Prestack Waveform Inversion by Using an Optimized Linear Inversion Scheme

Abstract

Seismic waveform inversion has been one of the most studied topics in recent years, which can be implemented both in common shot gather and prestack common midpoint gather. However, there are still several intractable issues to be addressed urgently, such as high-computational complexity, nonuniqueness, and robustness. In this paper, we have developed a model-based prestack waveform inversion (PWI) with a generalized propagation matrix scheme as forward operator, where a regularized function is minimized with the limited memory-Broyden–Fletcher–Goldfarb–Shanno technique to determine a model update corresponding to an adaptively determined regularization weight in each iterative step. To avoid falling into local extrema in the process of solving the objective function, we introduce an optimal transport method into the objective function to improve its convexity and use L-curve method to acquire the optimal regularization weight adaptively. The model tests show that the proposed scheme performs better than the conventional method significantly both on convergence and on accuracy. Furthermore, we apply the PWI with the proposed inversion scheme to the well-logging data and real seismic data. The results demonstrate that the proposed inversion scheme is not only capable of obtaining an accurate description of subsurface properties but also has a good convergence and robustness.