In which domain should we measure the misfit for robust full waveform inversion?

TitleIn which domain should we measure the misfit for robust full waveform inversion?
Publication TypeConference
Year of Publication2013
AuthorsTristan van Leeuwen, Aleksandr Y. Aravkin, Henri Calandra, Felix J. Herrmann
Conference NameEAGE Annual Conference Proceedings
Month06
KeywordsEAGE, full waveform inversion
Abstract

Full-waveform inversion relies on minimizing the difference between observed and modeled data, as measured by some penalty function. A popular choice, of course, is the least-squares penalty. However, when outliers are present in the data, the use of robust penalties such as the Huber or Student's t may significantly improve the results since they put relatively less weight on large residuals. In order for robust penalties to be effective, the outliers must be somehow localized and distinguishable from the good data. We propose to first transform the residual into a domain where the outliers are localized before measuring the misfit with a robust penalty. This is exactly how one would normally devise filters to remove the noise before applying conventional FWI. We propose to merge the two steps and let the inversion process implicitly filter out the noise. Results on a synthetic dataset show the effectiveness of the approach.

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Conferences/EAGE/2013/vanleeuwen2013EAGErobustFWI/vanleeuwen2013EAGErobustFWI.pdf
DOI10.3997/2214-4609.20130839
Presentation

https://www.slim.eos.ubc.ca/Publications/Public/Conferences/EAGE/2013/va...

Citation Keyvanleeuwen2013EAGErobustFWI
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