Academic Journal
Adaptive structure-guided impedance inversion using implicit sparse constraint.
| Τίτλος: | Adaptive structure-guided impedance inversion using implicit sparse constraint. |
|---|---|
| Συγγραφείς: | Li, Hao1,2,3, Cui, Yi-An1,2,3, Wang, Pu1,2,3 wpprospect1991@gmail.com, Li, Jing-Ye4, Liu, Jian-Xin1,2,3 |
| Πηγή: | Petroleum Science (KeAi Communications Co.). Jun2026, Vol. 23 Issue 6, p3228-3247. 20p. |
| Θεματικοί όροι: | Imaging systems in geophysics, Seismic traveltime inversion, Adaptive signal processing, Signal processing, Signal denoising, Tikhonov regularization |
| Περίληψη: | Structure-guided inversion has been increasingly adopted to enhance structural features and improve the interpretability of seismic impedance imaging. However, the limited bandwidth of seismic wavelets leads to low-resolution poststack records, which inevitably compromises the accuracy of structural estimates in discontinuous regions. Consequently, the success of this approach critically depends on the appropriate use of structural information as geometric constraints. To address this limitation, we propose an adaptive structure-guided approach that maintains large-scale structural continuity while preserving fine-scale variability. The average structural similarity (ASSIM) index and structural constraint sensitivity (SCS) index are introduced to adjust the structural penalty intensity across adjacent traces. The former metric determines the upper bound of penalty intensity for uniform structural constraints, while the latter one characterizes regions prone to distortion where a local reduction in lateral smoothing is required. In addition, the sparse constraint is separated from the objective function and implicitly applied via an iteratively refined reference model, which is generated by an adaptive edge-preserving filter (AEPF) followed by L2-TV denoising. This combined strategy promotes blocky impedance reconstruction and effectively preserves thin-layer details. Numerical and field examples demonstrate the superiority of the proposed approach. These improvements establish a more rational and geologically consistent framework for incorporating prior structural information, thereby enhancing the practicality and imaging performance of the structure-guided inversion in complex scenarios. [ABSTRACT FROM AUTHOR] |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive structure-guided impedance inversion using implicit sparse constraint. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Hao%22">Li, Hao</searchLink><relatesTo>1,2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Cui%2C+Yi-An%22">Cui, Yi-An</searchLink><relatesTo>1,2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Pu%22">Wang, Pu</searchLink><relatesTo>1,2,3</relatesTo><i> wpprospect1991@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Jing-Ye%22">Li, Jing-Ye</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Liu%2C+Jian-Xin%22">Liu, Jian-Xin</searchLink><relatesTo>1,2,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Petroleum+Science+%28KeAi+Communications+Co%2E%29%22">Petroleum Science (KeAi Communications Co.)</searchLink>. Jun2026, Vol. 23 Issue 6, p3228-3247. 20p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Imaging+systems+in+geophysics%22">Imaging systems in geophysics</searchLink><br /><searchLink fieldCode="DE" term="%22Seismic+traveltime+inversion%22">Seismic traveltime inversion</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+signal+processing%22">Adaptive signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+denoising%22">Signal denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Tikhonov+regularization%22">Tikhonov regularization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Structure-guided inversion has been increasingly adopted to enhance structural features and improve the interpretability of seismic impedance imaging. However, the limited bandwidth of seismic wavelets leads to low-resolution poststack records, which inevitably compromises the accuracy of structural estimates in discontinuous regions. Consequently, the success of this approach critically depends on the appropriate use of structural information as geometric constraints. To address this limitation, we propose an adaptive structure-guided approach that maintains large-scale structural continuity while preserving fine-scale variability. The average structural similarity (ASSIM) index and structural constraint sensitivity (SCS) index are introduced to adjust the structural penalty intensity across adjacent traces. The former metric determines the upper bound of penalty intensity for uniform structural constraints, while the latter one characterizes regions prone to distortion where a local reduction in lateral smoothing is required. In addition, the sparse constraint is separated from the objective function and implicitly applied via an iteratively refined reference model, which is generated by an adaptive edge-preserving filter (AEPF) followed by L2-TV denoising. This combined strategy promotes blocky impedance reconstruction and effectively preserves thin-layer details. Numerical and field examples demonstrate the superiority of the proposed approach. These improvements establish a more rational and geologically consistent framework for incorporating prior structural information, thereby enhancing the practicality and imaging performance of the structure-guided inversion in complex scenarios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Petroleum Science (KeAi Communications Co.) is the property of KeAi Communications Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.petsci.2026.02.014 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 3228 Subjects: – SubjectFull: Imaging systems in geophysics Type: general – SubjectFull: Seismic traveltime inversion Type: general – SubjectFull: Adaptive signal processing Type: general – SubjectFull: Signal processing Type: general – SubjectFull: Signal denoising Type: general – SubjectFull: Tikhonov regularization Type: general Titles: – TitleFull: Adaptive structure-guided impedance inversion using implicit sparse constraint. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Hao – PersonEntity: Name: NameFull: Cui, Yi-An – PersonEntity: Name: NameFull: Wang, Pu – PersonEntity: Name: NameFull: Li, Jing-Ye – PersonEntity: Name: NameFull: Liu, Jian-Xin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16725107 Numbering: – Type: volume Value: 23 – Type: issue Value: 6 Titles: – TitleFull: Petroleum Science (KeAi Communications Co.) Type: main |
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