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

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  • Hao Wu

    (School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
    The Laboratory of Imaging Detection and Intelligent Perception University of Electronic Science and Technology of China, Chengdu 610054, China
    Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, 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
    The Laboratory of Imaging Detection and Intelligent Perception University of Electronic Science and Technology of China, Chengdu 610054, China
    Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China)

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.

Suggested Citation

  • Hao Wu & Yingpin Chen & Shu Li & Zhenming Peng, 2019. "Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving," Energies, MDPI, vol. 12(14), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2744-:d:249295
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    References listed on IDEAS

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    1. Mark Girolami & Ben Calderhead, 2011. "Riemann manifold Langevin and Hamiltonian Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 123-214, March.
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    Cited by:

    1. Xuegong Zhao & Hao Wu & Xinyan Li & Zhenming Peng & Yalin Li, 2020. "Seismic Reflection Coefficient Inversion Using Basis Pursuit Denoising in the Joint Time-Frequency Domain," Energies, MDPI, vol. 13(19), pages 1-15, September.
    2. Shaoyong Liu & Wenting Zhu & Zhe Yan & Peng Xu & Huazhong Wang, 2021. "An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration," Energies, MDPI, vol. 14(14), pages 1-12, July.

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