Optimization driven model-space versus data-space approaches to invert elastic data with the acoustic wave equation

TitleOptimization driven model-space versus data-space approaches to invert elastic data with the acoustic wave equation
Publication TypeConference
Year of Publication2013
AuthorsXiang Li, Anais Tamalet, Tristan van Leeuwen, Felix J. Herrmann
Conference NameSEG Technical Program Expanded Abstracts
Volume32
Page986-990
Month9
Keywordselastic, Full-waveform inversion, least-squares, SEG
Abstract

Inverting data with elastic phases using an acoustic wave equation can lead to erroneous results, especially when the number of iterations is too high, which may lead to over fitting the data. Several approaches have been proposed to address this issue. Most commonly, people apply "data-independent" filtering operations that are aimed to deemphasize the elastic phases in the data in favor of the acoustic phases. Examples of this approach are nested loops over offset range and Laplace parameters. In this paper, we discuss two complementary optimization-driven methods where the minimization process decides adaptively which of the data or model components are consistent with the objective. Specifically, we compare the Student's t misfit function as the data-space alternative and curvelet-domain sparsity promotion as the model-space alternative. Application of these two methods to a realistic synthetic lead to comparable results that we believe can be improved by combining these two methods.

URLhttps://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/li2013SEGodmvdaiedwawe/li2013SEGodmvdaiedwawe.pdf
DOI10.1190/segam2013-1375.1
Presentation

https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/li2013S...

Citation Keyli2013SEGodmvdaiedwawe
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