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Pre-Stack Bayesian Cascade AVA Inversion in Complex Laplace Domain for Broadband Data
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 79th EAGE Conference and Exhibition 2017, Jun 2017, Volume 2017, p.1 - 5
Abstract
To make full use of the limited seismic data to recover richer low-frequency information of elastic parameters with AVO/AVA inversion, Bayesian inversion framework, linear initial model regularization and Laplace mixed-domain forward solver are jointed together to put forward the complex-Laplace mixed domain AVA cascade inversion. Firstly, the Laplace mixed-domain convolution model is deduced and the magnification phenomenon of low frequency of seismic data in Laplace mixed domain is analysed. Besides, the Laplace mixed-domain AVA matrix equation is constructed based on Laplace domain operator and Aki-Richard approximation. Then, the objective function of Laplace mixed domain optimization based on the Bayesian framework is deduced with the linear initial models of P-wave velocity, S-wave velocity and density. The proposed algorithm can be separated into two stages: (1) the recovery of richer low-frequency with complex-Laplace domain AVA inversion and (2) the estimation of final elastic parameters with pure frequency domain AVA inversion. The second stage of cascade AVA inversion is restricted with the low-frequency estimation of the first stage, which can improve the convergence accuracy of the estimation parameters. Finally, the feasibility of the proposed approach and the reliability of fluid discrimination are verified by numerous model tests and one field broadband application.