Full waveform inversion using Random Mixing. (April 2022)

• Record Type: Journal Article
• Authors: Chang, A.; Gross, L.; Hörning, S.
• Is Part Of: Computers & geosciences. Volume 161(2022)
• Publication Date: 2022-04
• Subjects: Full waveform inversion -- Stochastic inverse modeling -- Random Mixing -- Geostatistics; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2022.105041
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: 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. Highlights: New approach for stochastic full waveform inversion. No initial model required. No regularization required. Enables quantification of the estimation uncertainty.