Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Hao Wu, Yingpin Chen, Shu Li and Zhenming Peng
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Hao Wu: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Yingpin Chen: School of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China
Shu Li: School of Information Science and Engineering, Jishou University, Jishou 416000, China
Zhenming Peng: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

Energies, 2019, vol. 12, issue 14, 1-15

Abstract: The Markov chain Monte Carlo (MCMC) method based on Metropolis–Hastings (MH) sampling is a popular approach in solving seismic acoustic impedance (AI) inversion problem, as it can improve the inversion resolution by statistical prior information. However, the sampling function of the traditional MH sampling is a fixed parameter distribution. The parameter ignores the statistical information of AI that expands sampling range and reduces the inversion efficiency and resolution. To reduce the sampling range and improve the efficiency, we apply the statistical information of AI to the sampling function and build a Gaussian MH sampling with data driving (GMHDD) approach to the sampling function. Moreover, combining GMHDD and MCMC, we propose a novel Bayesian AI inversion method based on GMHDD. Finally, we use the Marmousi2 data and field data to test the proposed method based on GMHDD and other methods based on traditional MH. The results reveal that the proposed method can improve the efficiency and resolution of impedance inversion than other methods.

Keywords: metropolis–hastings sampling; bayesian impedance inversion; markov chain monte carlo; gaussian distribution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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