Bayesian Model Comparison of Inner Core Anisotropy Models

Tomography models of the inner core commonly assume a model of anisotropy that is transversely isotropic with a fast direction parallel to the Earth's rotation axis.  However, decades of debate have proposed various forms of this anisotropy, including the presence of a distinct innermost inner core or rotations of the direction of the fast axis.  These assumputions have yet to be directly compared to determine which is best supported by the available data.  Bayesian model comparison via the Bayesian Evidence provides this assessment but has historically been difficult to calculate, particularly in high-dimesional settings, and thus largely ignored in seismic tomography. New machine learning-based techniques can now be used to estimate the Evidence with greater stability and less uncertainty. In this work I demonstrate various methods, including the Savage-Dickey Density Ratio, the learnt harmonic mean estimator and trans-conceptual sampling, applied to the comparison of inner core anisotropy tomography models.