Mitigating data gaps in the estimation of primaries by sparse inversion without data reconstruction

TitleMitigating data gaps in the estimation of primaries by sparse inversion without data reconstruction
Publication TypeConference
Year of Publication2014
AuthorsTim T.Y. Lin, Felix J. Herrmann
Conference NameSEG Technical Program Expanded Abstracts
Page4157-4161
Month10
Keywordsalgorithm, EPSI, inversion, multiples, REPSI, SEG
Abstract

We propose to solve the Estimation of Primaries by Sparse Inversion problem from a sesimic record with missing near-offsets and large holes without any explicit data reconstruction, by instead simulating the missing multiple contributions with terms involving auto-convolutions of the primary wavefield. Exclusion of the unknown data as an inversion variable from the REPSI process is desireable, since it eliminates a significant source of local minima that arises from attempting to invert for the unobserved traces using primary and multiple models that may be far-away from the true solution. In this talk we investigate the necessary modifications to the Robust EPSI algorithm to account for the resulting non-linear modeling operator, and demonstrate that just a few auto-convolution terms are enough to satisfactorily mitigate the effects of data gaps during the inversion process.

Notes

(SEG)

URLhttps://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/lin2014SEGmdg/lin2014SEGmdg.html
DOIhttp://dx.doi.org/10.1190/segam2014-1680.1
Presentation

https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/lin2014...

Citation Keylin2014SEGmdg