Nonlinear inversion based on Metropolis sampling algorithm

作者：Wang Baoli; Sun Ruiying^*; Yin Xingyao; Zhang Guangzhi

来源：Oil Geophysical Prospecting, 2015, 50(1): 111-117.

DOI：10.13810/j.cnki.issn.1000-7210.2015.01.017

• Bayesian theory
• Gradual deformation method
• High resolution
• Metropolis sampling
• Moving averages
• Non linear inversion

Nonlinear inversion based on Metropolis sampling algorithm is formulated in the Bayesian framework. As one kind of Monte Carl non-linear inversions, it can effectively integrate high frequency information of well logging data, and obtain inversion results with a higher resolution. Firstly, we get the priori information through fast Fourier transform moving average (FFT-MA) and gradual deformation method (GDM). Second, we structure likelihood function. Then we apply Metropolis algorithm in order to obtain an exhaustive characterization of the posteriori probability density. FFT-MA is a kind of efficient simulation method. Combined with GDM, it can constantly modify reservoir model and keep the spatial structure unchanged until it matches the observed seismic data. According to the model trial and real data processing, we can conclude that nonlinear inversion based on Metropolis sampling algorithm provide reasonable elastic parameter information, especially it improves the resolution of P-wave velocity. Even when the signal noise ratio (SNR) is relatively low, it can still show reasonable elastic parameter information, which proves the effectiveness of the proposed method. The inversion resolution of P-wave and S-wave impedances is higher than elastic parameters inversion if we do not consider the noise.

• 出版日期2015
• 单位中国石油大学（华东）