Wave-equation Q tomography and least-squares migration

Handle URI:
http://hdl.handle.net/10754/605856
Title:
Wave-equation Q tomography and least-squares migration
Authors:
Dutta, Gaurav ( 0000-0002-2024-1683 )
Abstract:
This thesis designs new methods for Q tomography and Q-compensated prestack depth migration when the recorded seismic data suffer from strong attenuation. A motivation of this work is that the presence of gas clouds or mud channels in overburden structures leads to the distortion of amplitudes and phases in seismic waves propagating inside the earth. If the attenuation parameter Q is very strong, i.e., Q<30, ignoring the anelastic effects in imaging can lead to dimming of migration amplitudes and loss of resolution. This, in turn, adversely affects the ability to accurately predict reservoir properties below such layers. To mitigate this problem, I first develop an anelastic least-squares reverse time migration (Q-LSRTM) technique. I reformulate the conventional acoustic least-squares migration problem as a viscoacoustic linearized inversion problem. Using linearized viscoacoustic modeling and adjoint operators during the least-squares iterations, I show with numerical tests that Q-LSRTM can compensate for the amplitude loss and produce images with better balanced amplitudes than conventional migration. To estimate the background Q model that can be used for any Q-compensating migration algorithm, I then develop a wave-equation based optimization method that inverts for the subsurface Q distribution by minimizing a skeletonized misfit function ε. Here, ε is the sum of the squared differences between the observed and the predicted peak/centroid-frequency shifts of the early-arrivals. Through numerical tests on synthetic and field data, I show that noticeable improvements in the migration image quality can be obtained from Q models inverted using wave-equation Q tomography. A key feature of skeletonized inversion is that it is much less likely to get stuck in a local minimum than a standard waveform inversion method. Finally, I develop a preconditioning technique for least-squares migration using a directional Gabor-based preconditioning approach for isotropic, anisotropic or anelastic least-squares migration. During the least-squares iterations, I impose sparsity constraints on the inverted reflectivity model in the local Radon domain. The forward and the inverse mapping of the reflectivity to the local Radon domain is done through 3D Fourier-based discrete Radon transform operators. Using numerical tests on synthetic and 3D field data, I demonstrate that the proposed preconditioning approach can discriminate against artifacts in the image resulting from irregular or insufficient acquisition and can produce images with improved signal-to-noise ratio when compared with standard migration.
Advisors:
Schuster, Gerard T. ( 0000-0001-7532-1587 )
Committee Member:
Williamson, Paul; Alkhalifah, Tariq; Peter, Daniel; Wu, Ying ( 0000-0002-7919-1107 ) ; Turkiyyah, George M.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program
Program:
Earth Sciences and Engineering
Issue Date:
Mar-2016
Type:
Dissertation
Appears in Collections:
Dissertations; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.advisorSchuster, Gerard T.en
dc.contributor.authorDutta, Gauraven
dc.date.accessioned2016-04-19T08:44:09Zen
dc.date.available2016-04-19T08:44:09Zen
dc.date.issued2016-03en
dc.identifier.urihttp://hdl.handle.net/10754/605856en
dc.description.abstractThis thesis designs new methods for Q tomography and Q-compensated prestack depth migration when the recorded seismic data suffer from strong attenuation. A motivation of this work is that the presence of gas clouds or mud channels in overburden structures leads to the distortion of amplitudes and phases in seismic waves propagating inside the earth. If the attenuation parameter Q is very strong, i.e., Q<30, ignoring the anelastic effects in imaging can lead to dimming of migration amplitudes and loss of resolution. This, in turn, adversely affects the ability to accurately predict reservoir properties below such layers. To mitigate this problem, I first develop an anelastic least-squares reverse time migration (Q-LSRTM) technique. I reformulate the conventional acoustic least-squares migration problem as a viscoacoustic linearized inversion problem. Using linearized viscoacoustic modeling and adjoint operators during the least-squares iterations, I show with numerical tests that Q-LSRTM can compensate for the amplitude loss and produce images with better balanced amplitudes than conventional migration. To estimate the background Q model that can be used for any Q-compensating migration algorithm, I then develop a wave-equation based optimization method that inverts for the subsurface Q distribution by minimizing a skeletonized misfit function ε. Here, ε is the sum of the squared differences between the observed and the predicted peak/centroid-frequency shifts of the early-arrivals. Through numerical tests on synthetic and field data, I show that noticeable improvements in the migration image quality can be obtained from Q models inverted using wave-equation Q tomography. A key feature of skeletonized inversion is that it is much less likely to get stuck in a local minimum than a standard waveform inversion method. Finally, I develop a preconditioning technique for least-squares migration using a directional Gabor-based preconditioning approach for isotropic, anisotropic or anelastic least-squares migration. During the least-squares iterations, I impose sparsity constraints on the inverted reflectivity model in the local Radon domain. The forward and the inverse mapping of the reflectivity to the local Radon domain is done through 3D Fourier-based discrete Radon transform operators. Using numerical tests on synthetic and 3D field data, I demonstrate that the proposed preconditioning approach can discriminate against artifacts in the image resulting from irregular or insufficient acquisition and can produce images with improved signal-to-noise ratio when compared with standard migration.en
dc.language.isoenen
dc.subjectAttenuationen
dc.subjectTomographyen
dc.subjectLeast-squaresen
dc.subjectRTMen
dc.subjectQ-compensationen
dc.titleWave-equation Q tomography and least-squares migrationen
dc.typeDissertationen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEarth Science and Engineering Programen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberWilliamson, Paulen
dc.contributor.committeememberAlkhalifah, Tariqen
dc.contributor.committeememberPeter, Danielen
dc.contributor.committeememberWu, Yingen
dc.contributor.committeememberTurkiyyah, George M.en
thesis.degree.disciplineEarth Sciences and Engineeringen
thesis.degree.nameDoctor of Philosophyen
dc.person.id117361en
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