Seismic Random Noise Suppression Model Based on Downsampling and Superresolution
文献类型：期刊论文
| 作者 | Fang, Ziyi1; Lin, Hongbo1; Sun, Fuyao1; Song, Xue1; Zhang, Chao3 ; Wang, Bo2
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| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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| 出版日期 | 2023 |
| 卷号 | 61页码:14 |
| 关键词 | Noise reduction Superresolution Image restoration Generators Data models Complexity theory Signal resolution Seismic exploration seismic random noise seismic signal denoising superresolution (SR) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2023.3279866 |
| 通讯作者 | Lin, Hongbo(hblin@jlu.edu.cn) |
| 英文摘要 | Seismic random noise suppression presents two main challenges: achieving thorough noise suppression while simultaneously ensuring complete restoration of effective signal content. However, due to the complexity of random noise, the existing denoising methods often only achieve an awkward balance between removing random noise and restoring effective signals. In this article, we propose a novel random noise suppression model based on downsampling and superresolution (SR). By decoupling the denoising and signal restoration processes, our method reduces the difficulty of addressing these two challenges and mitigates the likelihood of suboptimal results. On the one hand, the high fitting-capacity downsampling network uses nonlinear transformations to separate random noise and effective signals while purifying the high-order features of effective signals. On the other hand, the SR network expands the low-dimensional seismic signal content containing the high-order features of the signal to restore the signal structure. Moreover, we propose a new adversarial loss by introducing the gradient between the generated data and the real data, which enhances the perceptual quality of the SR results and recovers the content of effective signals better. Because both subnetworks are not affected by signal/noise features during processing, the model exhibits strong fitting and generalization abilities. The experimental evaluation on four different types of seismic data demonstrates the superiority of our method in suppressing random noise and restoring the content of effective signals. |
| WOS关键词 | DECOMPOSITION ; ATTENUATION ; DICTIONARY ; DENOISER ; CNN |
| 资助项目 | National Natural Science Foundation of China[41774117] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001005737500032 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53633]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Lin, Hongbo |
| 作者单位 | 1.Jilin Univ, Coll Commun Engn, Dept Informat, Changchun 130012, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.People ai Inc, Res & Dev Ctr, Beijing 100036, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Ziyi,Lin, Hongbo,Sun, Fuyao,et al. Seismic Random Noise Suppression Model Based on Downsampling and Superresolution[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2023,61:14. |
| APA | Fang, Ziyi,Lin, Hongbo,Sun, Fuyao,Song, Xue,Zhang, Chao,&Wang, Bo.(2023).Seismic Random Noise Suppression Model Based on Downsampling and Superresolution.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61,14. |
| MLA | Fang, Ziyi,et al."Seismic Random Noise Suppression Model Based on Downsampling and Superresolution".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023):14. |
入库方式： OAI收割
来源：自动化研究所
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