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dc.contributor.advisorHale, Dave, 1955-
dc.contributor.authorMa, Yong
dc.date.accessioned2007-01-03T04:18:43Z
dc.date.accessioned2022-02-03T11:53:10Z
dc.date.available2013-05-02T04:18:44Z
dc.date.available2022-02-03T11:53:10Z
dc.date.issued2012
dc.date.submitted2012
dc.identifierT 7157
dc.identifier.urihttps://hdl.handle.net/11124/76636
dc.description2012 Fall.
dc.descriptionIncludes. Includes illustrations (some color).
dc.descriptionIncludes bibliographical references.
dc.description.abstractThe objective of this thesis is to investigate new methods in reflection seismology for estimating subsurface velocity models accurately and efficiently. Full waveform inversion (FWI) is a state-of-the-art approach that can yield high-resolution models. However, multiple problems, including high computational cost, spurious local minima and possible geologically-implausible solutions, have prevented widespread applications of FWI for reflection data. These problems correspond to the fact that we in practice must estimate numerous model parameters with inadequate data (e.g., limited aperture, insufficient low frequencies). To tackle the problems and achieve the goal of this thesis, I develop image-guided sparse-model FWI (IGFWI) and wave-equation reflection traveltime inversion (WERTI). IGFWI uses subsurface structures to constrain the inversion of a sparse model. I extract the subsurface structures from migrated seismic images. With the structures, I construct a sparse model, which can, with substantially fewer parameters, explain the subsurface in a geologically plausible way. The IGFWI optimization problem is then formulated with respect to the sparse model that is constrained by structures. With fewer parameters, IGFWI converges faster in fewer iterations than does conventional FWI; with structural constraints, estimated models are geologically plausible; with a sparse representation of the model, blocky updates mitigate the lack of low frequencies. I solve IGFWI using a image-guided conjugate-gradient method. With respect to the sparse model, I explicitly compute a projected Hessian matrix and its inverse using a projected BFGS (P-BFGS) method. Compared to the classic limited-memory BFGS (L-BFGS) method, P-BFGS substantially saves both computational time and memory, due to fewer model parameters of the sparse model. With the P-BFGS method, I propose an efficient quasi-Newton method to solve large-scale inversion problems, such as FWI. I also introduce WERTI to recover the low-wavenumber velocity background, and thereby mitigate local minima in reflection FWI with insufficient low frequencies. By alternating between WERTI and FWI, we can overcome the velocity-depth ambiguity in reflection traveltime inversion and estimate both the low- and high-wavenumber components of velocity models using only high-frequency reflection data .
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2012 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectreflection seismology
dc.subjectleast squares
dc.subjecttraveltime inversion
dc.subjectvelocity estimation
dc.subjectwaveform inversion
dc.subject.lcshSeismic traveltime inversion
dc.subject.lcshLeast squares
dc.subject.lcshSeismic reflection method
dc.subject.lcshSeismic waves--Measurement
dc.titleWaveform-based velocity estimation from reflection seismic data
dc.typeText
dc.contributor.committeememberSnieder, Roel, 1958-
dc.contributor.committeememberLi, Yaoguo
dc.contributor.committeememberLusk, Mark T.
dc.contributor.committeememberSzymczak, Andrzej
dcterms.embargo.terms2013-05-02
dcterms.embargo.expires2013-05-02
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineGeophysics
thesis.degree.grantorColorado School of Mines
dc.rights.access6-month embargo


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