Fast and Automatic Full Waveform Inversion by Dual Augmented Lagrangian

TL;DR

This paper presents a novel dual augmented Lagrangian approach for full waveform inversion that improves convergence speed and reduces computational costs by focusing on Lagrange multipliers instead of direct model parameter estimation.

Contribution

The paper introduces a dual formulation for FWI that simplifies the inversion process and enhances convergence efficiency compared to traditional primal methods.

Findings

Faster convergence in elastic and acoustic FWI examples.

Requires fewer computations than standard primal algorithms.

Achieves accurate subsurface imaging with reduced computational effort.

Abstract

Full Waveform Inversion (FWI) stands as a nonlinear, high-resolution technology for subsurface imaging via surface-recorded data. This paper introduces an augmented Lagrangian dual formulation for FWI, rooted in the viewpoint that Lagrange multipliers serve as fundamental unknowns for the accurate linearization of the FWI problem. Once these multipliers are estimated, the determination of model parameters becomes simple. Therefore, unlike traditional primal algorithms, the proposed dual method circumvents direct engagement with model parameters or wavefields, instead tackling the estimation of Lagrange multipliers through a gradient ascent iteration. This approach yields two significant advantages: i) the background model remains fixed, requiring only one LU matrix factorization for each frequency inversion. ii) Convergence of the algorithm can be improved by leveraging techniques like quasi-Newton l-BFGS methods and Anderson acceleration. Numerical examples from elastic and acoustic FWI utilizing different benchmark models are provided, showing that the dual algorithm converges quickly and requires fewer computations than the standard primal algorithm.