Symes, William W.2013-07-242013-07-242013-07-242013-07-242012-122013-07-24December 2Sun, Dong. "A Nonlinear Differential Semblance Algorithm for Waveform Inversion." (2013) Diss., Rice University. <a href="https://hdl.handle.net/1911/71694">https://hdl.handle.net/1911/71694</a>.https://hdl.handle.net/1911/71694This thesis proposes a nonlinear differential semblance approach to full waveform inversion as an alternative to standard least squares inversion, which cannot guarantee a reliable solution, because of the existence of many spurious local minima of the objective function for typical data that lacks low-frequency energy. Nonlinear differential semblance optimization combines the ability of full waveform inversion to account for nonlinear physical effects, such as multiple reflections, with the tendency of differential semblance migration velocity analysis to avoid local minima. It borrows the gather-flattening concept from migration velocity analysis, and updates the velocity by flattening primaries-only gathers obtained via nonlinear inversion. I describe a general formulation of this algorithm, its main components and implementation. Numerical experiments show for simple layered models, standard least squares inversion fails, whereas nonlinear differential semblance succeeds in constructing a kinematically correct model and fitting the data rather precisely.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Waveform inversionNonlinear differential semblance optimizationExtended modelingLow-frequency controlThesis2013-07-24123456789/ETD-2012-12-321