A New Stochastic Process of Prestack Inversion for Rock Property Estimation
In order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC). The MCMC could provide a stochastic optimization approach, and, with the APSO, have a be...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/5/2392 |
| _version_ | 1797475746087698432 |
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| author | Long Yin Sheng Zhang Kun Xiang Yongqiang Ma Yongzhen Ji Ke Chen Dongyu Zheng |
| author_facet | Long Yin Sheng Zhang Kun Xiang Yongqiang Ma Yongzhen Ji Ke Chen Dongyu Zheng |
| author_sort | Long Yin |
| collection | DOAJ |
| description | In order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC). The MCMC could provide a stochastic optimization approach, and, with the APSO, have a better performance in global optimization methods. This method uses logging data to define a preprocessed model space. It also uses Bayesian statistics and Markov chains with a state transition matrix to update and evolve each generation population in the data domain, then adaptive particle swarm optimization is used to find the global optimal value in the finite model space. The method overcomes the problem of over-fitting deterministic inversion and improves the efficiency of stochastic inversion. Meanwhile, the fusion of multiple sources of information can reduce the non-uniqueness of solutions and improve the inversion accuracy. We derive the APSO algorithm in detail, give the specific workflow of prestack stochastic inversion, and verify the validity of the inversion theory through the inversion test of two-dimensional prestack data in real areas. |
| first_indexed | 2024-03-09T20:49:10Z |
| format | Article |
| id | doaj.art-ff19c01b239c4b629c91f8d7101fa78c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:49:10Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ff19c01b239c4b629c91f8d7101fa78c2023-11-23T22:40:01ZengMDPI AGApplied Sciences2076-34172022-02-01125239210.3390/app12052392A New Stochastic Process of Prestack Inversion for Rock Property EstimationLong Yin0Sheng Zhang1Kun Xiang2Yongqiang Ma3Yongzhen Ji4Ke Chen5Dongyu Zheng6SINOPEC Geophysical Research Institute, Nanjing 211103, ChinaDepartment of Earth Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaSINOPEC Geophysical Research Institute, Nanjing 211103, ChinaSINOPEC Geophysical Research Institute, Nanjing 211103, ChinaSINOPEC Geophysical Research Institute, Nanjing 211103, ChinaSINOPEC Geophysical Research Institute, Nanjing 211103, ChinaInstitute of Sedimentary Geology, Chengdu University of Technology, Chengdu 610059, ChinaIn order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC). The MCMC could provide a stochastic optimization approach, and, with the APSO, have a better performance in global optimization methods. This method uses logging data to define a preprocessed model space. It also uses Bayesian statistics and Markov chains with a state transition matrix to update and evolve each generation population in the data domain, then adaptive particle swarm optimization is used to find the global optimal value in the finite model space. The method overcomes the problem of over-fitting deterministic inversion and improves the efficiency of stochastic inversion. Meanwhile, the fusion of multiple sources of information can reduce the non-uniqueness of solutions and improve the inversion accuracy. We derive the APSO algorithm in detail, give the specific workflow of prestack stochastic inversion, and verify the validity of the inversion theory through the inversion test of two-dimensional prestack data in real areas.https://www.mdpi.com/2076-3417/12/5/2392prestack stochastic inversionadaptive particle swarm optimizationMarkov Chain Monte Carlothe global optimal value |
| spellingShingle | Long Yin Sheng Zhang Kun Xiang Yongqiang Ma Yongzhen Ji Ke Chen Dongyu Zheng A New Stochastic Process of Prestack Inversion for Rock Property Estimation Applied Sciences prestack stochastic inversion adaptive particle swarm optimization Markov Chain Monte Carlo the global optimal value |
| title | A New Stochastic Process of Prestack Inversion for Rock Property Estimation |
| title_full | A New Stochastic Process of Prestack Inversion for Rock Property Estimation |
| title_fullStr | A New Stochastic Process of Prestack Inversion for Rock Property Estimation |
| title_full_unstemmed | A New Stochastic Process of Prestack Inversion for Rock Property Estimation |
| title_short | A New Stochastic Process of Prestack Inversion for Rock Property Estimation |
| title_sort | new stochastic process of prestack inversion for rock property estimation |
| topic | prestack stochastic inversion adaptive particle swarm optimization Markov Chain Monte Carlo the global optimal value |
| url | https://www.mdpi.com/2076-3417/12/5/2392 |
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