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Efficient algorithms for least squares wave equation migration and source signature estimation Open Access


Other title
wave equation
least squares
Type of item
Degree grantor
University of Alberta
Author or creator
Kazemi Nojadeh, Nasser
Supervisor and department
Sacchi, Mauricio (Physics)
Examining committee member and department
Lines, Laurence (Geoscience)
Morsink, Sharon (Physics)
Sacchi, Mauricio (Physics)
Gu, Yu Jeffrey (Physics)
Boisvert, Jeff (Civil and Environmental Engineering)
Department of Physics
Date accepted
Graduation date
2017-11:Fall 2017
Doctor of Philosophy
Degree level
Estimating accurate images of the subsurface is one of the end products of seismic data processing. Numerical solutions to the wave equation allow designing linearized forward operators. The adjoint of the linearized forward operator is used to image the interior of the earth. The adjoint operator (migration operator) is sensitive to data sampling and background velocity model. Likewise, migration via adjoint operators produces low-resolution images of the subsurface. Posing seismic imaging as an inverse problem leads to a procedure where the inversion of the linearized forward modelling operator can retrieve an image that honours the seismic observations. Formulating imaging as an inverse problem, besides, allows one to include model space constraints to improve the quality and resolution of subsurface images. This thesis concentrates on the development of efficient and accurate methods for linearized imaging also called least-squares migration. Developing efficient algorithms for least-squares migration is a vital step in better understanding the earth’s subsurface structure. However, computational requirements and proper data conditioning are some of the barriers that prevent least-squares migration from becoming a routinely used processing workflow. As part of the development of practical algorithms for least-squares migration, we took advantage of adaptive signal processing strategies and the computational efficiency of preconditioning techniques. We develop scalable algorithms for least-squares migration with less memory and computation time requirements. Our goal is to achieve similar imaging results without compromising the accuracy of the least-squares migration algorithm. Given that the least-squares migration method is sensitive to the accuracy of the seismic source function, the thesis also examines the pervasive problem of seismic source estimation and provides an algorithm for seismic source estimation that does not require the traditional minimum phase assumption. Imaging tests with synthetic data and a real marine dataset (Mississippi Canyon, north-central Gulf of Mexico south of Louisiana) exemplify the algorithms proposed in this thesis.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Nasser Kazemi and Mauricio D. Sacchi (2014). ”Sparse multichannel blind deconvolution.” GEOPHYSICS, 79(5), V143-V152. doi: 10.1190/geo2013-0465.1
Kazemi and Mauricio D. Sacchi (2015). ”Block row recursive least-squares migration.” GEOPHYSICS, 80(5), A95-A101. doi: 10.1190/geo2015-0070.1

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File title: Efficient algorithms for least squares wave equation migration, Ph. D. Thesis
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