Compressed-sensing application - Pre-stack kirchhoff migration

Handle URI:
http://hdl.handle.net/10754/564673
Title:
Compressed-sensing application - Pre-stack kirchhoff migration
Authors:
Aldawood, Ali ( 0000-0002-9535-0825 ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Alkhalifah, Tariq Ali ( 0000-0002-9363-9799 )
Abstract:
Least-squares migration is a linearized form of waveform inversion that aims to enhance the spatial resolution of the subsurface reflectivity distribution and reduce the migration artifacts due to limited recording aperture, coarse sampling of sources and receivers, and low subsurface illumination. Least-squares migration, however, due to the nature of its minimization process, tends to produce smoothed and dispersed versions of the reflectivity of the subsurface. Assuming that the subsurface reflectivity distribution is sparse, we propose the addition of a non-quadratic L1-norm penalty term on the model space in the objective function. This aims to preserve the sparse nature of the subsurface reflectivity series and enhance resolution. We further use a compressed-sensing algorithm to solve the linear system, which utilizes the sparsity assumption to produce highly resolved migrated images. Thus, the Kirchhoff migration implementation is formulated as a Basis Pursuit denoise (BPDN) problem to obtain the sparse reflectivity model. Applications on synthetic data show that reflectivity models obtained using this compressed-sensing algorithm are highly accurate with optimal resolution.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Science and Engineering Program; Earth Sciences and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
EAGE Publications
Journal:
London 2013, 75th eage conference en exhibition incorporating SPE Europec
Conference/Event name:
75th European Association of Geoscientists and Engineers Conference and Exhibition 2013 Incorporating SPE EUROPEC 2013: Changing Frontiers
Issue Date:
2013
DOI:
10.3997/2214-4609.20130612
Type:
Conference Paper
ISBN:
9781629937915
Appears in Collections:
Conference Papers; Environmental Science and Engineering Program; 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.accessioned2015-08-04T07:11:51Zen
dc.date.available2015-08-04T07:11:51Zen
dc.date.issued2013en
dc.identifier.isbn9781629937915en
dc.identifier.doi10.3997/2214-4609.20130612en
dc.identifier.urihttp://hdl.handle.net/10754/564673en
dc.description.abstractLeast-squares migration is a linearized form of waveform inversion that aims to enhance the spatial resolution of the subsurface reflectivity distribution and reduce the migration artifacts due to limited recording aperture, coarse sampling of sources and receivers, and low subsurface illumination. Least-squares migration, however, due to the nature of its minimization process, tends to produce smoothed and dispersed versions of the reflectivity of the subsurface. Assuming that the subsurface reflectivity distribution is sparse, we propose the addition of a non-quadratic L1-norm penalty term on the model space in the objective function. This aims to preserve the sparse nature of the subsurface reflectivity series and enhance resolution. We further use a compressed-sensing algorithm to solve the linear system, which utilizes the sparsity assumption to produce highly resolved migrated images. Thus, the Kirchhoff migration implementation is formulated as a Basis Pursuit denoise (BPDN) problem to obtain the sparse reflectivity model. Applications on synthetic data show that reflectivity models obtained using this compressed-sensing algorithm are highly accurate with optimal resolution.en
dc.publisherEAGE Publicationsen
dc.titleCompressed-sensing application - Pre-stack kirchhoff migrationen
dc.typeConference Paperen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentEarth Sciences and Engineering Programen
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.identifier.journalLondon 2013, 75th eage conference en exhibition incorporating SPE Europecen
dc.conference.date10 June 2013 through 13 June 2013en
dc.conference.name75th European Association of Geoscientists and Engineers Conference and Exhibition 2013 Incorporating SPE EUROPEC 2013: Changing Frontiersen
kaust.authorAldawood, Alien
kaust.authorHoteit, Ibrahimen
kaust.authorAlkhalifah, Tariq Alien
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