Attenuation imaging by wavefield reconstruction inversion with bound constraints and total variation regularization

TL;DR

This paper extends wavefield reconstruction inversion (WRI) to viscoacoustic media for attenuation imaging, employing regularization and linearization techniques to improve stability and reduce artifacts in the reconstruction process.

Contribution

It introduces a novel triconvex optimization framework for attenuation imaging in viscoacoustic media using WRI, with tailored regularizations and iterative correction methods.

Findings

Mitigates cross-talk artifacts in attenuation imaging.

Successfully reconstructs attenuation in synthetic North Sea data.

Enhances stability of WRI with regularization and linearization.

Abstract

Wavefield reconstruction inversion (WRI) extends the search space of Full Waveform Inversion (FWI) by allowing for wave equation errors during wavefield reconstruction to match the data from the first iteration. Then, the wavespeeds are updated from the wavefields by minimizing the source residuals. Performing these two tasks in alternating mode breaks down the nonlinear FWI as a sequence of two linear subproblems, relaying on the bilinearity of the wave equation. We solve this biconvex optimization with the alternating-direction method of multipliers (ADMM) to cancel out efficiently the data and source residuals in iterations and stabilize the parameter estimation with appropriate regularizations. Here, we extend WRI to viscoacoustic media for attenuation imaging. Attenuation reconstruction is challenging because of the small imprint of attenuation in the data and the cross-talks with velocities. To address these issues, we recast the multivariate viscoacoustic WRI as a triconvex optimization and update wavefields, squared slowness, and attenuation factor in alternating mode at each WRI iteration. This requires to linearize the attenuation-estimation subproblem via an approximated trilinear viscoacoustic wave equation. The iterative defect correction embedded in ADMM corrects the errors generated by this linearization, while the operator splitting allows us to tailor $\ell{1}$ regularization to each parameter class. A toy numerical example shows that these strategies mitigate cross-talk artifacts and noise from the attenuation reconstruction. A more realistic synthetic example representative of the North Sea validates the method.