@conference {herrmann2009EAGEcsa,
	title = {Compressive sensing applied to full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2009},
	month = {06},
	abstract = {With the recent resurgence of full-waveform inversion, the computational cost of solving forward modeling problems has become{\textendash}-aside from issues with non-uniqueness{\textendash}-one of the major impediments withstanding successful application of this technology to industry-size data volumes. To overcome this impediment, we argue that further improvements in this area will depend on a problem formulation with a computational complexity that is no longer strictly determined by the size of the discretization but by transform-domain sparsity of its solution. In this new paradigm, we bring computational costs in par with our ability to compress seismic data and images. This premise is related to two recent developments. First, there is the new field of compressive sensing (CS in short throughout the paper, Cand{\textquoteleft}es et al., 2006; Donoho, 2006){\textendash}-where the argument is made, and rigorously proven, that compressible signals can be recovered from severely sub-Nyquist sampling by solving a sparsity promoting program. Second, there is in the seismic community the recent resurgence of simultaneous-source acquisition (Beasley, 2008; Krohn and Neelamani, 2008; Herrmann et al., 2009; Berkhout, 2008; Neelamani et al., 2008), and continuing efforts to reduce the cost of seismic modeling, imaging, and inversion through phase encoding of simultaneous sources (Morton and Ober, 1998; Romero et al., 2000; Krohn and Neelamani, 2008; Herrmann et al., 2009), removal of subsets of angular frequencies (Sirgue and Pratt, 2004; Mulder and Plessix, 2004; Lin et al., 2008) or plane waves (Vigh and Starr, 2008). By using CS principles, we remove sub-sampling interferences asocciated with these approaches through a combination of exploiting transform-domain sparsity, properties of certain sub-sampling schemes, and the existence of sparsity promoting solvers.},
	keywords = {EAGE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/Herrmann09EAGEcsa/Herrmann09EAGEcsa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/Herrmann09EAGEcsa/Herrmann09EAGEcsa_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=23961},
	author = {Felix J. Herrmann and Yogi A. Erlangga and Tim T.Y. Lin}
}
@conference {erlangga2009EAGEmwi,
	title = {Migration with implicit solvers for the time-harmonic Helmholtz equation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2009},
	month = {06},
	abstract = {From the measured seismic data, the location and the amplitude of reflectors can be determined via a migration algorithm. Classically, following Claerbout{\textquoteright}s imaging principle [2], a reflector is located at the position where the source{\textquoteright}s forward-propagated wavefield correlates with the backward-propagated wavefield of the receiver data. Lailly and Tarantola later showed that this imaging principle is an instance of inverse problems, with the associated migration operator formulated via a least-squares functional; see [6, 12, 13]. Furthermore, they showed that the migrated image is associated with the gradient of this functional with respect to the image. If the solution of the least-squares functional is done iteratively, the correlation-based image coincides up to a constant with the first iteration of a gradient method. In practice, this migration is done either in the time domain or in the frequency domain. In the frequency-domain migration, the main bottleneck thus far, which renders its full implementation to large scale problems, is the lack of efficient solvers for computing wavefields. Robust direct methods easily run into excessive memory requirements as the size of the problem increases. On the other hand, iterative methods, which are less demanding in terms of memory, suffered from lack of convergence. During the past years, however, progress has been made in the development of an efficient iterative method [4, 3] for the frequency-domain wavefield computations. In this paper, we will show the significance of this method (called MKMG) in the context of the frequency-domain migration, where multi-shot-frequency wavefields (of order of 10,000 related wavefields) need to be computed.},
	keywords = {EAGE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/Erlangga09EAGEmwi/Erlangga09EAGEmwi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/Erlangga09EAGEmwi/Erlangga09EAGEmwi_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=23955},
	author = {Yogi A. Erlangga and Felix J. Herrmann}
}
