MagMaTaB: A machine learning-based model for magmatic liquid thermobarometry

Determining pressures and temperatures of magmas is crucial for addressing diverse challenges in petrology, geodynamics, and volcanology. However, inherent inaccuracies, especially in barometry, have limited the effectiveness of existing models in unravelling the architecture of crustal igneous systems. In this presentation, I will introduce a novel machine learning model, calibrated using an extensive experimental database, to create regression models for extracting P-T conditions of magmas. Calculations are conducted by considering melt chemistry and the coexisting mineral assemblage as input variables.
Our approach is versatile, applicable across a wide range of compositions from basalt to rhyolite, covering pressures from 0.2 to 15 kbar and temperatures ranging from 675 to 1400°C. Testing and optimization demonstrate that the model can recover pressures with a root-mean-square error of 1.1-1.3 kbar and temperature estimates with errors as low as 21°C. This indicates that melt chemistry-mineral assemblage pairs reliably capture magmatic variables across a broader spectrum of conditions than previously thought. We propose that this reliability arises from the relatively low thermodynamic variance in natural magma compositions, despite the presence of numerous oxide components.
Applying our model to two cases with well-constrained geophysics - Mount St. Helens volcano (USA) and the Askja caldera in Iceland - we analyse dacite whole-rocks from Mount St. Helens, erupted between 1980-1986. These rocks, inferred to represent liquids extracted from a complex mineral mush, yield melt extraction source pressures that align remarkably well with geophysical constraints. For Askja caldera, our model allows to assign basaltic and rhyolitic magma chemistries to distinct seismic wave speed anomalies, highlighting the potential of our model to bridge the gap between petrology and geophysics. Our model, named MagMaTaB, is accessible through a user-friendly web application.