@presentation {wang2014SINBADdwi,
	title = {Denoising the wavefield inversion problem through source blending and penalty method},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Previous FWI denoising techniques, such as source blending and stacking, are designed applied to the seismic traces. Hence no physical information is used to improve the fidelity of the reconstruction, even when a good initial model is available. Moreover, traditional source blending requires the existence of common source gathers, which might be missing from the data. In this talk, we incorporate the source blending idea into the penalty method, and extract the synthetic sources corresponding to the principle directions of the gradient update. By blending the sources, we can not only achieve an acceleration and storage efficiency of the penalty method, but also make the reconstruction more robust to the random noise. This method is applicable to any source receiver position but is more efficient if common source gathers are available.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wang2014SINBADdwi/wang2014SINBADdwi.pdf},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@presentation {esser2014SINBADlcs,
	title = {A lifted $\ell_1$/$\ell_2$ constraint for sparse blind deconvolution},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We propose a modification to a sparsity constraint based on the ratio of l1 and l2 norms for solving blind seismic deconvolution problems in which the data consist of linear convolutions of different sparse reflectivities with the same source wavelet. No assumptions are made about the location of the support of either the wavelet or the sparse signals. Minimizing the ratio of l1 and l2 norms has been previously shown to promote sparsity in a variety of applications including blind deconvolution. Most existing implementations are heuristic or require smoothing the l1/l2 penalty. Lifted versions of l1/l2 constraints have also been proposed but are still challenging to implement. Inspired by the lifting approach, we propose to split the sparse signals into positive and negative components and apply an l1/l2 constraint to the difference, thereby obtaining a constraint that is easy to implement without smoothing the l1 or l2 norms. We show that a method of multipliers implementation of the resulting model can recover source wavelets that are not necessarily minimum phase and approximately reconstruct the sparse reflectivities. It appears to be robust to the initialization and to small amounts of noise in the data. We also discuss extensions to the Estimation of Primaries by Sparse Inversion (EPSI) convolution model.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/esser2014SINBADlcs/esser2014SINBADlcs.pdf},
	author = {Ernie Esser and Tim T.Y. Lin and Rongrong Wang and Felix J. Herrmann}
}
