@conference {lopez2016CMSogl,
	title = {Off-the-grid low-rank matrix recovery: seismic data reconstruction},
	booktitle = {Canadian Mathematical Society Summer Meeting},
	year = {2016},
	note = {(CMS, Edmonton, Alberta)},
	month = {06},
	abstract = {This talk discusses a modified low-rank matrix recovery work-flow that admits unstructured observations. By incorporating a regularization operator which accurately maps structured data to unstructured data, into the nuclear-norm minimization problem, this approach is able to compensate for data irregularity. Furthermore, by construction this formulation yields output that is supported on a structured grid. Recovery error bounds are established for the methodology with matrix sensing and matrix completion numerical experiments including applications to seismic trace interpolation to demonstrate the potential of the approach.},
	keywords = {CMS, matrix completion, matrix sensing, nonuniform discrete Fourier transform, nuclear-norm, Seismic data Interpolation},
	presentation = {http://slim.gatech.edu/Publications/Public/Conferences/CMS/2016/lopez2016CMSogl/lopez2016CMSogl_pres.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Ozgur Yilmaz and Felix J. Herrmann}
}
@techreport {rajiv2012SEGFRM,
	title = {Fast methods for rank minimization with applications to seismic-data interpolation},
	number = {TR-EOAS-2012-3},
	year = {2012},
	month = {04},
	institution = {Department of Earth and Ocean Sciences},
	address = {University of British Columbia, Vancouver},
	abstract = {Rank penalizing techniques are an important direction in seismic inverse problems, since they allow improved recovery by exploiting low-rank structure. A major downside of current state of the art techniques is their reliance on the SVD of seismic data structures, which can be prohibitively expensive. Fortunately, recent work allows us to circumvent this problem by working with matrix factorizations. We review a novel approach to rank penalization, and successfully apply it to the seismic interpolation problem by exploiting the low-rank structure of seismic data in the midpoint-offset domain. Experiments for the recovery of 2D monochromatic data matrices and seismic lines represented as 3D volumes support the feasibility and potential of the new approach.},
	keywords = {Optimization, Rank, Seismic data Interpolation},
	url = {http://slim.gatech.edu/Publications/Public/TechReport/2012/rajiv2012SEGFRM/rajiv2012SEGFRM.pdf},
	author = {Rajiv Kumar and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
