Entropy Regularized Nonlinear Joint PP-PS AVO Inversion Using Zoeppritz Equations

Based on the Bayesian framework, pre-stack inversion aims to find the solution with the maximum posterior probability under the prior constraint. The prior constraint is usually a mathematical expression of the inverted parameters and guides the updating of the inversion results. To obtain a stable, high-resolution, and high-fidelity inversion result, we introduce a new prior constraint named "amplitude entropy" to help perform the pre-stack inversion. The amplitude entropy can make the chaos of the parameters to be inverted close to those of the known well-logging data. Compared with the traditional $L_{2}$ prior constraint, amplitude entropy can improve the resolution of the inversion results, and compared with the conventional $L_{1}$ prior constraint, it can obtain a solution that is more consistent with the geological characteristics. In addition, multicomponent seismic data contain richer lithology and fluid information than single-component data. Therefore, in this article, we directly develop the pre-stack inversion method based on the multicomponent seismic data. Furthermore, due to the high nonlinearity of the objective function under the amplitude entropy constraint, the quantum annealing (QA) algorithm is employed to solve the objective function of the joint inversion and find the final solution. Synthetic and field data examples demonstrate that the pre-stack inversion method with the amplitude entropy constraint is effective and stable, especially for processing seismic data with a low signal-to-noise ratio.