@mastersthesis {johnson2013THswr,
	title = {Seismic wavefield reconstruction using reciprocity},
	year = {2013},
	note = {(MSc)},
	month = {03},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {The primary focus of most reflection seismic surveys is to help locate hydrocarbon recourses. Due to an ever increasing scarcity of these recourses, we must increase the size and quality of our seismic surveys. However, processing such large seismic data volumes to accurately recover earth properties is a painstaking and computationally intensive process. Due to the way reflection seismic surveys are conducted there are often holes in the collected data, where traces are not recorded. This can be due to physical or cost constraints. For some of the initial stages of processing these missing traces are of little consequence. However processes like multiple prediction and removal, interferometric ground roll prediction, and migration require densely sampled data on a regular grid. Thus the need to interpolate undersampled data cannot be ignored. Using the fact that reflection seismic data sets obey a reciprocal relationship in source and receiver locations, combined with recent advances in the field of compressed sensing, we show that properly regularized the wavefield reconstruction problem can be solved with a high degree of accuracy. We exploit the compressible nature of seismic data in the curvelet domain to solve regularized l1 recovery problems that seek to match the measured data and enforce the above mentioned reciprocity. Using our method we were able to achieve results with a 20.45 dB signal to noise ratio when reconstructing a marine data set that had 50\% of its traces decimated. This is a 13.44 dB improvement over using the same method run without taking reciprocity into account.},
	keywords = {MSc, thesis},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2013/johnson2013THswr.pdf},
	author = {James Johnson}
}
@mastersthesis {jumah2012THdre,
	title = {Dimensionality-reduced estimation of primaries by sparse inversion},
	year = {2012},
	note = {(MSc)},
	month = {02},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {Data-driven methods{\textemdash}such as the estimation of primaries by sparse inversion suffer from the curse of dimensionality that 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 data matrix is approximated adaptively by a randomized low-rank factorization. Compared to conventional methods, which need passes through all the data possibly including on-the-fly interpolations for each iteration, our approach has the advantage that the passes is reduced to 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 formalism to synthetic and real data shows that significant performance improvements in speed and memory use are achievable at a low computational overhead required by the low-rank factorization.},
	keywords = {MSc, thesis},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2012/jumah2012THdre.pdf},
	author = {Bander Jumah}
}
