Mapping channel edges in seismic data using curvelet transform and morphological filter.
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| Title: | Mapping channel edges in seismic data using curvelet transform and morphological filter. |
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| Authors: | Boustani, Bahareh1, Javaherian, Abdolrahim1 javaheri@ut.ac.irjavaherian@aut.ac.ir, Nabi-Bidhendi, Mjid1, Torabi, Siyavash1, Amindavar, Hamid Reza1 |
| Source: | Journal of Applied Geophysics. Jan2019, Vol. 160, p57-68. 12p. |
| Subjects: | Curvelet transforms, Chemical decomposition, Seismology, Laplacian matrices, Laplacian operator |
| Abstract: | Abstract Mapping channel edges is a significant issue in 3D seismic data interpretation. In this research, curvelet transform was employed in channel edge enhancement, owing to its high ability to depict curve edges. The default parameters of curvelet transform were used to decompose the data. Hence, there are 6 scales and 16 directions in the 2nd level of decomposition for the real data of this study. Utilizing the modified top-hat algorithm, we calculated the maximum curvelet coefficients in all sub-bands. Employing top-hat in curvelet domain is more effective than the soft or hard thresholding to enhance the channel edges. Channel edges were further detected through morphological gradient algorithm with multi-length and multi-direction structuring elements. A directional feature of the proposed structuring element rendered the curvelet morphological gradient method used in the edge detection. Final edge map resulting from the weighted average of all sub-edge images was obtained from the structuring elements. Channel edge detection by the morphological gradient creates a large number of false edges. However, the combination of the morphological gradient with the curvelet transform eliminates many of those artifacts. The proposed algorithm was applied to both synthetic and real seismic data set containing channels. The findings resulted in a proper channel edge map as good as Canny, Sobel, and Laplacian of Gaussian edge detectors. Highlights • Mapping channel edges is a significant issue in 3D seismic interpretation. • Curvelet transform is a proper scale-space representation for curve singularities. • Curvelet transform is used to enhance channel boundaries in seismic horizon slice. • Morphological gradient algorithm detects channel edges. • The experimental results are comparable with the Canny, Sobel and LoG filters. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Applied Geophysics is the property of Elsevier B.V. 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: Mapping channel edges in seismic data using curvelet transform and morphological filter. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Boustani%2C+Bahareh%22">Boustani, Bahareh</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Javaherian%2C+Abdolrahim%22">Javaherian, Abdolrahim</searchLink><relatesTo>1</relatesTo><i> javaheri@ut.ac.irjavaherian@aut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Nabi-Bidhendi%2C+Mjid%22">Nabi-Bidhendi, Mjid</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Torabi%2C+Siyavash%22">Torabi, Siyavash</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Amindavar%2C+Hamid+Reza%22">Amindavar, Hamid Reza</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Geophysics%22">Journal of Applied Geophysics</searchLink>. Jan2019, Vol. 160, p57-68. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Curvelet+transforms%22">Curvelet transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+decomposition%22">Chemical decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22Seismology%22">Seismology</searchLink><br /><searchLink fieldCode="DE" term="%22Laplacian+matrices%22">Laplacian matrices</searchLink><br /><searchLink fieldCode="DE" term="%22Laplacian+operator%22">Laplacian operator</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract Mapping channel edges is a significant issue in 3D seismic data interpretation. In this research, curvelet transform was employed in channel edge enhancement, owing to its high ability to depict curve edges. The default parameters of curvelet transform were used to decompose the data. Hence, there are 6 scales and 16 directions in the 2nd level of decomposition for the real data of this study. Utilizing the modified top-hat algorithm, we calculated the maximum curvelet coefficients in all sub-bands. Employing top-hat in curvelet domain is more effective than the soft or hard thresholding to enhance the channel edges. Channel edges were further detected through morphological gradient algorithm with multi-length and multi-direction structuring elements. A directional feature of the proposed structuring element rendered the curvelet morphological gradient method used in the edge detection. Final edge map resulting from the weighted average of all sub-edge images was obtained from the structuring elements. Channel edge detection by the morphological gradient creates a large number of false edges. However, the combination of the morphological gradient with the curvelet transform eliminates many of those artifacts. The proposed algorithm was applied to both synthetic and real seismic data set containing channels. The findings resulted in a proper channel edge map as good as Canny, Sobel, and Laplacian of Gaussian edge detectors. Highlights • Mapping channel edges is a significant issue in 3D seismic interpretation. • Curvelet transform is a proper scale-space representation for curve singularities. • Curvelet transform is used to enhance channel boundaries in seismic horizon slice. • Morphological gradient algorithm detects channel edges. • The experimental results are comparable with the Canny, Sobel and LoG filters. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Applied Geophysics is the property of Elsevier B.V. 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.jappgeo.2018.11.004 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 57 Subjects: – SubjectFull: Curvelet transforms Type: general – SubjectFull: Chemical decomposition Type: general – SubjectFull: Seismology Type: general – SubjectFull: Laplacian matrices Type: general – SubjectFull: Laplacian operator Type: general Titles: – TitleFull: Mapping channel edges in seismic data using curvelet transform and morphological filter. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Boustani, Bahareh – PersonEntity: Name: NameFull: Javaherian, Abdolrahim – PersonEntity: Name: NameFull: Nabi-Bidhendi, Mjid – PersonEntity: Name: NameFull: Torabi, Siyavash – PersonEntity: Name: NameFull: Amindavar, Hamid Reza IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 09269851 Numbering: – Type: volume Value: 160 Titles: – TitleFull: Journal of Applied Geophysics Type: main |
| ResultId | 1 |
