Zoeppritz-based Joint Prestack Nonlinear Inversion of PP and PS Waves Using an Adaptive Markov Chain Monte Carlo Method

Abstract

The solutions of prestack inversion are non-unique and unstable, so the joint application of PP and PS seismic data should further constrain the three parameter solutions. In this study, we develop a new method of joint prestack nonlinear inversion of PP and PS waves based on adaptive Markov chain Monte Carlo (MCMC) algorithm to invert for three parameters including P- and S-wave velocity and density by using exact Zoeppritz equation. The adaptive MCMC algorithm can adaptively update proposal distribution with better convergence state, faster convergence speed and stronger practicability, and combine different scales constrained information based on Bayesian framework to improve unstable solutions, and then analyze the uncertainty of AVO inversion results to assist us in risk assessment of reservoir fluid detection. Test on synthetic data shows that all three parameters are well retrieved. Compared with the method of joint prestack inversion of PP and PS waves based on the least square method using approximate Zoeppritz equation, the method of joint prestack nonlinear inversion based on the adaptive MCMC algorithm using exact Zoeppritz equation is not only suitable for reservoirs with strong-contrast interfaces and long-offset ranges but also more stable, accurate, and anti-noise.