Calculation of the inverse data space via sparse inversion

Handle URI:
http://hdl.handle.net/10754/577104
Title:
Calculation of the inverse data space via sparse inversion
Authors:
Saragiotis, Christos; Doulgeris, Panagiotis C.; Verschuur, Dirk Jacob Eric
Abstract:
The inverse data space provides a natural separation of primaries and surface-related multiples, as the surface multiples map onto the area around the origin while the primaries map elsewhere. However, the calculation of the inverse data is far from trivial as theory requires infinite time and offset recording. Furthermore regularization issues arise during inversion. We perform the inversion by minimizing the least-squares norm of the misfit function by constraining the $ell_1$ norm of the solution, being the inverse data space. In this way a sparse inversion approach is obtained. We show results on field data with an application to surface multiple removal.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Publisher:
EAGE Publications
Journal:
73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011
Issue Date:
1-Jan-2011
DOI:
10.3997/2214-4609.20149264
Type:
Conference Paper
ISBN:
9781617829666
Appears in Collections:
Conference Papers; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSaragiotis, Christosen
dc.contributor.authorDoulgeris, Panagiotis C.en
dc.contributor.authorVerschuur, Dirk Jacob Ericen
dc.date.accessioned2015-09-10T14:18:18Zen
dc.date.available2015-09-10T14:18:18Zen
dc.date.issued2011-01-01en
dc.identifier.isbn9781617829666en
dc.identifier.doi10.3997/2214-4609.20149264en
dc.identifier.urihttp://hdl.handle.net/10754/577104en
dc.description.abstractThe inverse data space provides a natural separation of primaries and surface-related multiples, as the surface multiples map onto the area around the origin while the primaries map elsewhere. However, the calculation of the inverse data is far from trivial as theory requires infinite time and offset recording. Furthermore regularization issues arise during inversion. We perform the inversion by minimizing the least-squares norm of the misfit function by constraining the $ell_1$ norm of the solution, being the inverse data space. In this way a sparse inversion approach is obtained. We show results on field data with an application to surface multiple removal.en
dc.publisherEAGE Publicationsen
dc.titleCalculation of the inverse data space via sparse inversionen
dc.typeConference Paperen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journal73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011en
dc.contributor.institutionDelft University of Technology, Netherlandsen
kaust.authorSaragiotis, Christosen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.