A novel data-driven method to estimate GIA signal from Earth observation data

Geophysical inversions are usually solved with the help of a-priori constraints and several assumptions that simplify the physics of the problem. This is true for all the inversion approaches that estimate GIA signal from contemporary datasets such as GNSS vertical land motion (VLM) time-series and GRACE geopotential time-series. One of the assumptions in these GIA inversions is that the change in VLM due to GIA can be written in terms of surface mass change and average mantle density. Furthermore, the surface density change is obtained from GRACE data using the relations derived in Wahr et al., 1998, which actually is only applicable for surface processes (such as hydrology) and not for sub-surface processes such as GIA. This leaves us with a tricky signal-separation problem. Although many studies try to overcome this by constraining the inversion with the help of constrains from a priori GIA models, the output is not free from influence of GIA models that are known to have huge uncertainties. In this presentation, we discuss this problem in detail, then provide a novel mathematical framework that solves for GIA without any a priori GIA model. We validate our method in a synthetic environment first and then estimate a completely data-driven GIA field from contemporary Earth-observation data.