@conference {esser2015EAGElcs,
	title = {A lifted $\ell_1/\ell_2$ constraint for sparse blind deconvolution},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {We propose a modification to a sparsity constraint based on the ratio of $\ell_1$ and $\ell_2$ norms for solving blind seismic deconvolution problems in which the data consist of linear convolutions of different sparse reflectivities with the same source wavelet. We also extend the approach to the Estimation of Primaries by Sparse Inversion (EPSI) model, which includes surface related multiples. Minimizing the ratio of $\ell_1$ and $\ell_2$ 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 $\ell_1/\ell_2$ penalty. Lifted versions of $\ell_1/\ell_2$ constraints have also been proposed but are challenging to implement. Inspired by the lifting approach, we propose to split the sparse signals into positive and negative components and apply an $\ell_1/\ell_2$ constraint to the difference, thereby obtaining a constraint that is easy to implement without smoothing the $\ell_1$ or $\ell_2$ 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. Numerical experiments demonstrate robustness to the initialization as well as to noise in the data.},
	keywords = {blind deconvolution, EAGE, EPSI},
	doi = {10.3997/2214-4609.201413420},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/esser2015EAGElcs/esser2015EAGElcs.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/esser2015EAGElcs/esser2015EAGElcs_poster.pdf},
	author = {Ernie Esser and Tim T.Y. Lin and Rongrong Wang and Felix J. Herrmann}
}
@conference {lin2014EAGEmas,
	title = {Multilevel acceleration strategy for the robust estimation of primaries by sparse inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {We propose a method to substantially reduce the computational costs of the Robust Estimation of Primaries by Sparse Inversion algorithm, based on a multilevel inversion strategy that shifts early iterations of the method to successively coarser spatial sampling grids. This method requires no change in the core implementation of the original algorithm, and additionally only relies on trace decimation, low-pass filtering, and rudimentary interpolation techniques. We furthermore demonstrate with a synthetic seismic line significant computational speedups using this approach.},
	keywords = {EAGE, EPSI, multigrid, multilevel, multiples, multiscale, REPSI},
	doi = {10.3997/2214-4609.20140672},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/lin2014EAGEmas/lin2014EAGEmas.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/lin2014EAGEmas/lin2014EAGEmas_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {lin2013EAGEcsd,
	title = {Cosparse seismic data interpolation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2013},
	month = {06},
	abstract = {Many modern seismic data interpolation and redatuming algorithms rely on the promotion of transform-domain sparsity for high-quality results. Amongst the large diversity of methods and different ways of realizing sparse reconstruction lies a central question that often goes unaddressed: is it better for the transform-domain sparsity to be achieved through explicit construction of sparse representations (e.g., by thresholding of small transform-domain coefficients), or by demanding that the algorithm return physical signals which produces sparse coefficients when hit with the forward transform? Recent results show that the two approaches give rise to different solutions when the transform is redundant, and that the latter approach imposes a whole new class of constraints related to where the forward transform produces zero coefficients. From this framework, a new reconstruction algorithm is proposed which may allow better reconstruction from subsampled signaled than what the sparsity assumption alone would predict. In this work we apply the new framework and algorithm to the case of seismic data interpolation under the curvelet domain, and show that it admits better reconstruction than some existing L1 sparsity-based methods derived from compressive sensing for a range of subsampling factors.},
	keywords = {algorithm, cosparsity, curvelet, EAGE, interpolation, Optimization},
	doi = {10.3997/2214-4609.20130387},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/lin2013EAGEcsd/lin2013EAGEcsd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/lin2013EAGEcsd/lin2013EAGEcsd_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {lin2011EAGEepsic,
	title = {Estimating primaries by sparse inversion in a curvelet-like representation domain},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {We present an uplift in the fidelity and wavefront continuity of results obtained from the Estimation of Primaries by Sparse Inversion (EPSI) program by reconstructing the primary events in a hybrid wavelet-curvelet representation domain. EPSI is a multiple removal technique that belongs to the class of wavefield inversion methods, as an alternative to the traditional adaptive-subtraction process. The main assumption is that the correct primary events should be as sparsely-populated in time as possible. A convex reformulation of the original EPSI algorithm allows its convergence property to be preserved even when the solution wavefield is not formed in the physical domain. Since wavefronts and edge-type singularities are sparsely represented in the curvelet domain, sparse solutions formed in this domain will exhibit vastly improved continuity when compared to those formed in the physical domain, especially for the low-energy events at later arrival times. Further- more, a wavelet-type representation domain will preserve sparsity in the reflected events even if they originate from non-zero-order discontinuities in the subsurface, providing an additional level of robustness. This method does not require any changes in the underlying computational algorithm and does not explicitly impose continuity constraints on each update.},
	keywords = {EAGE, Presentation, Processing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/lin11EAGEepsic/lin11EAGEepsic.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/lin11EAGEepsic/lin11EAGEepsic_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50427},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {tu2011EAGEspmsrm,
	title = {Sparsity-promoting migration with surface-related multiples},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {Multiples, especially the surface-related multiples, form a significant part of the total up-going wave- field. If not properly dealt with, they can lead to false reflectors in the final image. So conventionally practitioners remove them prior to migration. Recently research has revealed that multiples can actually provide extra illumination so different methods are proposed to address the issue that how to use multiples in seismic imaging, but with various kinds of limitations. In this abstract, we combine primary estimation and sparsity-promoting migration into one convex-optimization process to include information from multiples. Synthetic examples show that multiples do make active contributions to seismic migration. Also by this combination, we can benefit from better recoveries of the Greens function by using sparsity-promoting algorithms since reflectivity is sparser than the Greens function.},
	keywords = {EAGE, Imaging, Presentation, Processing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/tu11EAGEspmsrm/tu11EAGEspmsrm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/tu11EAGEspmsrm/tu11EAGEspmsrm_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50369},
	author = {Ning Tu and Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {johnson2010EAGEeop,
	title = {Estimation of primaries via sparse inversion with reciprocity},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2010},
	abstract = {Accurate removal of surface related multiples is a key step in seismic data processing. The industry standard for removing multiples is SRME, which involves convolving the data with itself to predict the multiples, followed by an adaptive subtraction procedure to recover the primaries (Verschuur and Berkhout, 1997). Other methods involve multidimensional division of the up-going and down-going wavefields (Amundsen, 2001). However, this approach may suffer from stability problems. With the introduction of the {\textquoteleft}{\textquoteleft}estimation of primaries by sparse inversion{\textquoteright}{\textquoteright}(EPSI), van Groenestijn and Verschuur (2009) recentely reformulated SRME to jointly estimate the surface-free impulse response and the source signature directly from the data. The advantage of EPSI is that it recovers the primary response directly, and does not require a second processing step for the subtraction of estimated multiples from the original data. However, because it estimates both the primary impulse response and source signature from the data EPSI must be regularized. Motivated by recent successful application of the curvelet transform in seismic data processing (Herrmann et al., 2007), we formulate EPSI as a bi-convex optimization problem that seeks sparsity on the surface-free Green{\textquoteright}s function and Fourier-domain smoothness on the source wavelet. Our main contribution compared to previous work (Lin and Herrmann, 2009), and the contribution of that author to the proceedings of this meeting(Lin and Herrmann, 2010), is that we employ the physical principle of as source-receiver reciprocity to improve the inversion.},
	keywords = {EAGE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/johnson10EAGEeop/johnson10EAGEeop.pdf},
	author = {James Johnson and Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {lin2010EAGEseo,
	title = {Stabilized estimation of primaries via sparse inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2010},
	month = {06},
	abstract = {Estimation of Primaries by Sparse Inversion (EPSI) is a recent method for surface-related multiple removal using a direct estimation method closely related to Amundsen inversion, where under a sparsity assumption the primary impulse response is determined directly from a data-driven wavefield inversion process. One of the major difficulties in its practical adoption is that one must have precise knowledge of a time-window that contains multiple-free primaries during each update. Moreover, due to the nuances involved in regularizing the model impulse response in the inverse problem, the EPSI approach has an additional number of inversion parameters where it may be difficult to choose a reasonable value. We show that the specific sparsity constraint on the EPSI updates lead to an inherently intractable problem, and that the time-window and other inversion variables arise in the context of additional regularizations that attempts to drive towards a meaningful solution. We furthermore suggest a way to remove almost all of these parameters via convexification, which stabilizes the inversion while preserving the crucial sparsity assumption in the primary impulse response model.},
	keywords = {EAGE, Presentation, Processing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/lin10EAGEseo/lin10EAGEseo.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/lin10EAGEseo/lin10EAGEseo_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=39122},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@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 {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}
}
