Scalable Bayesian full waveform inversion via dual augmented Lagrangian SVGD

TL;DR

This paper introduces a scalable Bayesian full waveform inversion method combining Stein variational gradient descent with dual augmented Lagrangian techniques, enabling efficient uncertainty quantification with reduced computational costs.

Contribution

It develops a novel integration of SVGD with dual augmented Lagrangian methods, fixing the wave operator to improve efficiency and scalability in Bayesian inversion.

Findings

Provides well-calibrated uncertainty estimates.

Achieves inversion quality comparable to standard methods.

Reduces computational cost significantly.

Abstract

Full waveform inversion is an ill-posed inverse problem whose solution non-uniqueness -- i.e., arising from band-limited, finite-aperture, noisy data -- calls for uncertainty quantification to avoid overconfident geological interpretations. Bayesian inference addresses this need by characterizing the solution as a posterior distribution rather than a single point estimate. Sampling from this distribution, however, remains computationally challenging: Markov chain Monte Carlo and non-amortized variational inference require repeated wave equation solves, while amortized variational inference approaches that avoid repeated solves rely on training data that are inherently scarce in geoscience and face unresolved generalization challenges in high dimensions. To address these limitations, we integrate Stein variational gradient descent with the alternating direction method of multipliers under a dual augmented Lagrangian formulation. By fixing the wave operator at a background model that is updated between frequency batches, it need only be factorized once per particle per frequency, eliminating per-iteration refactorization and reducing the total cost to that of a handful of deterministic inversions while inheriting the favorable conditioning of extended-space formulations. Applied to the Marmousi~II model, the proposed method provides well-calibrated uncertainty estimates and achieves inversion quality comparable to that of the standard augmented Lagrangian SVGD at a fraction of the computational cost.