3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering
Seismic coherence attributes are valuable for identifying structural features, but they often face challenges due to significant background noise and non-feature-related stratigraphic discontinuities. To address this, it is necessary to apply attribute conditioning to the coherence to enhance the vi...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/278 |
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| author | Taiyin Zhao Yuehua Yue Tian Chen Feng Qian |
| author_facet | Taiyin Zhao Yuehua Yue Tian Chen Feng Qian |
| author_sort | Taiyin Zhao |
| collection | DOAJ |
| description | Seismic coherence attributes are valuable for identifying structural features, but they often face challenges due to significant background noise and non-feature-related stratigraphic discontinuities. To address this, it is necessary to apply attribute conditioning to the coherence to enhance the visibility of these structures. The primary challenge of attribute conditioning lies in finding a concise structural representation that isolates only the true interpretive features while effectively removing noise and stratigraphic interference. In this study, we choose sheet-like structures as this concise structural representation, as faults are typically characterized by their thin and narrow profiles. Inspired by multiscale Hessian-based filtering (MHF) and its application on vascular structure detection, we propose a method called anisotropic multiscale Hessian-based sheet-enhancing filtering (AMHSF). This method is specifically designed to extract and magnify sheet-like structures from noisy coherence images, with a novel enhancement function distinct from those traditionally used in vascular enhancement. The effectiveness of our AMHSF is demonstrated through experiments on both synthetic and real datasets, showcasing its potential to improve the identification of structural features in coherence images. |
| format | Article |
| id | doaj-art-76a0e4fb84c940cf8ba03e35a07a262c |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-76a0e4fb84c940cf8ba03e35a07a262c2025-01-24T13:47:58ZengMDPI AGRemote Sensing2072-42922025-01-0117227810.3390/rs170202783D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing FilteringTaiyin Zhao0Yuehua Yue1Tian Chen2Feng Qian3School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Resources and Environment and Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Information and Communication Engineering and Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSeismic coherence attributes are valuable for identifying structural features, but they often face challenges due to significant background noise and non-feature-related stratigraphic discontinuities. To address this, it is necessary to apply attribute conditioning to the coherence to enhance the visibility of these structures. The primary challenge of attribute conditioning lies in finding a concise structural representation that isolates only the true interpretive features while effectively removing noise and stratigraphic interference. In this study, we choose sheet-like structures as this concise structural representation, as faults are typically characterized by their thin and narrow profiles. Inspired by multiscale Hessian-based filtering (MHF) and its application on vascular structure detection, we propose a method called anisotropic multiscale Hessian-based sheet-enhancing filtering (AMHSF). This method is specifically designed to extract and magnify sheet-like structures from noisy coherence images, with a novel enhancement function distinct from those traditionally used in vascular enhancement. The effectiveness of our AMHSF is demonstrated through experiments on both synthetic and real datasets, showcasing its potential to improve the identification of structural features in coherence images.https://www.mdpi.com/2072-4292/17/2/278coherence attributesinterpretation featuresattribute conditioningsheet-like structuresanisotropic multiscale Hessian-based sheet-enhancing filtering (AMHSF) |
| spellingShingle | Taiyin Zhao Yuehua Yue Tian Chen Feng Qian 3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering Remote Sensing coherence attributes interpretation features attribute conditioning sheet-like structures anisotropic multiscale Hessian-based sheet-enhancing filtering (AMHSF) |
| title | 3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering |
| title_full | 3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering |
| title_fullStr | 3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering |
| title_full_unstemmed | 3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering |
| title_short | 3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering |
| title_sort | 3d seismic attribute conditioning using multiscale sheet enhancing filtering |
| topic | coherence attributes interpretation features attribute conditioning sheet-like structures anisotropic multiscale Hessian-based sheet-enhancing filtering (AMHSF) |
| url | https://www.mdpi.com/2072-4292/17/2/278 |
| work_keys_str_mv | AT taiyinzhao 3dseismicattributeconditioningusingmultiscalesheetenhancingfiltering AT yuehuayue 3dseismicattributeconditioningusingmultiscalesheetenhancingfiltering AT tianchen 3dseismicattributeconditioningusingmultiscalesheetenhancingfiltering AT fengqian 3dseismicattributeconditioningusingmultiscalesheetenhancingfiltering |