Least-squares reverse time migration with local Radon-based preconditioning

Handle URI:
http://hdl.handle.net/10754/623005
Title:
Least-squares reverse time migration with local Radon-based preconditioning
Authors:
Dutta, Gaurav ( 0000-0002-2024-1683 ) ; Giboli, Matteo; Agut, Cyril; Williamson, Paul; Schuster, Gerard T. ( 0000-0001-7532-1587 )
Abstract:
Least-squares migration (LSM) can produce images with better balanced amplitudes and fewer artifacts than standard migration. The conventional objective function used for LSM minimizes the L2-norm of the data residual between the predicted and the observed data. However, for field-data applications in which the recorded data are noisy and undersampled, the conventional formulation of LSM fails to provide the desired uplift in the quality of the inverted image. We have developed a leastsquares reverse time migration (LSRTM) method using local Radon-based preconditioning to overcome the low signal-tonoise ratio (S/N) problem of noisy or severely undersampled data. A high-resolution local Radon transform of the reflectivity is used, and sparseness constraints are imposed on the inverted reflectivity in the local Radon domain. The sparseness constraint is that the inverted reflectivity is sparse in the Radon domain and each location of the subsurface is represented by a limited number of geologic dips. The forward and the inverse mapping of the reflectivity to the local Radon domain and vice versa is done through 3D Fourier-based discrete Radon transform operators. The weights for the preconditioning are chosen to be varying locally based on the relative amplitudes of the local dips or assigned using quantile measures. Numerical tests on synthetic and field data validate the effectiveness of our approach in producing images with good S/N and fewer aliasing artifacts when compared with standard RTM or standard LSRTM.
KAUST Department:
Earth Science and Engineering Program
Citation:
Dutta G, Giboli M, Agut C, Williamson P, Schuster GT (2017) Least-squares reverse time migration with local Radon-based preconditioning. GEOPHYSICS 82: S75–S84. Available: http://dx.doi.org/10.1190/GEO2016-0117.1.
Publisher:
Society of Exploration Geophysicists
Journal:
GEOPHYSICS
Issue Date:
8-Mar-2017
DOI:
10.1190/GEO2016-0117.1
Type:
Article
ISSN:
0016-8033; 1942-2156
Sponsors:
G. Dutta would like to thank King Abdullah University of Science and Technology (KAUST) for funding his graduate studies. The authors are grateful to TOTAL E&P France for permission to publish this work. We also thank the associate editor K. Innanen and the three anonymous reviewers for their helpful comments and suggestions. The plots for this paper have been prepared using the Madagascar open-source software package.
Additional Links:
http://library.seg.org/doi/abs/10.1190/geo2016-0117.1
Appears in Collections:
Articles; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorDutta, Gauraven
dc.contributor.authorGiboli, Matteoen
dc.contributor.authorAgut, Cyrilen
dc.contributor.authorWilliamson, Paulen
dc.contributor.authorSchuster, Gerard T.en
dc.date.accessioned2017-03-15T07:15:27Z-
dc.date.available2017-03-15T07:15:27Z-
dc.date.issued2017-03-08en
dc.identifier.citationDutta G, Giboli M, Agut C, Williamson P, Schuster GT (2017) Least-squares reverse time migration with local Radon-based preconditioning. GEOPHYSICS 82: S75–S84. Available: http://dx.doi.org/10.1190/GEO2016-0117.1.en
dc.identifier.issn0016-8033en
dc.identifier.issn1942-2156en
dc.identifier.doi10.1190/GEO2016-0117.1en
dc.identifier.urihttp://hdl.handle.net/10754/623005-
dc.description.abstractLeast-squares migration (LSM) can produce images with better balanced amplitudes and fewer artifacts than standard migration. The conventional objective function used for LSM minimizes the L2-norm of the data residual between the predicted and the observed data. However, for field-data applications in which the recorded data are noisy and undersampled, the conventional formulation of LSM fails to provide the desired uplift in the quality of the inverted image. We have developed a leastsquares reverse time migration (LSRTM) method using local Radon-based preconditioning to overcome the low signal-tonoise ratio (S/N) problem of noisy or severely undersampled data. A high-resolution local Radon transform of the reflectivity is used, and sparseness constraints are imposed on the inverted reflectivity in the local Radon domain. The sparseness constraint is that the inverted reflectivity is sparse in the Radon domain and each location of the subsurface is represented by a limited number of geologic dips. The forward and the inverse mapping of the reflectivity to the local Radon domain and vice versa is done through 3D Fourier-based discrete Radon transform operators. The weights for the preconditioning are chosen to be varying locally based on the relative amplitudes of the local dips or assigned using quantile measures. Numerical tests on synthetic and field data validate the effectiveness of our approach in producing images with good S/N and fewer aliasing artifacts when compared with standard RTM or standard LSRTM.en
dc.description.sponsorshipG. Dutta would like to thank King Abdullah University of Science and Technology (KAUST) for funding his graduate studies. The authors are grateful to TOTAL E&P France for permission to publish this work. We also thank the associate editor K. Innanen and the three anonymous reviewers for their helpful comments and suggestions. The plots for this paper have been prepared using the Madagascar open-source software package.en
dc.publisherSociety of Exploration Geophysicistsen
dc.relation.urlhttp://library.seg.org/doi/abs/10.1190/geo2016-0117.1en
dc.rightsArchived with thanks to GEOPHYSICSen
dc.titleLeast-squares reverse time migration with local Radon-based preconditioningen
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.identifier.journalGEOPHYSICSen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionTOTAL Exploration and Production, CSTJF, Pau, , Franceen
kaust.authorDutta, Gauraven
kaust.authorSchuster, Gerard T.en
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