The possibilities of compressed-sensing-based Kirchhoff prestack migration

Handle URI:
http://hdl.handle.net/10754/555648
Title:
The possibilities of compressed-sensing-based Kirchhoff prestack migration
Authors:
Aldawood, Ali ( 0000-0002-9535-0825 ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Alkhalifah, Tariq Ali ( 0000-0002-9363-9799 )
Abstract:
An approximate subsurface reflectivity distribution of the earth is usually obtained through the migration process. However, conventional migration algorithms, including those based on the least-squares approach, yield structure descriptions that are slightly smeared and of low resolution caused by the common migration artifacts due to limited aperture, coarse sampling, band-limited source, and low subsurface illumination. To alleviate this problem, we use the fact that minimizing the L1-norm of a signal promotes its sparsity. Thus, we formulated the Kirchhoff migration problem as a compressed-sensing (CS) basis pursuit denoise problem to solve for highly focused migrated images compared with those obtained by standard and least-squares migration algorithms. The results of various subsurface reflectivity models revealed that solutions computed using the CS based migration provide a more accurate subsurface reflectivity location and amplitude. We applied the CS algorithm to image synthetic data from a fault model using dense and sparse acquisition geometries. Our results suggest that the proposed approach may still provide highly resolved images with a relatively small number of measurements. We also evaluated the robustness of the basis pursuit denoise algorithm in the presence of Gaussian random observational noise and in the case of imaging the recorded data with inaccurate migration velocities.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Citation:
The possibilities of compressed-sensing-based Kirchhoff prestack migration 2014, 79 (3):S113 GEOPHYSICS
Publisher:
Society of Exploration Geophysicists
Journal:
GEOPHYSICS
Issue Date:
8-May-2014
DOI:
10.1190/geo2013-0271.1
Type:
Article
ISSN:
0016-8033; 1942-2156
Additional Links:
http://library.seg.org/doi/abs/10.1190/geo2013-0271.1
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAldawood, Alien
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorAlkhalifah, Tariq Alien
dc.date.accessioned2015-05-25T08:21:12Zen
dc.date.available2015-05-25T08:21:12Zen
dc.date.issued2014-05-08en
dc.identifier.citationThe possibilities of compressed-sensing-based Kirchhoff prestack migration 2014, 79 (3):S113 GEOPHYSICSen
dc.identifier.issn0016-8033en
dc.identifier.issn1942-2156en
dc.identifier.doi10.1190/geo2013-0271.1en
dc.identifier.urihttp://hdl.handle.net/10754/555648en
dc.description.abstractAn approximate subsurface reflectivity distribution of the earth is usually obtained through the migration process. However, conventional migration algorithms, including those based on the least-squares approach, yield structure descriptions that are slightly smeared and of low resolution caused by the common migration artifacts due to limited aperture, coarse sampling, band-limited source, and low subsurface illumination. To alleviate this problem, we use the fact that minimizing the L1-norm of a signal promotes its sparsity. Thus, we formulated the Kirchhoff migration problem as a compressed-sensing (CS) basis pursuit denoise problem to solve for highly focused migrated images compared with those obtained by standard and least-squares migration algorithms. The results of various subsurface reflectivity models revealed that solutions computed using the CS based migration provide a more accurate subsurface reflectivity location and amplitude. We applied the CS algorithm to image synthetic data from a fault model using dense and sparse acquisition geometries. Our results suggest that the proposed approach may still provide highly resolved images with a relatively small number of measurements. We also evaluated the robustness of the basis pursuit denoise algorithm in the presence of Gaussian random observational noise and in the case of imaging the recorded data with inaccurate migration velocities.en
dc.publisherSociety of Exploration Geophysicistsen
dc.relation.urlhttp://library.seg.org/doi/abs/10.1190/geo2013-0271.1en
dc.rightsArchived with thanks to GEOPHYSICSen
dc.subjectimagingen
dc.subjectsparseen
dc.subjectinversionen
dc.subjectleast squaresen
dc.subjectmigrationen
dc.titleThe possibilities of compressed-sensing-based Kirchhoff prestack migrationen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalGEOPHYSICSen
dc.eprint.versionPublisher's Version/PDFen
kaust.authorAldawood, Alien
kaust.authorHoteit, Ibrahimen
kaust.authorAlkhalifah, Tariq Alien
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