V2RhoT_gibbs: a tool for conversion of seismic velocity to temperature and density in a self-consistent thermodynamic manner

Seismic tomography provides important data to understand the physical properties (temperature, density) of the lithosphere and upper mantle. These physical properties are crucial for understanding e.g., strain localization, geothermal potential. However, interpreting tomography models in terms of composition and temperature conditions is highly complex and non-unique. There are mainly two approaches to interpreting seismic tomography models: empirical and thermodynamics-based. In the empirical approach, the tomography models are calibrated with respect to known temperature distributions, such as those derived from age-dependent oceanic lithospheric thickness or heat flow models or temperatures derived from mantle xenoliths in the continental regions. In contrast, the thermodynamics-based approach involves taking into account the composition of the minerals or deriving it from bulk-rock chemical composition using Gibbs-free energy minimization. Recently, a new approach has emerged that combines the second approach with geophysical (potential fields, topography) and seismological data (surface wave dispersion curves). However, this method assumes a steady-state temperature distribution, limiting its ability to infer the second-order temperature and density distribution. To address these challenges, a new thermodynamics-based conversion tool called V2RhoT_gibbs has been developed. This tool is based on open-source Python libraries and coupled with the Gibbs-free energy minimization algorithm Perple_X. It does not assume any thermal state and, using bulk-average chemical composition, derives the temperature and density distribution. Output from this tool could be easily used to incorporate e.g., as voxel in gravity field interpretations, or as lower temperature boundary condition to compute the thermal field.