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dc.contributor.advisor Symes, William W
dc.creatorTerentyev, Igor
dc.date.accessioned 2018-06-21T19:57:18Z
dc.date.available 2018-06-21T19:57:18Z
dc.date.created 2017-05
dc.date.issued 2017-09-25
dc.date.submitted May 2017
dc.identifier.citation Terentyev, Igor. "Nonlinear waveform inversion with surface-oriented extended modeling." (2017) Diss., Rice University. http://hdl.handle.net/1911/102263.
dc.identifier.urihttp://hdl.handle.net/1911/102263
dc.description.abstract This thesis investigates surface-oriented model extension approach to nonlinear full waveform inversion (FWI). Conventional least-squares (LS) approach is capable of reconstructing highly detailed models of subsurface. Resolution requirements of the realistic problems dictate the use of local descent methods to solve the LS optimization problem. However, in the setting of any characteristic seismic problem, LS objective functional has numerous local extrema, rendering descent methods unsuitable when initial estimate is not kinematically accurate. The aim of my work is to improve convexity properties of the objective functional. I use the extended modeling approach, and construct an extended optimization functional incorporating differential semblance condition. An important advantage of surface-oriented extensions is that they do not increase the computational complexity of the forward modeling. This approach blends FWI technique with migration velocity analysis (MVA) capability to recover long scale velocity model, producing optimization problems that combine global convergence properties of the MVA with data fitting approach of FWI. In particular, it takes into account nonlinear physical effects, such as multiple reflections. I employ variable projection approach to solve the extended optimization problem. I validate the method on synthetic models for the constant density acoustics problem.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectfull waveform inversion
optimization
dc.title Nonlinear waveform inversion with surface-oriented extended modeling
dc.date.updated 2018-06-21T19:57:18Z
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Computational and Applied Mathematics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy


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