A graph-space optimal transport FWI approach based on \kappa-generalized Gaussian distribution

TL;DR

This paper introduces a novel graph-space optimal transport-based FWI method utilizing a ta-generalized Gaussian distribution, improving robustness against cycle skipping and non-Gaussian errors in seismic inversion.

Contribution

It proposes a new objective function for FWI based on graph-space optimal transport and ta-Gaussian distribution, addressing non-Gaussian errors and cycle skipping issues.

Findings

Enhanced robustness to cycle skipping and non-Gaussian noise.

Reduced computational runtime for transport plan calculation.

Demonstrated effectiveness in FWI applications.

Abstract

The statistical basis for conventional full-waveform inversion (FWI) approaches is commonly associated with Gaussian statistics. However, errors are rarely Gaussian in non-linear problems like FWI. In this work, we investigate the portability of a new objective function for FWI applications based on the graph-space optimal transport and $\kappa$-generalized Gaussian probability distribution. In particular, we demonstrate that the proposed objective function is robust in mitigating two critical problems in FWI, which are associated with cycle skipping issues and non-Gaussian errors. The results reveal that our proposal can mitigate the negative influence of cycle-skipping ambiguity and non-Gaussian noises and reduce the computational runtime for computing the transport plan associated with the optimal transport theory.