Source-independent Q-compensated viscoacoustic least-squares reverse time migration

Abstract

The strong viscosity of the subsurface introduces amplitude absorption and phase-velocity dispersion. Incorrect compensation of the inherent attenuation (the strength of the seismic attenuation can be quantified by the inverse of the quality factor Q, which is defined as [Formula: see text] times the ratio of the stored energy to the lost energy in a single cycle of deformation) can significantly affect imaging quality. Although Q-least-squares reverse time migration ( Q-LSRTM) allows for the compensation of attenuation effects during the iterations, the traditional [Formula: see text]-norm minimization, which is highly sensitive to the source wavelet, poses a challenge in accurately estimating the source wavelet from the field data. Thus, we develop a source-independent Q-LSRTM, in which a convolutional objective function is introduced to replace the [Formula: see text]-norm constraint to mitigate the source wavelet effect. According to the Born approximation, we first linearize the constant-order decoupled fractional Laplacian viscoacoustic wave equation to derive the demigration operator and then construct the corresponding adjoint equation and gradient based on the convolutional objective function, iteratively estimating the reflectivity images. Our method relaxes the sensitivity to the wavelet compared with the conventional [Formula: see text]-norm scheme due to the convolutional objective function, which has the ability to construct the same new source for simulated and observed data. Numerical tests on a layered model, the Marmousi model, and field data demonstrate that our source-independent Q-LSRTM enables us to obtain high-quality reflectivity images even when using incorrect source wavelets.