The possibilities of compressed sensing based migration

Handle URI:
http://hdl.handle.net/10754/593694
Title:
The possibilities of compressed sensing based migration
Authors:
Aldawood, Ali ( 0000-0002-9535-0825 ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Alkhalifah, Tariq Ali ( 0000-0002-9363-9799 )
Abstract:
Linearized waveform inversion or Least-square migration helps reduce migration artifacts caused by limited acquisition aperture, coarse sampling of sources and receivers, and low subsurface illumination. However, leastsquare migration, based on L2-norm minimization of the misfit function, tends to produce a smeared (smoothed) depiction of the true subsurface reflectivity. Assuming that the subsurface reflectivity distribution is a sparse signal, we use a compressed-sensing (Basis Pursuit) algorithm to retrieve this sparse distribution from a small number of linear measurements. We applied a compressed-sensing algorithm to image a synthetic fault model using dense and sparse acquisition geometries. Tests on synthetic data demonstrate the ability of compressed-sensing to produce highly resolved migrated images. We, also, studied the robustness of the Basis Pursuit algorithm in the presence of Gaussian random noise.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program
Publisher:
Society of Exploration Geophysicists
Journal:
SEG Technical Program Expanded Abstracts 2013
Conference/Event name:
SEG Technical Program Expanded Abstracts 2013
Issue Date:
22-Sep-2013
DOI:
10.1190/segam2013-0828.1
Type:
Conference Paper
Additional Links:
http://library.seg.org/doi/abs/10.1190/segam2013-0828.1
Appears in Collections:
Conference Papers; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorAldawood, Alien
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorAlkhalifah, Tariq Alien
dc.date.accessioned2016-01-18T08:24:16Zen
dc.date.available2016-01-18T08:24:16Zen
dc.date.issued2013-09-22en
dc.identifier.doi10.1190/segam2013-0828.1en
dc.identifier.urihttp://hdl.handle.net/10754/593694en
dc.description.abstractLinearized waveform inversion or Least-square migration helps reduce migration artifacts caused by limited acquisition aperture, coarse sampling of sources and receivers, and low subsurface illumination. However, leastsquare migration, based on L2-norm minimization of the misfit function, tends to produce a smeared (smoothed) depiction of the true subsurface reflectivity. Assuming that the subsurface reflectivity distribution is a sparse signal, we use a compressed-sensing (Basis Pursuit) algorithm to retrieve this sparse distribution from a small number of linear measurements. We applied a compressed-sensing algorithm to image a synthetic fault model using dense and sparse acquisition geometries. Tests on synthetic data demonstrate the ability of compressed-sensing to produce highly resolved migrated images. We, also, studied the robustness of the Basis Pursuit algorithm in the presence of Gaussian random noise.en
dc.publisherSociety of Exploration Geophysicistsen
dc.relation.urlhttp://library.seg.org/doi/abs/10.1190/segam2013-0828.1en
dc.subjectimagingen
dc.subjectfull-waveform inversionen
dc.subjecthigh-resolutionen
dc.subjectKirchhoffen
dc.subjectsparseen
dc.titleThe possibilities of compressed sensing based migrationen
dc.typeConference Paperen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEarth Science and Engineering Programen
dc.identifier.journalSEG Technical Program Expanded Abstracts 2013en
dc.conference.date22-27 September 2013en
dc.conference.nameSEG Technical Program Expanded Abstracts 2013en
dc.conference.locationHouston, Texas, USAen
dc.eprint.versionPublisher's Version/PDFen
kaust.authorAldawood, Alien
kaust.authorHoteit, Ibrahimen
kaust.authorAlkhalifah, Tariq Alien
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