A combined Reduced Order-Bayesian scheme to drastically accelerate stochastic inversions

<div>One of the main challenges in modern geophysics is the understanding and characterization of the present-day physical state of the thermal and compositional structure of the Earth&#8217;s lithospheric and sub-lithospheric mantle. In doing so, high resolution inverse problems need to be solved (with thousands of parameters&#160;to determine).<br>One of the most abundant and better constrained data used for the inversion is the Earth&#8217;s topography. Despite its quality, the topography models included in inversion schemes are usually very simplistic, based on density contrasts and neglecting dynamic components. The reason for this is simply computational efficiency; 3D dynamical models are too expensive to be embedded within inversion schemes.<br>In this context we propose the use of a greedy reduced basis strategy within a probabilistic Bayesian inversion scheme (MCMC) that makes feasible accounting for the fully dynamic topography model within the inversion.</div><div>We tested the proposed approach in a synthetic experiment aiming to recover the base of the African plate. It is well-agreed within the geophysical community that the dynamic component in the region is of first&#160;order importance. Our scheme is able to successfully recover the expected shape of the plate while reducing the computational time to less than 1% when compared to a full Finite Element approach. <div> <div></div> </div> </div>