Robust Wavefield Inversion via Phase Retrieval

• Published in: arXiv.org 2019-11
• Main Authors: Aghamiry, Hossein S, Gholami, Ali, Operto, Stéphane
• Format: Article
• Language: English
• Subjects: Algorithms; Conditioning; Constraint modelling; Green's functions; Inverse problems; Operators (mathematics); Parameter estimation; Parameter robustness; Phase matching; Phase retrieval; Regularization; Signal processing; Workflow
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1907.11048

Extended formulation of Full Waveform Inversion (FWI), called Wavefield Reconstruction Inversion (WRI), offers potential benefits of decreasing the nonlinearity of the inverse problem by replacing the explicit inverse of the ill-conditioned wave-equation operator of classical FWI (the oscillating Green functions) with a suitably defined data-driven regularized inverse. This regularization relaxes the wave-equation constraint to reconstruct wavefields that match the data, hence mitigating the risk of cycle skipping. The subsurface model parameters are then updated in a direction that reduces these constraint violations. However, in the case of a rough initial model, the phase errors in the reconstructed wavefields may trap the waveform inversion in a local minimum leading to inaccurate subsurface models. In this paper, in order to avoid matching such incorrect phase information during the early WRI iterations, we design a new cost function based upon phase retrieval, namely a process which seeks to reconstruct a signal from the amplitude of linear measurements. This new formulation, called Wavefield Inversion with Phase Retrieval (WIPR), further improves the robustness of the parameter estimation subproblem by a suitable phase correction. We implement the resulting WIPR problem with an alternating-direction approach, which combines the Majorization-Minimization (MM) algorithm to linearise the phase-retrieval term and a variable splitting technique based upon the alternating direction method of multipliers (ADMM). This new workflow equipped with Tikhonov-total variation (TT) regularization, which is the combination of second-order Tikhonov and total variation regularizations and bound constraints, successfully reconstructs the 2004 BP salt model from a sparse fixed-spread acquisition using a 3~Hz starting frequency and a homogeneous initial velocity model.