@presentation {lin2014SINBADdag,
	title = {Dealing with acquisition gaps in Robust EPSI without interpolation using wavefield auto-convolution},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Straightforward modifications to the Estimation of Primaries by Sparse Inversion (EPSI) problem allows us to mitigate acquisition holes without any reconstruction of the missing traces, neither prior to nor during the inversion process. This is achieved by simulating the missing multiple contributions with terms involving auto-convolutions of the primary wavefield. In this formulation we no longer need to treat the missing data as another unknown in the inversion process, which is important in eliminating a significant source of possible local minima from attempting to invent data using incorrect primary and multiple models. In this talk we investigate the necessary modifications to the Robust EPSI problem, as well as algorithms that account for the resulting non-linear modelling operator. We will also investigate the reconstruction limits of this approach in relation to acquisition geometry and subsurface characteristics.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lin2014SINBADdag/lin2014SINBADdag.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {lin2013SEGdss,
	title = {Dense shot-sampling via time-jittered marine sources},
	booktitle = {SEG Workshop on Simultaneous Sources; Houston},
	year = {2013},
	month = {09},
	keywords = {Acquisition, SEG, simultaneous sources, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/lin2013SEGdss/lin2013SEGdss_pres.pdf},
	author = {Tim T.Y. Lin and Haneet Wason and Felix J. Herrmann}
}
@conference {lin2009EAGEdsa,
	title = {Designing simultaneous acquisitions with compressive sensing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2009},
	month = {06},
	abstract = {The goal of this paper is in designing a functional simultaneous acquisition scheme by applying the principles of compressive sensing. By framing the acquisition in a compressive sensing setting we immediately gain insight into not only how to choose the source signature and shot patterns, but also in how well we can hope to demultiplex the data when given a set amount of reduction in the number of sweeps. The principles of compressive sensing dictates that the quality of the demultiplexed data is closely related to the transform-domain sparsity of the solution. This means that, given an estimate in the complexity of the expectant data wavefield, it is possible to controllably reduce the number of shots that needs to be recorded in the field. We show a proof of concept by introducing an acquisition compatible with compressive sensing based on randomly phase-encoded vibroseis sweeps.},
	keywords = {EAGE, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/lin09EAGEdsa/lin09EAGEdsa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/lin09EAGEdsa/lin2009EAGEdsa_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
