Academic Journal
A cepstrum-guided 2D-VMD–TOPSIS framework for seismic gas-bearing reservoir detection.
| Τίτλος: | A cepstrum-guided 2D-VMD–TOPSIS framework for seismic gas-bearing reservoir detection. |
|---|---|
| Συγγραφείς: | Pu, Yu-Jia1,2 (AUTHOR), Xue, Ya-Juan1,2 (AUTHOR) xueyj0869@163.com, Liu, Zhe-Ge1,2 (AUTHOR) |
| Πηγή: | Applied Geophysics: Bulletin of Chinese Geophysical Society. Aug2026, p1-16. 16p. |
| Θεματικοί όροι: | *TOPSIS method, *Adaptive signal processing, *Signal separation, *Outlier detection, *Imaging systems in seismology |
| Περίληψη: | Gas-bearing reservoir detection from seismic data is essential for reducing exploration risk, but it remains challenging when target responses are obscured by background reflections and noise. This study proposes a seismic anomaly detection workflow that integrates adaptive signal decomposition, multi-feature component selection, and fluid-sensitive imaging. The seismic profile is first decomposed into multiple intrinsic mode functions (IMFs) with different spatial and frequency characteristics. The IMF most relevant to gas-bearing responses is then identified through an entropy-weighted TOPSIS ranking strategy that jointly evaluates five complementary indicators: energy, spectral centroid, lateral continuity, permutation entropy, and a cepstral difference factor. Finally, the selected IMF is processed through a fusion of a local cepstral attribute and Teager-Kaiser energy to generate an anomaly image that enhances reservoir-related responses while suppressing background interference. The proposed workflow was tested using a noisy Marmousi2 model, a thick-reservoir 2D post-stack migrated seismic section acquired from a gas field in western Sichuan, China, and a benchmark thin-reservoir profile from previously reported well-constrained data. The model test shows that the method can produce a more concentrated anomaly around the target zone than conventional attribute-based methods. In the thick-reservoir field example, the method improves the local contrast of the well-adjacent anomaly relative to the surrounding stratigraphic background. In the benchmark thin-reservoir case, the detected anomaly shows improved lateral continuity within the well-constrained target interval. These results indicate that the proposed workflow can provide more focused and interpretable gas-bearing anomaly images under different reservoir thickness conditions. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Academic Search Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: asx DbLabel: Academic Search Index An: 196505871 RelevancyScore: 1452 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1452.42541503906 |
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| Items | – Name: Title Label: Title Group: Ti Data: A cepstrum-guided 2D-VMD–TOPSIS framework for seismic gas-bearing reservoir detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pu%2C+Yu-Jia%22">Pu, Yu-Jia</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xue%2C+Ya-Juan%22">Xue, Ya-Juan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> xueyj0869@163.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zhe-Ge%22">Liu, Zhe-Ge</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Geophysics%3A+Bulletin+of+Chinese+Geophysical+Society%22">Applied Geophysics: Bulletin of Chinese Geophysical Society</searchLink>. Aug2026, p1-16. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22TOPSIS+method%22">TOPSIS method</searchLink><br />*<searchLink fieldCode="DE" term="%22Adaptive+signal+processing%22">Adaptive signal processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Signal+separation%22">Signal separation</searchLink><br />*<searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br />*<searchLink fieldCode="DE" term="%22Imaging+systems+in+seismology%22">Imaging systems in seismology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Gas-bearing reservoir detection from seismic data is essential for reducing exploration risk, but it remains challenging when target responses are obscured by background reflections and noise. This study proposes a seismic anomaly detection workflow that integrates adaptive signal decomposition, multi-feature component selection, and fluid-sensitive imaging. The seismic profile is first decomposed into multiple intrinsic mode functions (IMFs) with different spatial and frequency characteristics. The IMF most relevant to gas-bearing responses is then identified through an entropy-weighted TOPSIS ranking strategy that jointly evaluates five complementary indicators: energy, spectral centroid, lateral continuity, permutation entropy, and a cepstral difference factor. Finally, the selected IMF is processed through a fusion of a local cepstral attribute and Teager-Kaiser energy to generate an anomaly image that enhances reservoir-related responses while suppressing background interference. The proposed workflow was tested using a noisy Marmousi2 model, a thick-reservoir 2D post-stack migrated seismic section acquired from a gas field in western Sichuan, China, and a benchmark thin-reservoir profile from previously reported well-constrained data. The model test shows that the method can produce a more concentrated anomaly around the target zone than conventional attribute-based methods. In the thick-reservoir field example, the method improves the local contrast of the well-adjacent anomaly relative to the surrounding stratigraphic background. In the benchmark thin-reservoir case, the detected anomaly shows improved lateral continuity within the well-constrained target interval. These results indicate that the proposed workflow can provide more focused and interpretable gas-bearing anomaly images under different reservoir thickness conditions. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11770-026-1553-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: TOPSIS method Type: general – SubjectFull: Adaptive signal processing Type: general – SubjectFull: Signal separation Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Imaging systems in seismology Type: general Titles: – TitleFull: A cepstrum-guided 2D-VMD–TOPSIS framework for seismic gas-bearing reservoir detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pu, Yu-Jia – PersonEntity: Name: NameFull: Xue, Ya-Juan – PersonEntity: Name: NameFull: Liu, Zhe-Ge IsPartOfRelationships: – BibEntity: Dates: – D: 28 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16727975 Titles: – TitleFull: Applied Geophysics: Bulletin of Chinese Geophysical Society Type: main |
| ResultId | 1 |