Three-parameter AVO waveform inversion based on Bayesian theorem
基于贝叶斯理论的AVO三参数波形反演

In the actual AVO inversion, the noise and other factors heavily influence the well-posed property of the inversion problem. So AVO inversion constrained by a priori geological information is a practical method to the solution of ill-posed inversion problem. In this paper the solution for the l^p norm is used to describe the likelihood function. The probability of the prior model parameter is independently Cauchy distribution. Based on these theories the impedance reflectivity attribute parameter covariance matrix from well log is established to constrain inversion, and the conjugate gradient algorithm is used to implement multi-parameter non-linear inversion. In order to improve the inversion accuracy and avoid NMO stretch and offset dependent tuning which influences the estimation of the attribute parameters, the attribution parameter inversion is based on synthetic gather before NMO. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved compared to the actual values, even if the signal/noise ratio is not higher. Therefore it can provide a new tool for identifying fluid content in reservoir pores.