High-Dimensional Seismic Data Reconstruction Based on Linear Radon Transform–Constrained Tensor CANDECOM/PARAFAC Decomposition
Random noise and missing seismic traces are common in field seismic data, which seriously affects the subsequent seismic processing flow. The complete noise-free high-dimensional seismic dataset in the frequency–space (f-x) domain under the local linear assumption are regarded as a low-rank tensor,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/24/6275 |
| _version_ | 1797455465998712832 |
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| author | Zhiyuan Ouyang Liqi Zhang Huazhong Wang Kai Yang |
| author_facet | Zhiyuan Ouyang Liqi Zhang Huazhong Wang Kai Yang |
| author_sort | Zhiyuan Ouyang |
| collection | DOAJ |
| description | Random noise and missing seismic traces are common in field seismic data, which seriously affects the subsequent seismic processing flow. The complete noise-free high-dimensional seismic dataset in the frequency–space (f-x) domain under the local linear assumption are regarded as a low-rank tensor, and each high dimensional seismic dataset containing only one linear event is a rank-1 tensor. The tensor CANDECOM/PARAFAC decomposition (CPD) method estimates complete noise-free seismic signals by characterizing high-dimensional seismic signals as the sum of several rank-1 tensors. In order to improve the stability and effect of the tensor CPD algorithm, this paper proposes a linear Radon transform–constrained tensor CPD method (RCPD) by using the sparsity of factor matrix in the Radon domain after high-dimensional seismic signal tensor CPD and uses alternating direction multiplier method (ADMM) to solve the established optimization problem. This proposed method is an essential realization of the high-dimensional linear Radon transform, and the results of synthetic and field data reconstruction prove the effectiveness of the proposed method. |
| first_indexed | 2024-03-09T15:54:45Z |
| format | Article |
| id | doaj.art-b453e5c44476468189d7e9d25c80d777 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T15:54:45Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b453e5c44476468189d7e9d25c80d7772023-11-24T17:47:01ZengMDPI AGRemote Sensing2072-42922022-12-011424627510.3390/rs14246275High-Dimensional Seismic Data Reconstruction Based on Linear Radon Transform–Constrained Tensor CANDECOM/PARAFAC DecompositionZhiyuan Ouyang0Liqi Zhang1Huazhong Wang2Kai Yang3Wave Phenomena and Intelligent Inversion Imaging group (WPI), School of Ocean and Earth Science, Tongji University, Shanghai 200092, ChinaWave Phenomena and Intelligent Inversion Imaging group (WPI), School of Ocean and Earth Science, Tongji University, Shanghai 200092, ChinaWave Phenomena and Intelligent Inversion Imaging group (WPI), School of Ocean and Earth Science, Tongji University, Shanghai 200092, ChinaSchool of Ocean and Earth Science, Tongji University, Shanghai 200092, ChinaRandom noise and missing seismic traces are common in field seismic data, which seriously affects the subsequent seismic processing flow. The complete noise-free high-dimensional seismic dataset in the frequency–space (f-x) domain under the local linear assumption are regarded as a low-rank tensor, and each high dimensional seismic dataset containing only one linear event is a rank-1 tensor. The tensor CANDECOM/PARAFAC decomposition (CPD) method estimates complete noise-free seismic signals by characterizing high-dimensional seismic signals as the sum of several rank-1 tensors. In order to improve the stability and effect of the tensor CPD algorithm, this paper proposes a linear Radon transform–constrained tensor CPD method (RCPD) by using the sparsity of factor matrix in the Radon domain after high-dimensional seismic signal tensor CPD and uses alternating direction multiplier method (ADMM) to solve the established optimization problem. This proposed method is an essential realization of the high-dimensional linear Radon transform, and the results of synthetic and field data reconstruction prove the effectiveness of the proposed method.https://www.mdpi.com/2072-4292/14/24/6275seismic data reconstructionlocal plane wavetensor CP decompositionlinear Radon transform |
| spellingShingle | Zhiyuan Ouyang Liqi Zhang Huazhong Wang Kai Yang High-Dimensional Seismic Data Reconstruction Based on Linear Radon Transform–Constrained Tensor CANDECOM/PARAFAC Decomposition Remote Sensing seismic data reconstruction local plane wave tensor CP decomposition linear Radon transform |
| title | High-Dimensional Seismic Data Reconstruction Based on Linear Radon Transform–Constrained Tensor CANDECOM/PARAFAC Decomposition |
| title_full | High-Dimensional Seismic Data Reconstruction Based on Linear Radon Transform–Constrained Tensor CANDECOM/PARAFAC Decomposition |
| title_fullStr | High-Dimensional Seismic Data Reconstruction Based on Linear Radon Transform–Constrained Tensor CANDECOM/PARAFAC Decomposition |
| title_full_unstemmed | High-Dimensional Seismic Data Reconstruction Based on Linear Radon Transform–Constrained Tensor CANDECOM/PARAFAC Decomposition |
| title_short | High-Dimensional Seismic Data Reconstruction Based on Linear Radon Transform–Constrained Tensor CANDECOM/PARAFAC Decomposition |
| title_sort | high dimensional seismic data reconstruction based on linear radon transform constrained tensor candecom parafac decomposition |
| topic | seismic data reconstruction local plane wave tensor CP decomposition linear Radon transform |
| url | https://www.mdpi.com/2072-4292/14/24/6275 |
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