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Stochastic Inversion of Elastic Impedance Based on Metropolis Sampling Algorithm
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 76th EAGE Conference and Exhibition 2014, Jun 2014, Volume 2014, p.1 - 5
Abstract
Stochastic inversion of elastic impedance based on Metropolis sampling algorithm is a Monte Carlo based non-linear inversion method, which can effectively integrate the high frequency information of well logging data and have a higher resolution. This method is formulated in a Bayesian theory framework. Firstly, we can get the priori information through Fast Fourier Transform- Moving Average (FFT-MA) and Gradual Deformation Method (GDM). Then we apply Metropolis algorithm in order to obtain an exhaustive description of the posteriori probability density. FFT-MA is a kind of efficient simulation method. Combined with GDM, it can constantly modify the reservoir model and remain the spatial structure unchanged until it matches the observed seismic data. According to the numerical calculations, we can conclude that FFT-MA simulation can reduce the time consumption. Combined with GDM, the inversion result can be converged rapidly, and the final results match the model well and have a higher resolution. In addition, this method adopts two-step method to inverse elastic parameters, so it can improve computational efficiency.