Radon Transform Constrained Multitrace Pre-Stack Deconvolution Algorithm
This article proposes a pre-stack deconvolution algorithm for the seismic common midpoint (CMP) gathers. Due to the low signal-to-noise ratio (SNR), poor lateral continuity of seismic CMP gathers, and residual time differences, conventional deconvolution algorithms struggle to enhance the resolution...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-10 |
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| container_title | IEEE transactions on geoscience and remote sensing |
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| creator | Shi, Wei Wang, Weihong Shi, Ying Chen, Siyuan Wang, Ning Cao, Bingyi |
| description | This article proposes a pre-stack deconvolution algorithm for the seismic common midpoint (CMP) gathers. Due to the low signal-to-noise ratio (SNR), poor lateral continuity of seismic CMP gathers, and residual time differences, conventional deconvolution algorithms struggle to enhance the resolution while maintaining the SNR. As a result, the data after deconvolution are overwhelmed by noise. In addition, the deconvolution methods in the Radon transform domain are limited by the tailing of focal points in the Radon domain. Therefore, this research employs the Radon transform as a sparse-promoting transform for deconvolution. By applying thresholds in the Radon domain, this algorithm suppresses noise and reduces the instability of deconvolution. Depending on the noise distribution, either the L_{2} norm or the L_{1} norm is flexibly chosen as the fitting term to enhance the algorithm's versatility. Leveraging the strong denoising capability of the Radon transform, this algorithm improves resolution on gathers with a low SNR while enhancing lateral continuity. Model and actual data tests indicate that the algorithm effectively enhances the resolution of gathers, thus facilitating pre-stack amplitude versus offset (AVO) analysis and pre-stack inversion. |
| doi_str_mv | 10.1109/TGRS.2024.3387756 |
| format | Article |
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Due to the low signal-to-noise ratio (SNR), poor lateral continuity of seismic CMP gathers, and residual time differences, conventional deconvolution algorithms struggle to enhance the resolution while maintaining the SNR. As a result, the data after deconvolution are overwhelmed by noise. In addition, the deconvolution methods in the Radon transform domain are limited by the tailing of focal points in the Radon domain. Therefore, this research employs the Radon transform as a sparse-promoting transform for deconvolution. By applying thresholds in the Radon domain, this algorithm suppresses noise and reduces the instability of deconvolution. Depending on the noise distribution, either the <inline-formula> <tex-math notation="LaTeX">L_{2} </tex-math></inline-formula> norm or the <inline-formula> <tex-math notation="LaTeX">L_{1} </tex-math></inline-formula> norm is flexibly chosen as the fitting term to enhance the algorithm's versatility. Leveraging the strong denoising capability of the Radon transform, this algorithm improves resolution on gathers with a low SNR while enhancing lateral continuity. Model and actual data tests indicate that the algorithm effectively enhances the resolution of gathers, thus facilitating pre-stack amplitude versus offset (AVO) analysis and pre-stack inversion.]]></description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2024.3387756</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Common midpoint (CMP) gathers ; Continuity (mathematics) ; Deconvolution ; high-resolution ; Mathematical models ; Noise reduction ; Radon ; radon transform ; Radon transformation ; Seismic stability ; Signal to noise ratio ; Trajectory ; Transforms ; Wavelet transforms</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2024, Vol.62, p.1-10</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c246t-ac69a319ad6c576549f7a318be79d8c882103b7205d67c35efabc4c60e81281c3</cites><orcidid>0000-0001-5609-7401 ; 0009-0004-2845-8221 ; 0000-0003-2332-2646 ; 0009-0009-8930-4492 ; 0000-0002-5931-5950</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10497098$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,4014,27914,27915,27916,54749</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10497098$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Shi, Wei</creatorcontrib><creatorcontrib>Wang, Weihong</creatorcontrib><creatorcontrib>Shi, Ying</creatorcontrib><creatorcontrib>Chen, Siyuan</creatorcontrib><creatorcontrib>Wang, Ning</creatorcontrib><creatorcontrib>Cao, Bingyi</creatorcontrib><title>Radon Transform Constrained Multitrace Pre-Stack Deconvolution Algorithm</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description><![CDATA[This article proposes a pre-stack deconvolution algorithm for the seismic common midpoint (CMP) gathers. Due to the low signal-to-noise ratio (SNR), poor lateral continuity of seismic CMP gathers, and residual time differences, conventional deconvolution algorithms struggle to enhance the resolution while maintaining the SNR. As a result, the data after deconvolution are overwhelmed by noise. In addition, the deconvolution methods in the Radon transform domain are limited by the tailing of focal points in the Radon domain. Therefore, this research employs the Radon transform as a sparse-promoting transform for deconvolution. By applying thresholds in the Radon domain, this algorithm suppresses noise and reduces the instability of deconvolution. Depending on the noise distribution, either the <inline-formula> <tex-math notation="LaTeX">L_{2} </tex-math></inline-formula> norm or the <inline-formula> <tex-math notation="LaTeX">L_{1} </tex-math></inline-formula> norm is flexibly chosen as the fitting term to enhance the algorithm's versatility. Leveraging the strong denoising capability of the Radon transform, this algorithm improves resolution on gathers with a low SNR while enhancing lateral continuity. Model and actual data tests indicate that the algorithm effectively enhances the resolution of gathers, thus facilitating pre-stack amplitude versus offset (AVO) analysis and pre-stack inversion.]]></description><subject>Algorithms</subject><subject>Common midpoint (CMP) gathers</subject><subject>Continuity (mathematics)</subject><subject>Deconvolution</subject><subject>high-resolution</subject><subject>Mathematical models</subject><subject>Noise reduction</subject><subject>Radon</subject><subject>radon transform</subject><subject>Radon transformation</subject><subject>Seismic stability</subject><subject>Signal to noise ratio</subject><subject>Trajectory</subject><subject>Transforms</subject><subject>Wavelet transforms</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1LAzEURYMoWKs_QHAx4HpqXr6zLNW2QkVp6zqkmYxOnU5qMiP4753SLlw9LtxzHxyEbgGPALB-WM-WqxHBhI0oVVJycYYGwLnKsWDsHA0waJETpcklukppizEwDnKA5ktbhCZbR9ukMsRdNglNaqOtGl9kL13dVn1wPnuLPl-11n1lj96F5ifUXVv14Lj-CLFqP3fX6KK0dfI3pztE79On9WSeL15nz5PxIneEiTa3TmhLQdtCOC4FZ7qUfVYbL3WhnFIEMN1IgnkhpKPcl3bjmBPYKyAKHB2i--PuPobvzqfWbEMXm_6loZgxQYEQ3bfg2HIxpBR9afax2tn4awCbgzBzEGYOwsxJWM_cHZnKe_-vz7TEWtE_KHRnIw</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>Shi, Wei</creator><creator>Wang, Weihong</creator><creator>Shi, Ying</creator><creator>Chen, Siyuan</creator><creator>Wang, Ning</creator><creator>Cao, Bingyi</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Due to the low signal-to-noise ratio (SNR), poor lateral continuity of seismic CMP gathers, and residual time differences, conventional deconvolution algorithms struggle to enhance the resolution while maintaining the SNR. As a result, the data after deconvolution are overwhelmed by noise. In addition, the deconvolution methods in the Radon transform domain are limited by the tailing of focal points in the Radon domain. Therefore, this research employs the Radon transform as a sparse-promoting transform for deconvolution. By applying thresholds in the Radon domain, this algorithm suppresses noise and reduces the instability of deconvolution. Depending on the noise distribution, either the <inline-formula> <tex-math notation="LaTeX">L_{2} </tex-math></inline-formula> norm or the <inline-formula> <tex-math notation="LaTeX">L_{1} </tex-math></inline-formula> norm is flexibly chosen as the fitting term to enhance the algorithm's versatility. Leveraging the strong denoising capability of the Radon transform, this algorithm improves resolution on gathers with a low SNR while enhancing lateral continuity. Model and actual data tests indicate that the algorithm effectively enhances the resolution of gathers, thus facilitating pre-stack amplitude versus offset (AVO) analysis and pre-stack inversion.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TGRS.2024.3387756</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-5609-7401</orcidid><orcidid>https://orcid.org/0009-0004-2845-8221</orcidid><orcidid>https://orcid.org/0000-0003-2332-2646</orcidid><orcidid>https://orcid.org/0009-0009-8930-4492</orcidid><orcidid>https://orcid.org/0000-0002-5931-5950</orcidid></addata></record> |
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| subjects | Algorithms Common midpoint (CMP) gathers Continuity (mathematics) Deconvolution high-resolution Mathematical models Noise reduction Radon radon transform Radon transformation Seismic stability Signal to noise ratio Trajectory Transforms Wavelet transforms |
| title | Radon Transform Constrained Multitrace Pre-Stack Deconvolution Algorithm |
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