@article {jumah2014GPdre,
	title = {Dimensionality-reduced estimation of primaries by sparse inversion},
	journal = {Geophysical Prospecting},
	volume = {62},
	number = {5},
	year = {2014},
	note = {(Geophysical Prospecting)},
	month = {09},
	pages = {972-993},
	abstract = {Wave-equation based methods, such as the estimation of primaries by sparse inversion, have been successful in the mitigation of the adverse effects of surface-related multiples on seismic imaging and migration-velocity analysis. However, the reliance of these methods on multidimensional convolutions with fully sampled data exposes the {\textquoteleft}curse of dimensionality{\textquoteright}, which leads to disproportional growth in computational and storage demands when moving to realistic 3D field data. To remove this fundamental impediment, we propose a dimensionality-reduction technique where the {\textquoteleft}data matrix{\textquoteright} is approximated adaptively by a randomized low-rank factorization. Compared to conventional methods, which need for each iteration passage through all data possibly requiring on-the-fly interpolation, our randomized approach has the advantage that the total number of passes is reduced to only one to three. In addition, the low-rank matrix factorization leads to considerable reductions in storage and computational costs of the matrix multiplies required by the sparse inversion. Application of the proposed method to two-dimensional synthetic and real data shows that significant performance improvements in speed and memory use are achievable at a low computational up-front cost required by the low-rank factorization.},
	keywords = {factorization, primaries, sparse inversion},
	doi = {10.1111/1365-2478.12113},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/1365-2478.12113/abstract},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2014/jumah2014GPdre/jumah2014GPdre.pdf},
	author = {Bander Jumah and Felix J. Herrmann}
}
