Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving
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 tradi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/14/2744 |
| _version_ | 1811306486140239872 |
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| author | Hao Wu Yingpin Chen Shu Li Zhenming Peng |
| author_facet | Hao Wu Yingpin Chen Shu Li Zhenming Peng |
| author_sort | Hao Wu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:45:22Z |
| format | Article |
| id | doaj.art-494d0af9846a43b597a45c2335301e0b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T08:45:22Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-494d0af9846a43b597a45c2335301e0b2022-12-22T02:53:40ZengMDPI AGEnergies1996-10732019-07-011214274410.3390/en12142744en12142744Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data DrivingHao Wu0Yingpin Chen1Shu Li2Zhenming Peng3School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, ChinaSchool of Information Science and Engineering, Jishou University, Jishou 416000, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaThe 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.https://www.mdpi.com/1996-1073/12/14/2744metropolis–hastings samplingbayesian impedance inversionmarkov chain monte carlogaussian distribution |
| spellingShingle | Hao Wu Yingpin Chen Shu Li Zhenming Peng Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving Energies metropolis–hastings sampling bayesian impedance inversion markov chain monte carlo gaussian distribution |
| title | Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving |
| title_full | Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving |
| title_fullStr | Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving |
| title_full_unstemmed | Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving |
| title_short | Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving |
| title_sort | acoustic impedance inversion using gaussian metropolis hastings sampling with data driving |
| topic | metropolis–hastings sampling bayesian impedance inversion markov chain monte carlo gaussian distribution |
| url | https://www.mdpi.com/1996-1073/12/14/2744 |
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