Resolution-enhanced BM3D for post-stack weak signal recovery
Improving the signal-to-noise ratio (SNR) and enhancing resolution are essential for accurately recovering weak signals in seismic signal processing. Block-Matching and 3D filtering (BM3D) is a widely used denoising algorithm in data processing that employs two key stages—hard thresholding and Wiene...
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Published in | Journal of geophysics and engineering Vol. 22; no. 4; pp. 1115 - 1131 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
01.08.2025
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Online Access | Get full text |
ISSN | 1742-2132 1742-2140 |
DOI | 10.1093/jge/gxaf076 |
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Abstract | Improving the signal-to-noise ratio (SNR) and enhancing resolution are essential for accurately recovering weak signals in seismic signal processing. Block-Matching and 3D filtering (BM3D) is a widely used denoising algorithm in data processing that employs two key stages—hard thresholding and Wiener filtering—to achieve multidimensional noise attenuation and improve SNR. However, the traditional BM3D algorithm does not fully consider the complex stratum effects present in seismic data, which limits its effectiveness in weak signal recovery. To address this limitation, we propose a resolution-enhanced BM3D (RE-BM3D), which refines both key stages to improve the recovery of weak signals. In the hard thresholding stage, we introduce an inverse filtering operator with Tikhonov regularization to better recover weak signals attenuated by stratum filtering. Additionally, a dynamic threshold is applied based on the variance of spectral coefficients to further enhance the accuracy of noise attenuation. In the Wiener filtering stage, regularized inverse filtering is employed to enhance vertical resolution, while the shrinkage coefficient is calculated based on the variance of spectral coefficients to achieve optimal SNR improvement. Experimental results show that RE-BM3D successfully enhances vertical resolution and recovers weak seismic signals, effectively overcoming the limitations of traditional BM3D in seismic data processing. |
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AbstractList | Improving the signal-to-noise ratio (SNR) and enhancing resolution are essential for accurately recovering weak signals in seismic signal processing. Block-Matching and 3D filtering (BM3D) is a widely used denoising algorithm in data processing that employs two key stages—hard thresholding and Wiener filtering—to achieve multidimensional noise attenuation and improve SNR. However, the traditional BM3D algorithm does not fully consider the complex stratum effects present in seismic data, which limits its effectiveness in weak signal recovery. To address this limitation, we propose a resolution-enhanced BM3D (RE-BM3D), which refines both key stages to improve the recovery of weak signals. In the hard thresholding stage, we introduce an inverse filtering operator with Tikhonov regularization to better recover weak signals attenuated by stratum filtering. Additionally, a dynamic threshold is applied based on the variance of spectral coefficients to further enhance the accuracy of noise attenuation. In the Wiener filtering stage, regularized inverse filtering is employed to enhance vertical resolution, while the shrinkage coefficient is calculated based on the variance of spectral coefficients to achieve optimal SNR improvement. Experimental results show that RE-BM3D successfully enhances vertical resolution and recovers weak seismic signals, effectively overcoming the limitations of traditional BM3D in seismic data processing. |
Author | Chen, Gui Liu, Yang Zhang, Hao-Ran Zhang, Mi Jiao, Kai |
Author_xml | – sequence: 1 givenname: Mi surname: Zhang fullname: Zhang, Mi – sequence: 2 givenname: Kai surname: Jiao fullname: Jiao, Kai – sequence: 3 givenname: Yang orcidid: 0000-0001-5193-9825 surname: Liu fullname: Liu, Yang – sequence: 4 givenname: Gui surname: Chen fullname: Chen, Gui – sequence: 5 givenname: Hao-Ran surname: Zhang fullname: Zhang, Hao-Ran |
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Cites_doi | 10.1190/geo2014-0396.1 10.3390/rs14205240 10.1007/s10712-022-09756-7 10.1093/jge/gxad060 10.1088/1742-2140/aab274 10.1093/gji/ggaa329 10.1190/geo2023-0767.1 10.1190/geo2023-0320.1 10.1190/geo2024-0502.1 10.1016/j.infrared.2014.11.006 10.1016/j.jappgeo.2019.04.018 10.1109/TGRS.2022.3141113 10.1190/1.3043446 10.1109/ICIP.2015.7351377 10.1109/TGRS.2024.3398590 10.1190/geo2017-0288.1 10.1137/05064182X 10.1109/TGRS.2021.3057857 10.1190/geo2024-0148.1 10.1109/WCSP52459.2021.9613588 10.1109/TGRS.2017.2698342 10.1190/geo2017-0310.1 10.1190/1.1821933 10.1109/MI-STA61267.2024.10599673 10.1007/s11770-017-0607-z 10.1016/j.jappgeo.2024.105416 10.1177/15485129211036044 10.1190/1.1894168 10.1117/12.643267 10.1016/j.jappgeo.2023.105236 10.1109/TIP.2020.3014721 10.1190/geo2022-0198.1 10.1190/1.3552706 10.1190/geo2019-0063.1 10.1109/TGRS.2021.3053399 10.1093/gji/ggx422 10.1109/TGRS.2024.3357729 10.1109/TGRS.2023.3300578 10.1109/TGRS.2023.3234617 |
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