Research on Seismic Signal Denoising Model Based on DnCNN Network
Addressing the noise in seismic signals, a prevalent challenge within seismic signal processing, has been the focus of extensive research. Conventional algorithms for seismic signal denoising often fall short due to their reliance on manually determined feature functions and threshold parameters. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/4/2083 |
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| author | Li Duan Jianxian Cai Li Wang Yan Shi |
| author_facet | Li Duan Jianxian Cai Li Wang Yan Shi |
| author_sort | Li Duan |
| collection | DOAJ |
| description | Addressing the noise in seismic signals, a prevalent challenge within seismic signal processing, has been the focus of extensive research. Conventional algorithms for seismic signal denoising often fall short due to their reliance on manually determined feature functions and threshold parameters. These limitations hinder effective noise removal, resulting in suboptimal signal-to-noise ratios (SNRs) and post-denoising waveform distortion. To address these shortcomings, this study introduces a novel denoising approach leveraging a DnCNN network. The DnCNN framework, which integrates batch normalization with residual learning, is adept at swiftly identifying and eliminating noise from seismic signals through its residual learning capabilities. To assess the efficacy of this DnCNN-based model, it was rigorously tested against a curated test set and benchmarked against other denoising techniques, including wavelet thresholding, empirical mode decomposition, and convolutional auto-encoders. The findings demonstrate that the DnCNN model not only significantly enhances the SNR and correlation coefficient of the processed seismic signals but also achieves superior noise reduction performance. |
| format | Article |
| id | doaj-art-706f4effcf73485190b0cffc3b62b5a3 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-706f4effcf73485190b0cffc3b62b5a32025-08-20T03:12:16ZengMDPI AGApplied Sciences2076-34172025-02-01154208310.3390/app15042083Research on Seismic Signal Denoising Model Based on DnCNN NetworkLi Duan0Jianxian Cai1Li Wang2Yan Shi3College of Electronic Science and Control Engineering, Institute of Disaster Prevention, Langfang 065201, ChinaCollege of Electronic Science and Control Engineering, Institute of Disaster Prevention, Langfang 065201, ChinaCollege of Electronic Science and Control Engineering, Institute of Disaster Prevention, Langfang 065201, ChinaCollege of Electronic Science and Control Engineering, Institute of Disaster Prevention, Langfang 065201, ChinaAddressing the noise in seismic signals, a prevalent challenge within seismic signal processing, has been the focus of extensive research. Conventional algorithms for seismic signal denoising often fall short due to their reliance on manually determined feature functions and threshold parameters. These limitations hinder effective noise removal, resulting in suboptimal signal-to-noise ratios (SNRs) and post-denoising waveform distortion. To address these shortcomings, this study introduces a novel denoising approach leveraging a DnCNN network. The DnCNN framework, which integrates batch normalization with residual learning, is adept at swiftly identifying and eliminating noise from seismic signals through its residual learning capabilities. To assess the efficacy of this DnCNN-based model, it was rigorously tested against a curated test set and benchmarked against other denoising techniques, including wavelet thresholding, empirical mode decomposition, and convolutional auto-encoders. The findings demonstrate that the DnCNN model not only significantly enhances the SNR and correlation coefficient of the processed seismic signals but also achieves superior noise reduction performance.https://www.mdpi.com/2076-3417/15/4/2083CNN (convolutional neural network)residual dense modulesignal noise reductionSNR (signal-to-noise ratio) |
| spellingShingle | Li Duan Jianxian Cai Li Wang Yan Shi Research on Seismic Signal Denoising Model Based on DnCNN Network Applied Sciences CNN (convolutional neural network) residual dense module signal noise reduction SNR (signal-to-noise ratio) |
| title | Research on Seismic Signal Denoising Model Based on DnCNN Network |
| title_full | Research on Seismic Signal Denoising Model Based on DnCNN Network |
| title_fullStr | Research on Seismic Signal Denoising Model Based on DnCNN Network |
| title_full_unstemmed | Research on Seismic Signal Denoising Model Based on DnCNN Network |
| title_short | Research on Seismic Signal Denoising Model Based on DnCNN Network |
| title_sort | research on seismic signal denoising model based on dncnn network |
| topic | CNN (convolutional neural network) residual dense module signal noise reduction SNR (signal-to-noise ratio) |
| url | https://www.mdpi.com/2076-3417/15/4/2083 |
| work_keys_str_mv | AT liduan researchonseismicsignaldenoisingmodelbasedondncnnnetwork AT jianxiancai researchonseismicsignaldenoisingmodelbasedondncnnnetwork AT liwang researchonseismicsignaldenoisingmodelbasedondncnnnetwork AT yanshi researchonseismicsignaldenoisingmodelbasedondncnnnetwork |