Inexact Augmented Lagrangian Method-Based Full-waveform Inversion with Randomized Singular Value Decomposition

TL;DR

This paper introduces an enhanced full-waveform inversion method combining randomized SVD, weighted nuclear norm regularization, and inexact augmented Lagrangian optimization to improve resolution, noise suppression, and convergence speed in seismic imaging.

Contribution

It proposes a novel integration of rSVD-WTNNR with iALM into FWI, addressing noise sensitivity and convergence issues in traditional methods.

Findings

More effective noise suppression in seismic data

Higher resolution and more accurate subsurface models

Significantly faster convergence rate

Abstract

Full Waveform Inversion (FWI) is a modeling algorithm used for seismic data processing and subsurface structure inversion. Theoretically, the main advantage of FWI is its ability to obtain useful subsurface structure information, such as velocity and density, from complex seismic data through inversion simulation. However, under complex conditions, FWI is difficult to achieve high-resolution imaging results, and most of the cases are due to random noise, initial model, or inversion parameters and so on. Therefore, we consider an effective image processing and dimension reduction tool, randomized singular value decomposition (rSVD) - weighted truncated nuclear norm regularization (WTNNR), for embedding FWI to achieve high-resolution imaging results. This algorithm obtains a truncated matrix approximating the original matrix by reducing the rank of the velocity increment matrix, thus achieving the truncation of noisy data, with the truncation range controlled by WTNNR. Subsequently, we employ an inexact augmented Lagrangian method (iALM) algorithm in the optimization to compress the solution space range, thus relaxing the dependence of FWI and rSVD-WTNNR on the initial model and accelerating the convergence rate of the objective function. We tested on two sets of synthetic data, and the results show that compared with traditional FWI, our method can more effectively suppress the impact of random noise, thus obtaining higher resolution and more accurate subsurface model information. Meanwhile, due to the introduction of iALM, our method also significantly improves the convergence rate. This work indicates that the combination of rSVD-WTNNR and FWI is an effective imaging strategy which can help to solve the challenges faced by traditional FWI.