@conference {lin2014SEGmdg, title = {Mitigating data gaps in the estimation of primaries by sparse inversion without data reconstruction}, booktitle = {SEG Technical Program Expanded Abstracts}, year = {2014}, note = {(SEG)}, month = {10}, pages = {4157-4161}, 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.}, keywords = {algorithm, EPSI, inversion, multiples, REPSI, SEG}, doi = {http://dx.doi.org/10.1190/segam2014-1680.1}, url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/lin2014SEGmdg/lin2014SEGmdg.html}, presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/lin2014SEGmdg/lin2014SEGmdg_pres.pdf}, author = {Tim T.Y. Lin and Felix J. Herrmann} }