Scattering-based decomposition of sensitivity kernels for full-waveform inversion

Field: Earth and Planetary Sciences (Physical Sciences)

PreviousNext No Access12th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 15–18 August 2011Scattering-based decomposition of sensitivity kernels for full-waveform inversionAuthors: Daniel Leal MacedoIvan VasconcelosDaniel Leal MacedoUniversidade Estadual de Campinas; and Ivan VasconcelosSchlumberger Cambridge Researchhttps://doi.org/10.1190/sbgf2011-184 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract With the increasing demand in complexity for subsurface models in environments such as subsalt, sub-basalt and pre-salt, full-waveform inversion (FWI) is quickly becoming one of the model-building methods of choice. While in principle capable of handling all of the nonlinearity in the data, in practice nonlinear gradient-based FWI is limited due to its notorious sensitivity to the choice of starting models. To help addressing model convergence issues in FWI, in this paper we analyze the role of nonlinearity in the so-called sensitivity kernels, which are the centerpiece of gradient-based FWI algorithms. Using a scattering-based approach and assuming acoustic-only data, we start by reparameterizing the subsurface model in terms of smooth and singular components for both compressibility and density. This leads to a decomposition of the data into a reference field that is sensitive only to the smooth model, and a scattered field sensitive to both smooth and sharp model components. Focussing on the model backprojections from the scattered data only, we provide expressions for the Fréchet-derivative sensitivity kernels of all four model parameters. Our results provide for the decomposition of current FWI kernels into no less than nine different sub-kernels which have explicitly different levels of nonlinearity with respect to both data and model parameters. This capability to discern levels of nonlinearity within FWI kernels is key to understanding model convergence in gradient-based, iterative FWI. We illustrate this by analyzing some of the sub-kernel terms in detail. The scattering-based FWI kernel decomposition we provide could have broad potential applications, such devising multiscale FWI algorithms, and improving velocity model building in the image domain using extended image gathers. Keywords: inversion, full-waveform, scatteringPermalink: https://doi.org/10.1190/sbgf2011-184FiguresReferencesRelatedDetails 12th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 15–18 August 2011ISSN (online):2159-6832Copyright: 2011 Pages: 2223 publication data© 2011 Published in electronic format with permission by the Brazilian Geophysical SocietyPublisher:Society of Exploration Geophysicists HistoryPublished Online: 13 Mar 2014 CITATION INFORMATION Daniel Leal Macedo and Ivan Vasconcelos, (2011), "Scattering-based decomposition of sensitivity kernels for full-waveform inversion," SEG Global Meeting Abstracts : 889-894. https://doi.org/10.1190/sbgf2011-184 Plain-Language Summary Keywordsinversionfull-waveformscatteringPDF DownloadLoading ...