Assessing and improving the robustness of Bayesian evidential learning in one dimension for inverting TDEM data: introducing a new threshold procedure

Understanding the subsurface is of prime importance for many geological and hydro-geological applications. Geophysical methods offer an economical alternative for in-vestigating the subsurface compared to costly borehole investigation methods, but geophysical results are commonly obtained through an inversion whose solution is non-unique. Deterministic inversions providing a unique solution are computationally efficient while stochastic inversions investigating the full uncertainty range are more expensive. In this research, we investigate the robustness of the recently introduced Bayesian evidential learning in one dimension (BEL1D) to stochastically invert time domain electromagnetic data (TDEM). In particular, we analyse the performance and accuracy of BEL1D when using the coarser discretization used for the computation of the forward solution using SimPEG. We demonstrate that it is possible to speed-up BEL1D by introducing a threshold rejection method on the data misfit to by-pass itera-tions. In addition, we discuss the impact of the prior model space on the results. Fi-nally, we apply the algorithm on field data collected in the Luy river catchment (Vi-etnam) to delineate saltwater intrusions. Our results show that the proper selection of timesteps and space discretization is essential to limit the computational cost while maintaining the accuracy of the posterior estimation. The selection of the prior distri-bution has a direct impact on fitting the observed data and is crucial to a realistic un-certainty quantification. The application of BEL1D for stochastic TDEM inversion is an efficient approach as it allows us to estimate the uncertainty at a limited cost.