Adaptive Feedback Convolutional-Neural-Network-Based High-Resolution Reflection-Waveform Inversion
文献类型：期刊论文
| 作者 | Wu, Yulang1,3; McMechan, George A.4; Wang, Yanfei1,2,3 |
| 刊名 | JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 127期号:6页码:23 |
| 关键词 | machine learning deep learning convolutional neural network reflection full-waveform inversion k-means clustering |
| ISSN号 | 2169-9313 |
| DOI | 10.1029/2022JB024138 |
| 英文摘要 | Full-waveform inversion (FWI) applies non-linear optimization to estimate the velocity model by fitting the observed seismic data. With a smooth starting velocity model, FWI mainly inverts for the shallower background velocity model by fitting the observed direct, diving and refracted data, and updates the interfaces by fitting the observed reflected data. As the deeper parts of background velocity model cannot be effectively updated by fitting the reflected data in FWI, the deeper interfaces are less accurate than the shallower interfaces. To update the deeper background velocity model, many reflection-waveform inversion (RWI) algorithms were proposed to separate the tomographic and migration components from the reflection-related gradient. We propose a convolutional-neural-network-based reflection-waveform inversion (CNN-RWI) to repeatedly apply the iteratively updated convolutional neural network (CNN) to predict the true velocity model from the smooth starting velocity model (the tomographic components), and the high-resolution migration image (the migration components). The CNN is iteratively updated by the more representative training data set, which is obtained from the latest CNN-predicted velocity model by the proposed spatially constrained divisive hierarchical k-means parcellation method. The more representative the training velocity models are, the more accurate the CNN-predicted velocity model becomes. Synthetic examples using different portions of the Marmousi2 P-wave velocity model show that CNN-RWI inverts for both the shallower and deeper parts of velocity models more accurately than the conjugate-gradient FWI (CG-FWI). Both the CNN-RWI and the CG-FWI are sensitive to the accuracy of the starting velocity model and the complexity of the unknown true velocity model. |
| WOS关键词 | REVERSE-TIME MIGRATION ; NONLINEAR INVERSION ; FREQUENCY-DOMAIN ; REGULARIZATION ; COMPONENTS ; MODEL |
| 资助项目 | National Natural Science Foundation of China (NSFC)[12171455] ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS)[ZDBS-LY-DQC003] ; Key Research Program of the Institute of Geology & Geophysics, CAS[IGGCAS-2019031] ; Key Research Program of the Institute of Geology & Geophysics, CAS[SZJJ-201901] ; University of Texas at Dallas Geophysical Consortium ; Department of Geosciences at the University of Texas at Dallas |
| WOS研究方向 | Geochemistry & Geophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:000814555200001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 资助机构 | National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Original Innovation Research Program of the Chinese Academy of Sciences (CAS) ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; University of Texas at Dallas Geophysical Consortium ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas ; Department of Geosciences at the University of Texas at Dallas |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/105888]  |
| 专题 | 地质与地球物理研究所_中国科学院油气资源研究重点实验室 |
| 通讯作者 | Wang, Yanfei |
| 作者单位 | 1.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing, Peoples R China 4.Univ Texas Dallas, Richardson, TX 75083 USA |
| 推荐引用方式 GB/T 7714 | Wu, Yulang,McMechan, George A.,Wang, Yanfei. Adaptive Feedback Convolutional-Neural-Network-Based High-Resolution Reflection-Waveform Inversion[J]. JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,2022,127(6):23. |
| APA | Wu, Yulang,McMechan, George A.,&Wang, Yanfei.(2022).Adaptive Feedback Convolutional-Neural-Network-Based High-Resolution Reflection-Waveform Inversion.JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH,127(6),23. |
| MLA | Wu, Yulang,et al."Adaptive Feedback Convolutional-Neural-Network-Based High-Resolution Reflection-Waveform Inversion".JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 127.6(2022):23. |
入库方式： OAI收割
来源：地质与地球物理研究所
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