RMSE by 40.9%/16.1% vs. NoLiquid baseline. • Physics-informed data augmentation significantly enhances model robustness. • Predictive precision approaches the analytical limit. • Input information density is the primary bottleneck for thermobarometric accuracy.
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