Full waveform inversion using Random Mixing

Most deterministic full waveform inversion (FWI) approaches require an initial model, which serves as a starting point for the inversion. Determining such an initial model, however, is not straight forward. In this paper, a geostatistical inversion approach called Random Mixing (RM) for FWI is presented. Instead of an initial model, RM requires the assumption of a spatial dependence structure and a univariate marginal distribution. RM uses linear combinations of spatial random fields to constrain the velocity field during inversion. As it is realization-based, RM allows quantification of estimation uncertainty in terms of estimation variance. The presented algorithm uses the finite element method to discretize the seismic forward model and Message Passing Interface is used to parallelize the algorithm. Two synthetic examples are presented to demonstrate the applicability of RM for FWI.

Automated summary

This paper presents a geostatistical inversion approach called Random Mixing (RM) for deterministic full waveform inversion (FWI). RM uses linear combinations of spatial random fields to constrain the velocity field during inversion, allowing quantification of estimation uncertainty. The algorithm is implemented using the finite element method and Message Passing Interface for parallelization.