Effects of seismic filters on pretrained CNN models for fault detection: application to the Delft 3D seismic volume, the Netherlands.
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| Title: | Effects of seismic filters on pretrained CNN models for fault detection: application to the Delft 3D seismic volume, the Netherlands. |
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| Authors: | ALEMDAR, Süleyman1,2 suleymanalemdar@gmail.com, PEKŞEN, Ertan1 |
| Source: | Turkish Journal of Earth Sciences. 2026, Vol. 35 Issue 2, p184-196. 14p. |
| Subjects: | Signal denoising, Seismic reflection method, Image processing, Machine learning, Fault diagnosis |
| Geographic Terms: | Delft (Netherlands), Netherlands |
| Abstract: | Accurate mapping of faults from 3D seismic volumes is critical for identifying structural traps, assessing reservoir compartmentalization, and guiding drilling decisions. This study evaluates how common seismic conditioning filters affect both the visual quality of a production Delft 3D poststack time-migrated volume and the outputs of two pretrained 3D CNN fault detectors: UNet3D and FaultNet. Seven filters were applied independently—AJAX, DeSmile, SimpleDenoise, Dip-Steered Median Filter (DSMF), Edge-Preserving Smoother (EPS), Fault Enhancement, and Ridge Enhancement—and results were inspected across inline, crossline, time-slice, and full-cube perspectives. The analysis is image-driven, supported by high-resolution comparison figures and view-specific interpretations. Filters that suppress incoherent noise while preserving dip-aligned continuity and edge gradients, particularly DSMF and EPS, produce the most interpretable inputs for UNet3D and yield thin, continuous fault traces with low internal clutter. SimpleDenoise provides conservative conditioning that improves signal-to-noise without creating artificial discontinuities, while DeSmile stabilizes outputs in areas affected by migration-smile curvature. In contrast, contrast-enhancing operators (AJAX and Fault/Ridge Enhancement) increase the detectability of weak lineaments but also broaden the apparent fault response and emphasize non-fault edges that require interpreter screening. Across all conditioning strategies, FaultNet produces a larger, higher-recall set of candidates that is useful for lead generation but requires curation, whereas UNet3D tends to return thinner, more connected masks when background noise is reduced, and edges are preserved. These findings provide practical guidance for selecting conditioning filters that improve the usability of pretrained CNN fault predictors on production seismic data. [ABSTRACT FROM AUTHOR] |
| Copyright of Turkish Journal of Earth Sciences is the property of Scientific and Technical Research Council of Turkey 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. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Effects of seismic filters on pretrained CNN models for fault detection: application to the Delft 3D seismic volume, the Netherlands. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22ALEMDAR%2C+Süleyman%22">ALEMDAR, Süleyman</searchLink><relatesTo>1,2</relatesTo><i> suleymanalemdar@gmail.com</i><br /><searchLink fieldCode="AR" term="%22PEKŞEN%2C+Ertan%22">PEKŞEN, Ertan</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Turkish+Journal+of+Earth+Sciences%22">Turkish Journal of Earth Sciences</searchLink>. 2026, Vol. 35 Issue 2, p184-196. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Signal+denoising%22">Signal denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Seismic+reflection+method%22">Seismic reflection method</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Delft+%28Netherlands%29%22">Delft (Netherlands)</searchLink><br /><searchLink fieldCode="DE" term="%22Netherlands%22">Netherlands</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate mapping of faults from 3D seismic volumes is critical for identifying structural traps, assessing reservoir compartmentalization, and guiding drilling decisions. This study evaluates how common seismic conditioning filters affect both the visual quality of a production Delft 3D poststack time-migrated volume and the outputs of two pretrained 3D CNN fault detectors: UNet3D and FaultNet. Seven filters were applied independently—AJAX, DeSmile, SimpleDenoise, Dip-Steered Median Filter (DSMF), Edge-Preserving Smoother (EPS), Fault Enhancement, and Ridge Enhancement—and results were inspected across inline, crossline, time-slice, and full-cube perspectives. The analysis is image-driven, supported by high-resolution comparison figures and view-specific interpretations. Filters that suppress incoherent noise while preserving dip-aligned continuity and edge gradients, particularly DSMF and EPS, produce the most interpretable inputs for UNet3D and yield thin, continuous fault traces with low internal clutter. SimpleDenoise provides conservative conditioning that improves signal-to-noise without creating artificial discontinuities, while DeSmile stabilizes outputs in areas affected by migration-smile curvature. In contrast, contrast-enhancing operators (AJAX and Fault/Ridge Enhancement) increase the detectability of weak lineaments but also broaden the apparent fault response and emphasize non-fault edges that require interpreter screening. Across all conditioning strategies, FaultNet produces a larger, higher-recall set of candidates that is useful for lead generation but requires curation, whereas UNet3D tends to return thinner, more connected masks when background noise is reduced, and edges are preserved. These findings provide practical guidance for selecting conditioning filters that improve the usability of pretrained CNN fault predictors on production seismic data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Turkish Journal of Earth Sciences is the property of Scientific and Technical Research Council of Turkey 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: 10.55730/1300-0985.2013 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 184 Subjects: – SubjectFull: Signal denoising Type: general – SubjectFull: Seismic reflection method Type: general – SubjectFull: Image processing Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Fault diagnosis Type: general – SubjectFull: Delft (Netherlands) Type: general – SubjectFull: Netherlands Type: general Titles: – TitleFull: Effects of seismic filters on pretrained CNN models for fault detection: application to the Delft 3D seismic volume, the Netherlands. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: ALEMDAR, Süleyman – PersonEntity: Name: NameFull: PEKŞEN, Ertan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13000985 Numbering: – Type: volume Value: 35 – Type: issue Value: 2 Titles: – TitleFull: Turkish Journal of Earth Sciences Type: main |
| ResultId | 1 |
