THERMOBAR: A CRITICAL EVALUATION OF MINERAL-MELT THERMOBAROMETRY AND HYGROMETRY IN ARC MAGMAS USING A NEW OPEN-SOURCE PYTHON 3 TOOL

The chemistry of erupted minerals and melts are commonly used to determine the pressures, temperatures and H₂O contents of magma storage regions beneath volcanic centres. In the last few decades, more than 100 empirical and thermodynamic expressions have been calibrated using measurements of phases in experimental studies where these intensive parameters are known. However, users wishing to apply these expressions to deduce storage conditions in natural samples have to format their mineral and melt data in specific ways, and perform calculations using a number of different tools (e.g. multiple Excel spreadsheets, Matlab, R).

Here, we present Thermobar, a user friendly and open-source tool written in Python3 that requires no prior coding experience. Thermobar allows users to perform calculations for equilibrium involving liquids, olivines, spinels, pyroxenes, feldspars, and amphiboles based on oxide data provided in a minimally-formatted Excel spreadsheet. We also provide a number of functions for mineral-melt matching, plotting equilibrium diagrams, and propagating errors using Monte-Carlo approaches.

We demonstrate the versatility of this new tool by using it to assess the strengths and weaknesses of existing thermobarometers and hygrometers at conditions relevant to the storage and evolution of arc magmas. In particular, we capitalize on the large number of experiments performed at crustal conditions (0-12 kbar) with well constrained H₂O contents and oxygen fugacities since the compilation of the LEPR database in 2008, which was used to calibrate most existing thermobarometers. Using this new compilation as a test dataset, we show that existing expressions are associated with significantly larger standard errors and lower R² values than previously stated. As well as identifying limitations in the original calibration dataset, we attribute many of these issues to the fact that published statistics are normally calculated where a single unknown is being solved for, rather than the more realistic scenario where equations must be solved iteratively for ≥2 unknowns (e.g. co-solving P and T, or H₂O and T). This new dataset allows us to present recommendations and recalibrations to optimize the application of mineral-melt equilibria to deduce magma storage conditions in volcanic arcs.