Seismic channel edge detection using 3D shearlets—a study on synthetic and real channelised 3D seismic data.
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| Title: | Seismic channel edge detection using 3D shearlets—a study on synthetic and real channelised 3D seismic data. |
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| Authors: | Karbalaali, Haleh1, Javaherian, Abdolrahim1,2 javaherian@aut.ac.ir, Dahlke, Stephan3, Reisenhofer, Rafael4, Torabi, Siyavash5 |
| Source: | Geophysical Prospecting. Sep2018, Vol. 66 Issue 7, p1272-1289. 18p. 2 Diagrams, 3 Charts, 14 Graphs. |
| Subjects: | Seismic reflection method, Edge detection (Image processing), Feature extraction, Reservoirs, Random noise theory, Algorithms |
| Abstract: | ABSTRACT: Automatic feature detection from seismic data is a demanding task in today's interpretation workstations. Channels are among important stratigraphic features in seismic data both due to their reservoir capability or drilling hazard potential. Shearlet transform as a multi‐scale and multi‐directional transformation is capable of detecting anisotropic singularities in two and higher dimensional data. Channels occur as edges in seismic data, which can be detected based on maximizing the shearlet coefficients through all sub‐volumes at the finest scale of decomposition. The detected edges may require further refinement through the application of a thinning methodology. In this study, a three‐dimensional, pyramid‐adapted, compactly supported shearlet transform was applied to synthetic and real channelised, three‐dimensional post‐stack seismic data in order to decompose the data into different scales and directions for the purpose of channel boundary detection. In order to be able to compare the edge detection results based on three‐dimensional shearlet transform with some famous gradient‐based edge detectors, such as Sobel and Canny, a thresholding scheme is necessary. In both synthetic and real data examples, the three‐dimensional shearlet edge detection algorithm outperformed Sobel and Canny operators even in the presence of Gaussian random noise. [ABSTRACT FROM AUTHOR] |
| Copyright of Geophysical Prospecting is the property of Wiley-Blackwell 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 |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Seismic channel edge detection using 3D shearlets—a study on synthetic and real channelised 3D seismic data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Karbalaali%2C+Haleh%22">Karbalaali, Haleh</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Javaherian%2C+Abdolrahim%22">Javaherian, Abdolrahim</searchLink><relatesTo>1,2</relatesTo><i> javaherian@aut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Dahlke%2C+Stephan%22">Dahlke, Stephan</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Reisenhofer%2C+Rafael%22">Reisenhofer, Rafael</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Torabi%2C+Siyavash%22">Torabi, Siyavash</searchLink><relatesTo>5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Geophysical+Prospecting%22">Geophysical Prospecting</searchLink>. Sep2018, Vol. 66 Issue 7, p1272-1289. 18p. 2 Diagrams, 3 Charts, 14 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Seismic+reflection+method%22">Seismic reflection method</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+detection+%28Image+processing%29%22">Edge detection (Image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Reservoirs%22">Reservoirs</searchLink><br /><searchLink fieldCode="DE" term="%22Random+noise+theory%22">Random noise theory</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: ABSTRACT: Automatic feature detection from seismic data is a demanding task in today's interpretation workstations. Channels are among important stratigraphic features in seismic data both due to their reservoir capability or drilling hazard potential. Shearlet transform as a multi‐scale and multi‐directional transformation is capable of detecting anisotropic singularities in two and higher dimensional data. Channels occur as edges in seismic data, which can be detected based on maximizing the shearlet coefficients through all sub‐volumes at the finest scale of decomposition. The detected edges may require further refinement through the application of a thinning methodology. In this study, a three‐dimensional, pyramid‐adapted, compactly supported shearlet transform was applied to synthetic and real channelised, three‐dimensional post‐stack seismic data in order to decompose the data into different scales and directions for the purpose of channel boundary detection. In order to be able to compare the edge detection results based on three‐dimensional shearlet transform with some famous gradient‐based edge detectors, such as Sobel and Canny, a thresholding scheme is necessary. In both synthetic and real data examples, the three‐dimensional shearlet edge detection algorithm outperformed Sobel and Canny operators even in the presence of Gaussian random noise. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Geophysical Prospecting is the property of Wiley-Blackwell 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: 10.1111/1365-2478.12629 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1272 Subjects: – SubjectFull: Seismic reflection method Type: general – SubjectFull: Edge detection (Image processing) Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Reservoirs Type: general – SubjectFull: Random noise theory Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Seismic channel edge detection using 3D shearlets—a study on synthetic and real channelised 3D seismic data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Karbalaali, Haleh – PersonEntity: Name: NameFull: Javaherian, Abdolrahim – PersonEntity: Name: NameFull: Dahlke, Stephan – PersonEntity: Name: NameFull: Reisenhofer, Rafael – PersonEntity: Name: NameFull: Torabi, Siyavash IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 00168025 Numbering: – Type: volume Value: 66 – Type: issue Value: 7 Titles: – TitleFull: Geophysical Prospecting Type: main |
| ResultId | 1 |
