@conference {li2011CSEGefimag,
	title = {Efficient full-waveform inversion with marine acquisition geometry},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2012},
	month = {02},
	abstract = {Full-waveform inversion (FWI) is a nonlinear data fitting procedure based on seismic data to derive a accurate velocity model. With the increasing demand for high resolution images in complex geological settings, the importance of improvements in acquisition and inversion become more and more critical. However, these improvements will be obtained at high computational cost, as a typical marine survey contains thousands of shot and receiver positions, and FWI needs several passes through massive seismic data. Computational cost of FWI will grow exponentially as the size of seismic data and desired resolution increase. In this paper we present a modified Gauss-Newton (GN) method that borrows ideas from compressive sensing, where we compute the GN updates from a few randomly selected sequential shots. Each subproblem is solved by using a sparsity promoting algorithm. With this approach, we dramatically reduce the size and hence the computational costs of the problem, whilst we control information loss by redrawing a different set of sequential shots for each subproblem.},
	keywords = {CSEG},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2012/li2011CSEGefimag/li2011CSEGefimag.pdf},
	author = {Xiang Li and Felix J. Herrmann}
}
@conference {wason2012CSEGode,
	title = {Only dither: efficient simultaneous marine acquisition},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2012},
	month = {02},
	abstract = {Simultaneous-source acquisition is an emerging technology that is stimulating both geophysical research and commercial efforts. Simultaneous marine acquisition calls for the development of a new set of design principles and post-processing tools. The focus here is on simultaneous-source marine acquisition design and sparsity-promoting sequential-source data recovery. We propose a pragmatic simultaneous-source, randomized marine acquisition scheme where multiple vessels sail across an ocean-bottom array firing airguns at {\textendash}- sequential locations and randomly time-dithered instances. By leveraging established findings from the field of compressive sensing, where the choice of the sparsifying transform needs to be incoherent with the compressive sampling matrix, we can significantly impact the reconstruction quality, and demonstrate that the compressive sampling matrix resulting from the proposed sampling scheme is sufficiently incoherent with the curvelet transform to yield successful recovery by sparsity promotion. Results are illustrated with simulations of {\textquotedblleft}purely{\textquotedblright} random marine acquisition, which requires an airgun to be located at each source location, and random time-dithering marine acquisition with one and two source vessels. Size of the collected data volumes in all cases is the same. Compared to the recovery from the former acquisition scheme (SNR = 10.5dB), we get good results by dithering with only one source vessel (SNR = 8.06dB) in the latter scheme, which improve at the cost of having an additional source vessel (SNR = 9.85dB).},
	keywords = {Acquisition, CSEG, marine, simultaneous},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2012/wason2012CSEGode/wason2012CSEGode.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
