Making the most out of the least (squares migration)

Handle URI:
http://hdl.handle.net/10754/593347
Title:
Making the most out of the least (squares migration)
Authors:
Dutta, Gaurav ( 0000-0002-2024-1683 ) ; Huang, Yunsong; Dai, Wei; Wang, Xin; Schuster, Gerard T. ( 0000-0001-7532-1587 )
Abstract:
Standard migration images can suffer from migration artifacts due to 1) poor source-receiver sampling, 2) weak amplitudes caused by geometric spreading, 3) attenuation, 4) defocusing, 5) poor resolution due to limited source-receiver aperture, and 6) ringiness caused by a ringy source wavelet. To partly remedy these problems, least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution (MD), proposes to linearly invert seismic data for the reflectivity distribution. If the migration velocity model is sufficiently accurate, then LSM can mitigate many of the above problems and lead to a more resolved migration image, sometimes with twice the spatial resolution. However, there are two problems with LSM: the cost can be an order of magnitude more than standard migration and the quality of the LSM image is no better than the standard image for velocity errors of 5% or more. We now show how to get the most from least-squares migration by reducing the cost and velocity sensitivity of LSM.
KAUST Department:
Earth Science and Engineering Program; Physical Sciences and Engineering (PSE) Division
Publisher:
Society of Exploration Geophysicists
Journal:
SEG Technical Program Expanded Abstracts 2014
Conference/Event name:
SEG Technical Program Expanded Abstracts 2014
Issue Date:
5-Aug-2014
DOI:
10.1190/segam2014-1242.1
Type:
Conference Paper
Additional Links:
http://library.seg.org/doi/abs/10.1190/segam2014-1242.1
Appears in Collections:
Conference Papers; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorDutta, Gauraven
dc.contributor.authorHuang, Yunsongen
dc.contributor.authorDai, Weien
dc.contributor.authorWang, Xinen
dc.contributor.authorSchuster, Gerard T.en
dc.date.accessioned2016-01-13T09:55:46Zen
dc.date.available2016-01-13T09:55:46Zen
dc.date.issued2014-08-05en
dc.identifier.doi10.1190/segam2014-1242.1en
dc.identifier.urihttp://hdl.handle.net/10754/593347en
dc.description.abstractStandard migration images can suffer from migration artifacts due to 1) poor source-receiver sampling, 2) weak amplitudes caused by geometric spreading, 3) attenuation, 4) defocusing, 5) poor resolution due to limited source-receiver aperture, and 6) ringiness caused by a ringy source wavelet. To partly remedy these problems, least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution (MD), proposes to linearly invert seismic data for the reflectivity distribution. If the migration velocity model is sufficiently accurate, then LSM can mitigate many of the above problems and lead to a more resolved migration image, sometimes with twice the spatial resolution. However, there are two problems with LSM: the cost can be an order of magnitude more than standard migration and the quality of the LSM image is no better than the standard image for velocity errors of 5% or more. We now show how to get the most from least-squares migration by reducing the cost and velocity sensitivity of LSM.en
dc.publisherSociety of Exploration Geophysicistsen
dc.relation.urlhttp://library.seg.org/doi/abs/10.1190/segam2014-1242.1en
dc.subjectattenuationen
dc.subjectimagingen
dc.subjectleast squaresen
dc.subjectmigrationen
dc.subjectreverse-timeen
dc.titleMaking the most out of the least (squares migration)en
dc.typeConference Paperen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalSEG Technical Program Expanded Abstracts 2014en
dc.conference.nameSEG Technical Program Expanded Abstracts 2014en
dc.eprint.versionPublisher's Version/PDFen
kaust.authorDutta, Gauraven
kaust.authorHuang, Yunsongen
kaust.authorDai, Weien
kaust.authorWang, Xinen
kaust.authorSchuster, Gerard T.en
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