A regularization by denoising (RED) scheme for 3-D FWI model updates in large-contrast media

Abstract

SUMMARY Full waveform inversion (FWI) endeavours to estimate high-resolution physical properties of subsurface structures. The technique minimizes the data misfit between observed and modelled seismograms. Despite its success, the application of FWI in areas with high-velocity contrasts remains a challenging problem. Often, quadratic regularization methods are chosen to stabilize inverse problems. Unfortunately, quadratic regularization does not preserve edges and sharp discontinuities adequately. Conversely, a regularization term that uses the l1 norm of the gradient of model parameters can preserve discontinuities. The latter leads to edge-preserving methods based on total variation regularization. This work adopts the framework named regularization by denoising (RED) to solve the FWI problem in high-contrast media. The RED technique only requires an image denoising engine, which, in our case, is a modified weighted total variation filter. One advantage of adopting the RED algorithm for solving FWI problems is its simplicity in the numerical implementation and selection of trade-off parameters. We have benchmarked our algorithm via the 2-D BP/EAGE model, a model with significant velocity contrasts and complex salt bodies. We have also tested the proposed regularization method with the 3-D SEG/EAGE overthrust P-wave velocity model. We also compare the proposed RED method and FWI with total variation regularization.