Accelerating seismic inversion and uncertainty quantification with efficient high-rank Hessian approximations

TL;DR

This paper introduces efficient high-rank Hessian approximation methods to accelerate seismic inversion and improve uncertainty quantification, enabling more accurate and computationally feasible Bayesian analysis in large-scale seismic problems.

Contribution

It develops a novel high-rank Hessian approximation method by unifying two existing approaches, enhancing computational efficiency and accuracy in seismic inversion and UQ.

Findings

High-rank Hessian approximations reduce computational costs in FWI.

Hessian-based MCMC explores the posterior more effectively.

High-rank methods improve variance estimation and statistical reliability.

Abstract

Efficient high-rank approximations of the Hessian can accelerate seismic full waveform inversion (FWI) and uncertainty quantification (UQ). In FWI, approximations of the inverse of the Hessian may be used as preconditioners for Newton-type or quasi-Newton algorithms, reducing computational costs and improving recovery in deeper subsurface regions. In Bayesian UQ, Hessian approximations enable the construction of Markov chain Monte Carlo (MCMC) proposals that capture the directional scalings of the posterior, enhancing the efficiency of MCMC. Computing the exact Hessian is intractable for large-scale problems because the Hessian is accessible only through matrix-vector products, and performing each matrix-vector product requires costly solution of wave equations. Moreover, the Hessian is high-rank, which means that low-rank methods, often employed in large-scale inverse problems, are inefficient. We adapt two existing high-rank Hessian approximations -- the point spread function method and the pseudo-differential operator probing method. Building on an observed duality between these approaches, we develop a novel method that unifies their complementary strengths. We validate these methods on a synthetic quadratic model and on the Marmousi model. Numerical experiments show that these high-rank Hessian approximations substantially reduce the computational costs in FWI. In UQ, MCMC samples computed using no Hessian approximation or a low-rank approximation explore the posterior slowly, providing little meaningful statistical information after tens of thousands of iterations and underestimating the variance. At the same time, the effective sample size is overestimated, providing false confidence. In contrast, MCMC samples generated using the high-rank Hessian approximations provide meaningful statistical information about the posterior and more accurately assess the posterior variance.