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dc.contributor.authorPadhi, Amit
dc.contributor.authorMallick, Subhashis
dc.contributor.authorPadhi, Amit
dc.contributor.authorMallick, Subhashis
dc.date2014-03-01
dc.date.accessioned2018-06-10T21:30:57Z
dc.date.available2018-06-10T21:30:57Z
dc.description©2013 The Authors. Published by Oxford University Press on behalf of The Royal Astronomical Society.
dc.description.abstractInversion of band- and offset-limited single component (P wave) seismic data does not provide robust estimates of subsurface elastic parameters and density. Multicomponent seismic data can, in principle, circumvent this limitation but adds to the complexity of the inversion algorithm because it requires simultaneous optimization of multiple objective functions, one for each data component. In seismology, these multiple objectives are typically handled by constructing a single objective given as a weighted sum of the objectives of individual data components and sometimes with additional regularization terms reflecting their interdependence; which is then followed by a single objective optimization. Multi-objective problems, inclusive of the multicomponent seismic inversion are however non-linear. They have nonunique solutions, known as the Pareto-optimal solutions. Therefore, casting such problems as a single objective optimization provides one out of the entire set of the Pareto-optimal solutions, which in turn, may be biased by the choice of the weights. To handle multiple objectives, it is thus appropriate to treat the objective as a vector and simultaneously optimize each of its components so that the entire Pareto-optimal set of solutions could be estimated. This paper proposes such a novel multi-objective methodology using a non-dominated sorting genetic algorithm for waveform inversion of multicomponent seismic data. The applicability of the method is demonstrated using synthetic data generated from multilayer models based on a real well log. We document that the proposed method can reliably extract subsurface elastic parameters and density from multicomponent seismic data both when the subsurface is considered isotropic and transversely isotropic with a vertical symmetry axis. We also compute approximate uncertainty values in the derived parameters. Although we restrict our inversion applications to horizontally stratified models, we outline a practical procedure of extending the method to approximately include local dips for each source-receiver offset pair. Finally, the applicability of the proposed method is not just limited to seismic inversion but it could be used to invert different data types not only requiring multiple objectives but also multiple physics to describe them.
dc.identifier.doi10.1093/gji/ggt460
dc.identifier.urihttps://hdl.handle.net/20.500.11919/1186
dc.languageEnglish
dc.publisherUniversity of Wyoming. Libraries
dc.relation.ispartofFaculty Publication - Geology & Geophysics
dc.sourceGeology and Geophysics Faculty Publications
dc.subjectComputational seismology
dc.subjectInverse theory
dc.subjectSeismic anisotropy
dc.titleMulticomponent Pre-Stack Seismic Waveform Inversion in Transversely Isotropic Media Using a Non-Dominated Sorting Genetic Algorithm
dc.typeJournal contribution
dcterms.title.journalGeophysical Journal International
thesis.degree.disciplineGeology


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