@presentation {esser2013SINBADasr,
	title = {Applications of phase retrieval methods to blind seismic deconvolution},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Phase retrieval is the non-convex optimization problem of recovering a signal from magnitudes of complex linear measurements. Solving convex semi-definite program (SDP) relaxations has been shown to be a robust approach, but it remains too expensive to apply to large problems. We will discuss methods for accelerating computations and explore applications to seismic deconvolution.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/esser2013SINBADasr/esser2013SINBADasr.pdf},
	author = {Ernie Esser and Felix J. Herrmann}
}
@presentation {oghenekohwo2013SINBADedt,
	title = {Estimating 4D differences in time-lapse using randomized sampling techniques},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Repeatability in the seismic survey and processing has been cited as the main reason for which 4D seismic technology work. In the last decade, concerted efforts have been spent to make the 4D seismic process highly repeatable, without significant success. On the contrary, Compressed Sensing, which is also a relatively new sampling paradigm proposes that one can recover an estimate of a fully sampled signal from a noisy, under-sampled measurements provided the acquisition architecture satisfies some properties. By observing different under-sampled and random measurements from each vintage, corresponding to different acquisition geometries, we show that one can still detect the 4D change in time using recent ideas from Compressed sensing. Using a realistic synthetic model, we show two methods of estimating the 4D difference and compare their relative performance to each other.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/oghenekohwo2013SINBADedt/oghenekohwo2013SINBADedt.pdf},
	author = {Felix Oghenekohwo and Ernie Esser and Felix J. Herrmann}
}
@presentation {kumar2013SINBADwrs,
	title = {Wavefield reconstruction with SVD-free low-rank matrix factorization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {As shown in past, we can leverage the ideas from the field of compressed sensing to cast problems like seismic data interpolation or sequential shot data recovery from simultaneous data, as a compressed sensing problem. In this work we will show how we can borrow the same ideas of compressed sensing and cast these problems as matrix completion problems. Instead of sparsity we will show that we can exploit the low-rank structure of seismic data to solve these problems. One of the impediments in rank-minimization problem is the computation of singular values. We will also show how we can solve the rank minimization problems SVD-free. The practical application is divided into three parts: 1. In case of sequential seismic data acquisition, how jittered subsampling helps to recover the better quality data as compared to random subsampling. 2. How the incorporation of reciprocity principles help to enhance the quality of recovered fully sampled data. 3. How we can recover the sequential source data from simultaneous source data.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADwrs/kumar2013SINBADwrs.pdf},
	author = {Rajiv Kumar and Aleksandr Y. Aravkin and Hassan Mansour and Ernie Esser and Felix J. Herrmann}
}
