@techreport {sharan2020lsh,
	title = {Large scale high-frequency wavefield reconstruction with recursively weighted matrix factorizations},
	number = {TR-CSE-2020-4},
	year = {2020},
	month = {10},
	abstract = {Acquiring seismic data on a regular periodic fine grid is challenging. By exploiting the low-rank approximation property of fully sampled seismic data in some transform domain, low-rank matrix completion offers a scalable way to reconstruct seismic data on a regular periodic fine grid from coarsely randomly sampled data acquired in the field. While wavefield reconstruction have been applied successfully at the lower end of the spectrum, its performance deteriorates at the higher frequencies where the low-rank assumption no longer holds rendering this type of wavefield reconstruction ineffective in situations where high resolution images are desired. We overcome this shortcoming by exploiting similarities between adjacent frequency slices explicitly. During low-rank matrix factorization, these similarities translate to alignment of subspaces of the factors, a notion we propose to employ as we reconstruct monochromatic frequency slices recursively starting at the low frequencies. While this idea is relatively simple in its core, to turn this recent insight into a successful scalable wavefield reconstruction scheme for 3D seismic requires a number of important steps. First, we need to move the weighting matrices, which encapsulate the prior information from adjacent frequency slices, from the objective to the data misfit constraint. This move considerably improves the performance of the weighted low-rank matrix factorization on which our wavefield reconstructions is based. Secondly, we introduce approximations that allow us to decouple computations on a row-by-row and column-by-column basis, which in turn allow to parallelize the alternating optimization on which our low-rank factorization relies. The combination of weighting and decoupling leads to a computationally feasible full-azimuth wavefield reconstruction scheme that scales to industry-scale problem sizes. We demonstrate the performance of the proposed parallel algorithm on a 2D field data and on a 3D synthetic dataset. In both cases our approach produces high-fidelity broadband wavefield reconstructions from severely subsampled data.},
	keywords = {5D reconstruction, compressed sensing, frequency-domain, parallel, signal processing},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2020/sharan2020lsh/sharan2020lsh.html},
	author = {Shashin Sharan and Yijun Zhang and Oscar Lopez and Felix J. Herrmann}
}
@conference {zhang2020SEGwrw,
	title = {Wavefield recovery with limited-subspace weighted matrix factorizations},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2020},
	note = {(SEG, virtual)},
	month = {09},
	pages = {2858-2862},
	abstract = {Modern-day seismic imaging and monitoring technology increasingly rely on dense full-azimuth sampling. Unfortunately, the costs of acquiring densely sampled data rapidly become prohibitive and we need to look for ways to sparsely collect data, e.g. from sparsely distributed ocean bottom nodes, from which we then derive densely sampled surveys through the method of wavefield reconstruction. Because of their relatively cheap and simple calculations, wavefield reconstruction via matrix factorizations has proven to be a viable and scalable alternative to the more generally used transform-based methods. While this method is capable of processing all full azimuth data frequency by frequency slice, its performance degrades at higher frequencies because monochromatic data at these frequencies is not as well approximated by low-rank factorizations. We address this problem by proposing a recursive recovery technique, which involves weighted matrix factorizations where recovered wavefields at the lower frequencies serve as prior information for the recovery of the higher frequencies. To limit the adverse effects of potential overfitting, we propose a limited-subspace recursively weighted matrix factorization approach where the size of the row and column subspaces to construct the weight matrices is constrained. We apply our method to data collected from the Gulf of Suez, and our results show that our limited-subspace weighted recovery method significantly improves the recovery quality.},
	keywords = {algorithm, data reconstruction, frequency-domain, interpolation, Processing, SEG},
	doi = {10.1190/segam2020-3428365.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGwrw/zhang2020SEGwrw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGwrw/zhang2020SEGwrw_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGwrw/zhang2020SEGwrw_pres.mp4},
	software = {https://github.com/slimgroup/Software.SEG2020},
	author = {Yijun Zhang and Shashin Sharan and Oscar Lopez and Felix J. Herrmann}
}
