Improving full waveform inversion by wavefield reconstruction with the alternating direction method of multipliers

TL;DR

This paper introduces an improved wavefield reconstruction inversion method using an augmented Lagrangian approach with operator splitting, enhancing convergence and robustness in full waveform inversion tasks.

Contribution

It proposes an IR-WRI method that replaces penalty tuning with an augmented Lagrangian, decomposing FWI into linear subproblems for better convergence and noise resilience.

Findings

IR-WRI converges faster than WRI in simple experiments.

IR-WRI achieves more accurate results with fewer iterations.

IR-WRI is robust to cycle skipping and noise in complex models.

Abstract

Full waveform inversion (FWI) is an iterative nonlinear waveform matching procedure subject to wave-equation constraint. FWI is highly nonlinear when the wave-equation constraint is enforced at each iteration. To mitigate nonlinearity, wavefield-reconstruction inversion (WRI) expands the search space by relaxing the wave-equation constraint with a penalty method. The pitfall of this approach resides in the tuning of the penalty parameter because increasing values should be used to foster data fitting during early iterations while progressively enforcing the wave-equation constraint during late iterations. However, large values of penalty parameter lead to ill-conditioned problems. Here, this tuning issue is solved by replacing the penalty method by an augmented Lagrangian method equipped with operator splitting (IR-WRI as iteratively-refined WRI). It is shown that IR-WRI is similar to a penalty method in which data and sources are updated at each iteration by the running sum of the data and source residuals of previous iterations. Moreover, the alternating direction strategy exploits the bilinearity of the wave equation constraint to linearize the subsurface model estimation around the reconstructed wavefield. Accordingly, the original nonlinear FWI is decomposed into a sequence of two linear subproblems, the optimization variable of one subproblem being passed as a passive variable for the next subproblem. The convergence of WRI and IR-WRI are first compared with a simple transmission experiment, which lies in the linear regime of FWI. Under the same conditions, IR-WRI converges to a more accurate minimizer with a smaller number of iterations than WRI. More realistic case studies performed with the Marmousi II and the BP salt models show the resilience of IR-WRI to cycle skipping and noise.