A Strong Noise Reduction Network for Seismic Records
Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. Thes...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10262 |
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| author | Tong Shen Xuan Jiang Wenzheng Rong Lei Xu Xianguo Tuo Guili Peng |
| author_facet | Tong Shen Xuan Jiang Wenzheng Rong Lei Xu Xianguo Tuo Guili Peng |
| author_sort | Tong Shen |
| collection | DOAJ |
| description | Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. These enhancements improve the network’s capability to distinguish between signal and noise in the time–frequency domain. We trained and tested our model on the STEAD dataset, which eliminated noise across various frequency bands, improved the signal-to-noise ratio (SNR) of seismic records, and reduced the waveform distortion significantly. Comparative analyses against U-Net, DeepDenoiser, and DnRDB models, using signals with SNRs ranging from −14 dB to 0 dB, demonstrated our model’s superior performance. At the same time, we demonstrated that the Inception Conv Block has a significant impact on the denoising ability of the network. Furthermore, validation using the “Di Ting” dataset and real noisy signals confirmed the model’s generalizability. These results show that the proposed model significantly outperforms the comparative methods in terms of the SNR, correlation coefficient (r), and root mean square error (RMSE), delivering higher-quality seismograms. The enhanced phase-picking accuracy underscores the potential of our approach to advance in geophysics applications. |
| format | Article |
| id | doaj-art-efcf1d2e378b43f39f5e3718dd8eec4a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-efcf1d2e378b43f39f5e3718dd8eec4a2025-08-20T02:26:45ZengMDPI AGApplied Sciences2076-34172024-11-0114221026210.3390/app142210262A Strong Noise Reduction Network for Seismic RecordsTong Shen0Xuan Jiang1Wenzheng Rong2Lei Xu3Xianguo Tuo4Guili Peng5School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, ChinaSchool of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaNoise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. These enhancements improve the network’s capability to distinguish between signal and noise in the time–frequency domain. We trained and tested our model on the STEAD dataset, which eliminated noise across various frequency bands, improved the signal-to-noise ratio (SNR) of seismic records, and reduced the waveform distortion significantly. Comparative analyses against U-Net, DeepDenoiser, and DnRDB models, using signals with SNRs ranging from −14 dB to 0 dB, demonstrated our model’s superior performance. At the same time, we demonstrated that the Inception Conv Block has a significant impact on the denoising ability of the network. Furthermore, validation using the “Di Ting” dataset and real noisy signals confirmed the model’s generalizability. These results show that the proposed model significantly outperforms the comparative methods in terms of the SNR, correlation coefficient (r), and root mean square error (RMSE), delivering higher-quality seismograms. The enhanced phase-picking accuracy underscores the potential of our approach to advance in geophysics applications.https://www.mdpi.com/2076-3417/14/22/10262denoiselow SNRdeep learningU-Netinceptionchannel attention |
| spellingShingle | Tong Shen Xuan Jiang Wenzheng Rong Lei Xu Xianguo Tuo Guili Peng A Strong Noise Reduction Network for Seismic Records Applied Sciences denoise low SNR deep learning U-Net inception channel attention |
| title | A Strong Noise Reduction Network for Seismic Records |
| title_full | A Strong Noise Reduction Network for Seismic Records |
| title_fullStr | A Strong Noise Reduction Network for Seismic Records |
| title_full_unstemmed | A Strong Noise Reduction Network for Seismic Records |
| title_short | A Strong Noise Reduction Network for Seismic Records |
| title_sort | strong noise reduction network for seismic records |
| topic | denoise low SNR deep learning U-Net inception channel attention |
| url | https://www.mdpi.com/2076-3417/14/22/10262 |
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