Convolutional sparse coding network for sparse seismic time-frequency representation
Seismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data. Recently, sparse TF transforms, which leverage sparse coding (SC), have gained significant attention in t...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-06-01
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| Series: | Artificial Intelligence in Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666544124000455 |
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| _version_ | 1849433426569986048 |
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| author | Qiansheng Wei Zishuai Li Haonan Feng Yueying Jiang Yang Yang Zhiguo Wang |
| author_facet | Qiansheng Wei Zishuai Li Haonan Feng Yueying Jiang Yang Yang Zhiguo Wang |
| author_sort | Qiansheng Wei |
| collection | DOAJ |
| description | Seismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data. Recently, sparse TF transforms, which leverage sparse coding (SC), have gained significant attention in the geosciences due to their ability to achieve high TF resolution. However, the iterative approaches typically employed in sparse TF transforms are computationally intensive, making them impractical for real seismic data analysis. To address this issue, we propose an interpretable convolutional sparse coding (CSC) network to achieve high TF resolution. The proposed model is generated based on the traditional short-time Fourier transform (STFT) transform and a modified UNet, named ULISTANet. In this design, we replace the conventional convolutional layers of the UNet with learnable iterative shrinkage thresholding algorithm (LISTA) blocks, a specialized form of CSC. The LISTA block, which evolves from the traditional iterative shrinkage thresholding algorithm (ISTA), is optimized for extracting sparse features more effectively. Furthermore, we create a synthetic dataset featuring complex frequency-modulated signals to train ULISTANet. Finally, the proposed method's performance is subsequently validated using both synthetic and field data, demonstrating its potential for enhanced seismic data analysis. |
| format | Article |
| id | doaj-art-558d982f2afe4a14ad8a7445ea78bd9c |
| institution | Kabale University |
| issn | 2666-5441 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Geosciences |
| spelling | doaj-art-558d982f2afe4a14ad8a7445ea78bd9c2025-08-20T03:27:02ZengKeAi Communications Co. Ltd.Artificial Intelligence in Geosciences2666-54412025-06-016110010410.1016/j.aiig.2024.100104Convolutional sparse coding network for sparse seismic time-frequency representationQiansheng Wei0Zishuai Li1Haonan Feng2Yueying Jiang3Yang Yang4Zhiguo Wang5The Third Gas Production Plant of PetroChina Changqing Oilfield Branch, Wushenqi, Inner Mongolia, 017000, PR ChinaSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, PR ChinaThe Third Gas Production Plant of PetroChina Changqing Oilfield Branch, Wushenqi, Inner Mongolia, 017000, PR ChinaSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, PR ChinaSchool of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, PR ChinaSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, PR China; Corresponding author.Seismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data. Recently, sparse TF transforms, which leverage sparse coding (SC), have gained significant attention in the geosciences due to their ability to achieve high TF resolution. However, the iterative approaches typically employed in sparse TF transforms are computationally intensive, making them impractical for real seismic data analysis. To address this issue, we propose an interpretable convolutional sparse coding (CSC) network to achieve high TF resolution. The proposed model is generated based on the traditional short-time Fourier transform (STFT) transform and a modified UNet, named ULISTANet. In this design, we replace the conventional convolutional layers of the UNet with learnable iterative shrinkage thresholding algorithm (LISTA) blocks, a specialized form of CSC. The LISTA block, which evolves from the traditional iterative shrinkage thresholding algorithm (ISTA), is optimized for extracting sparse features more effectively. Furthermore, we create a synthetic dataset featuring complex frequency-modulated signals to train ULISTANet. Finally, the proposed method's performance is subsequently validated using both synthetic and field data, demonstrating its potential for enhanced seismic data analysis.http://www.sciencedirect.com/science/article/pii/S2666544124000455Time-frequency transformIteration shrinkage threshold algorithmDeep learningUNet |
| spellingShingle | Qiansheng Wei Zishuai Li Haonan Feng Yueying Jiang Yang Yang Zhiguo Wang Convolutional sparse coding network for sparse seismic time-frequency representation Artificial Intelligence in Geosciences Time-frequency transform Iteration shrinkage threshold algorithm Deep learning UNet |
| title | Convolutional sparse coding network for sparse seismic time-frequency representation |
| title_full | Convolutional sparse coding network for sparse seismic time-frequency representation |
| title_fullStr | Convolutional sparse coding network for sparse seismic time-frequency representation |
| title_full_unstemmed | Convolutional sparse coding network for sparse seismic time-frequency representation |
| title_short | Convolutional sparse coding network for sparse seismic time-frequency representation |
| title_sort | convolutional sparse coding network for sparse seismic time frequency representation |
| topic | Time-frequency transform Iteration shrinkage threshold algorithm Deep learning UNet |
| url | http://www.sciencedirect.com/science/article/pii/S2666544124000455 |
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