Efficient extended-search space full-waveform inversion with unknown source signatures

Abstract

Full waveform inversion (FWI) requires an accurate estimation of source signatures. Due to the coupling between the source signatures and the subsurface model, small errors in the former can translate into large errors in the latter. When direct methods are used to solve the forward problem, classical frequency-domain FWI efficiently processes multiple sources for source signature and wavefield estimations once a single Lower-Upper (LU) decomposition of the wave-equation operator has been performed. However, this efficient FWI formulation is based on the exact solution of the wave equation and hence is highly sensitive to the inaccuracy of the velocity model due to the cycle skipping pathology. Recent extended-space FWI variants tackle this sensitivity issue through a relaxation of the wave equation combined with data assimilation, allowing the wavefields to closely match the data from the first inversion iteration. Then, the subsurface parameters are updated by minimizing the wave-equation violations. When the wavefields and the source signatures are jointly estimated with this approach, the extended wave equation operator becomes source dependent, hence making direct methods and, to a lesser extent, block iterative methods ineffective. In this paper, we propose a simple method to bypass this issue and estimate source signatures efficiently during extended FWI. The proposed method replaces each source with a blended source during each data-assimilated wavefield reconstruction to make the extended wave equation operator source independent. Besides computational efficiency, the additional degrees of freedom introduced by spatially distributing the sources allows for a better signature estimation at the physical location when the velocity model is rough. We implement the source signature estimation with a variable projection method in the recently proposed iteratively-refined wavefield reconstruction inversion (IR-WRI) method. Numerical tests on the Marmousi II and 2004 BP salt synthetic models confirm the efficiency and the robustness against velocity model errors of the new method compared to the case where source signatures are known.

Accurate and efficient data-assimilated wavefield reconstruction in the time domain

“…If the location of the seismic events are known, Aghamiry et al (2021b); Fang et al (2018) have shown that it is possible to jointly estimate the data-assimilated wavefield and the event signatures with high accuracy by solving a linear least-squares problem. But, when the location and the number of seismic events are unknown, the first step of the proposed algorithm tries to find this information by applying a peak finder algorithm on the sparsified predicted source map.…”