Bayesian Linearized AVO Inversion of PP and PS Seismic Data

An extension of the Bayesian linearized AVO inversion framework is presented to jointly incorporate PP- and PS-wave seismic reflection data for improved prediction of elastic subsurface properties. The inversion is based on a convolutional model and a weak contrast linearization of the Zoeppritz equations. The inclusion of PS-wave data increases sensitivity to shear properties, enabling enhanced resolution of S-wave velocity and elastic parameter ratios such as VP/VS V P / V S . The inverse problem is formulated in a Bayesian Gaussian framework, with explicit expressions for the posterior mean and covariance of the model variables, including P-wave velocity, S-wave velocity, and density. The joint inversion of PP- and PS-wave data leads to improved constraints on the posterior distributions, compared to the PP-only inversion. Synthetic tests, based on a field dataset from the Norwegian Sea, demonstrate that the joint inversion reduces prediction uncertainty and improves accuracy under realistic noise conditions. Application to a 3-D multicomponent field dataset from a Brazilian oil field shows better match of the inversion results with well log data compared to PP-only results, particularly for VP/VS V P / V S , confirming the added value of incorporating PS information into the Bayesian AVO inversion framework.