@presentation {kumar2017SINBADFfas,
	title = {Full-azimuth seismic data processing w/ coil acquisition},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this work, we will demonstrate the performance of our in-house 5D low-rank based interpolation method on a seismic data acquired using coil shooting full-azimuth acquisition. We will show that we can recover full-azimuthal interpolated data from highly subsampled data, where the subsampling ratio is 4\%. This is the first time, we are testing our interpolation ideas on real 3D marine seismic data acquisition. Our findings show that we can avoid the general practice of windowing the data while performing the interpolation, specially using rank-minimization based framework.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFfas/kumar2017SINBADFfas.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFfas/kumar2017SINBADFfas.mov},
	author = {Rajiv Kumar and Nick Moldoveanu and Keegan Lensink and Felix J. Herrmann}
}
@presentation {lopez2017SINBADFmci,
	title = {Matrix Completion in Parallel Architectures: Julia Implementation},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Matrix completion techniques offer potential tools for frugal seismic data acquisition, where dense acquisition is replaced by optimization. This shift of focus means that efficient numerical methods are critical for the implementation of these techniques in large-scale seismic applications. To this end, this talk modifies rank-penalization methodologies to suit parallel architectures. By adopting factorization-based alternating minimization schemes, each program can be decoupled into independent sub-problems handled in parallel. We showcase a distributed parallel execution in Julia and explore the scalability of the approach.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFmci/lopez2017SINBADFmci.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFmci/lopez2017SINBADFmci.mov},
	author = {Oscar Lopez and Keegan Lensink and Rajiv Kumar and Henryk Modzelewski}
}
@presentation {macedo2013SINBADdap,
	title = {A dual approach to PhaseLift via gauge programming and bundle methods},
	year = {2013},
	publisher = {SINBAD},
	abstract = {A feature common to many sparse optimization problems is that the number of variables may be significantly larger than the number of constraints- e.g., the matrix-lifting approach taken by PhaseLift for phase retrieval results in a problem where the number of variables is quadratic in the number of constraints. We consider a duality framework and numerical methods to leverage the relatively small number of constraints. Preliminary numerical results illustrate our approach and its flexibility.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/macedo2013SINBADdap/macedo2013SINBADdap.pdf},
	author = {Ives Macedo and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {miao2013SINBADfid,
	title = {Fast imaging via depth stepping with the two-way wave equation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In this presentation we propose a fast imaging algorithm via depth stepping with the two-way wave equation. Within the framework of survey sinking, a stabilized depth extrapolation operator is computed using a spectral projector which can efficiently split evanescent wave components. The computation of the spectral projector features with an Hierarchically Semi-Seperable (HSS) matrix representation speeded up polynomial recursion, resulting in an accleration from cubic numerical complexity to linear numerical complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/miao2013SINBADfid/miao2013SINBADfid.pdf},
	author = {Lina Miao and Felix J. Herrmann}
}
@presentation {kumar2013SINBADhss,
	title = {Seismic data interpolation via low-rank matrix factorization in the hierarchical semi-separable representation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Recent developments in matrix rank optimization have allowed for new computational approaches in the field of seismic data interpolation. One of the main requirements of exploiting rank-minimization approaches is that the target data set should exhibit a low-rank structure. Seismic frequency slices exhibit low-rank structure at the low-frequencies, but not at the high frequencies. This behavior is due to the increase in oscillations as we move from low to high-frequency slices, even though the energy remains focused around the diagonal. Therefore, interpolation via rank minimization in the high-frequency range requires extended formulations that incorporate low-rank structure. We propose an approach for seismic data interpolation which incorporates the Hierarchical Semi-Separable Structure (HSS) inside rank-regularized least-squares formulations for the missing-trace interpolation problem. The proposed approach is suitable for large scale problems, since it avoids SVD computations and uses a low-rank factorized formulation instead. We illustrate the advantages of the new HSS approach by interpolating a seismic line from the Gulf of Suez and compare the reconstruction with conventional rank minimization.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/kumar2013SINBADhss/kumar2013SINBADhss.pdf},
	author = {Rajiv Kumar and Hassan Mansour and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@presentation {fang2013SINBADsfwi,
	title = {Swift FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In 3D case, there is much more data and modeling is much more expensive. As a result, parallel computing is very important for 3D full-waveform inversion. Both domain decomposition and data decomposition need a large number of parallel computing resources. However, programs based on parallel MATLAB suffer from the limitation of licenses. In order to obtain a MATLAB licenses free solution, we use SWIFT, which is a fast and easy parallel scripting language. Once the original Mat file is complied to an executable file, SWIFT can run the code inside the executable file in parallel without using parallel MATLAB. We use the SWIFT to compute the object functions and gradients of different shots in parallel, and test the parallel 3D FWI code with overthrust data. This is joint work with Thomas Lai, Harsh Juneja, Bas Peters.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADsfwi/fang2013SINBADsfwi.pdf},
	author = {Zhilong Fang and Henryk Modzelewski and Tristan van Leeuwen and Thomas Lai and Harsh Juneja and Bas Peters and Felix J. Herrmann}
}
@presentation {kumar2013SINBADwrs,
	title = {Wavefield reconstruction with SVD-free low-rank matrix factorization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {As shown in past, we can leverage the ideas from the field of compressed sensing to cast problems like seismic data interpolation or sequential shot data recovery from simultaneous data, as a compressed sensing problem. In this work we will show how we can borrow the same ideas of compressed sensing and cast these problems as matrix completion problems. Instead of sparsity we will show that we can exploit the low-rank structure of seismic data to solve these problems. One of the impediments in rank-minimization problem is the computation of singular values. We will also show how we can solve the rank minimization problems SVD-free. The practical application is divided into three parts: 1. In case of sequential seismic data acquisition, how jittered subsampling helps to recover the better quality data as compared to random subsampling. 2. How the incorporation of reciprocity principles help to enhance the quality of recovered fully sampled data. 3. How we can recover the sequential source data from simultaneous source data.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADwrs/kumar2013SINBADwrs.pdf},
	author = {Rajiv Kumar and Aleksandr Y. Aravkin and Hassan Mansour and Ernie Esser and Felix J. Herrmann}
}
@presentation {miao2012SINBADasp,
	title = {Accelerating on sparse promoting recovery and its benefits in seismic application},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Sparse promoting recovery problem arises more and more frequently with the broad application of compressed sensing tool in exploration seismology. Because of the curse of dimensionality, the prohibitive computation burden on iteratively evaluating objective functions is one of the key issues that constrain high performance l1 solver. In this paper, we try to further improve the convergence performance of SPGl1, one of the state-of-the-art large scale sparse recovery solver, and as a result limit the number of objective function evaluations by introducing a projected quasi Newton method. Examples showing acceleration on seismic data collection, data processing as well as inversion are included.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/miao2012SINBADasp/miao2012SINBADasp_pres.pdf},
	author = {Lina Miao}
}
@presentation {au-yeung2012SINBADcs,
	title = {Compressed sensing, random Fourier matrix and jitter sampling},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Compressed sensing is an emerging signal processing technique that allows signals to be sampled well below the Nyquist rate, when the signal has a sparse representation in an orthonormal basis. By using a random Fourier matrix or a Gaussian matrix as our measurement matrix, we can reconstruct a signal from far fewer measurements than required by Shannon sampling theorem. In this talk, we will discuss the role of uniform versus jitter sampling, both in a theoretical and practical viewpoint.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/au-yeung2012SINBADcs/au-yeung2012SINBADcs_pres.pdf},
	author = {Enrico Au-Yeung and Hassan Mansour and Ozgur Yilmaz}
}
@presentation {mansour2012SINBADsti,
	title = {Seismic trace interpolation via sparsity promoting reweighted algorithms},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Missing-trace interpolation aims to reconstruct regularly sampled wavefields from periodically sampled data with gaps caused by physical constraints. While transform-domain sparsity promotion has proven to be an effective tool to solve this recovery problem, current recovery techniques make no use of a priori information on the transform-domain coefficients. To overcome these vulnerabilities in solving the recovery problem for large-scale problems, we propose recovery by weighted one-norm minimization, which exploits correlations between locations of significant coefficients of different partitions, e.g., shot records, common-offset gathers, or frequency slices, of the acquired data. Moreover, in situations where no prior support estimate is available, we propose the WSPGL1 algorithm that outperforms standard $\ell_1$ minimization in finding sparse solutions to underdetermined linear systems of equations. Our algorithm is a modification of the SPGL1 algorithm and enjoys better sparse recovery performance at no additional computational cost. We illustrate the improved recovery using WSPGL1 for randomly subsampled seismic traces.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/mansour2012SINBADsti/mansour2012SINBADsti_pres.pdf},
	author = {Hassan Mansour}
}
@presentation {min2011SINBADpss,
	title = {Parameter-selection strategy for density in frequency-domain elastic waveform inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/min2011SINBADpss/min2011SINBADpss_pres.pdf},
	author = {Dong-Joo Min}
}
@presentation {mansour2011SINBADwcw,
	title = {Why do curvelets work?},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/mansour2011SINBADwcw/mansour2011SINBADwcw_pres.pdf},
	author = {Hassan Mansour}
}
@presentation {moghaddam2010SINBADrfwi,
	title = {Randomized full-waveform inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/moghaddam2010SINBADrfwi/moghaddam2010SINBADrfwi_pres.pdf},
	author = {Peyman P. Moghaddam}
}
@presentation {mansour2010SINBADrcs,
	title = {Recovering compressively sampled signals using partial support information},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/mansour2010SINBADrcs/mansour2010SINBADrcs_pres.pdf},
	author = {Hassan Mansour}
}
@presentation {modzelewski2010SINBADsra,
	title = {Software releases and architecture},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/modzelewski2010SINBADsra/modzelewski2010SINBADsra_pres.pdf},
	author = {Henryk Modzelewski}
}
@conference {modzelewski2008SINBADdas,
	title = {Design and specifications for SLIM{\textquoteright}s software framework},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {The SLIM group is actively developing software for seismic imaging. This talk will give a general overview of the software development during SINBAD project with focus on the final release in February 2008. The covered topics will include: 1) adopting Python for object-oriented programming, 2) including parallelism into the algorithms used in seismic imaging/modeling, 3) in-house algorithms for seismic imaging, and 4) contributions to Madagascar (RSF). The talk will serve as an introduction to the other presentations in the session "SINBAD Software releases".},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/modzelewski2008SINBADdas/modzelewski2008SINBADdas.pdf},
	author = {Henryk Modzelewski}
}
@conference {moghaddam2008SINBADrtm,
	title = {Reverse-time migration amplitude recovery with curvelets},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {We recover the amplitude of a seismic image by approximating the normal (demigration-migration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigenvectors. Subsequently, we propose an approximate nonlinear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time {\textquoteright}wave-equation{\textquoteright} migration code simulating the acoustic wave equation.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/moghaddam2008SINBADrtm/moghaddam2008SINBADrtm.pdf},
	author = {Peyman P. Moghaddam}
}
@conference {modzelewski2006SINBADdas,
	title = {Design and specifications for SLIM{\textquoteright}s software framework},
	booktitle = {SINBAD 2006},
	year = {2006},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/modzelewski2006SINBADdas/modzelewski2006SINBADdas.pdf},
	author = {Henryk Modzelewski}
}
@conference {moghaddam2006SINBADioa,
	title = {Imaging operator approximation using curvelets},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {In this presentation, the normal (demigation-migration) operator is studied in terms of a pseudo-differential operator. The invariance of curvelets under this operator and their sparsity on the seismic images is used to precondition the migration operator. A brief overview will be given on some of the theory from micro-local analysis which proofs that curvelets remain approximately invariant under the operator. The proper setting for which a diagonal approximation in the curvelet domain is accurate is discussed together with different methods that estimate this diagonal from of-the-shelf migration operators. This is joint work with Chris Stolk.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/moghaddam2006SINBADioa/moghaddam2006SINBADioa.pdf},
	author = {Peyman P. Moghaddam}
}
@conference {maysami2006SINBADrro,
	title = {Recent results on seismic deconvolution},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {One of the important steps in seismic imaging is to provide suitable information about boundaries. Sharp variation of physical properties at a layer boundary cause reflection the wavefield. In previous work done by C. M. Dupuis, seismic signal characterization is divided into two steps: detection and estimation. In the detection phase, the goal is to find all singularities in a seismic section regardless of their order and then to categorize the data to different events by windowing each singularity. In the estimation step, we determine the order of singularity more precisely by using a rough estimate based on the detection phase. Traditionally, a redundant dictionary method is employed for the detection part. However, we attempt to instead use a new L1-solver developed by D.L. Donoho: the Stagewise Orthogonal Matching Pursuit (StOMP). It approximates the solution to inverse problems while promoting the sparsity in the solution vector. This algorithm will allow us to experimentally confirm the recent analysis by S. Mallat on spiky deconvolution limits, which imposes a required minimum distance between spikes. This required minimum distance between different spikes is dependent on the number of spikes as well as the width of the chosen source wavelet used in convolution with the train. These results allow for the design of more robust and accurate detection schemes for seismic signal characterization.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/maysami06SINBADrro/maysami06SINBADrro_pres.pdf},
	author = {Mohammad Maysami}
}
@conference {moghaddam2006SINBADsac,
	title = {Sparsity- and continuity-promoting norms for seismic images},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation, the importance of sparsity and continuity enhancing energy norms is emphasized for seismic imaging and inversion. The continuity promoting energy norm is justified by the apparent smoothness of reflectors in the direction along and the oscillatory behavior across the interfaces. This energy norm is called anisotropic diffusion and will be defined mathematically. Denoising examples will be given during which seismic images are recovered from the noise by a joint norm-one and continuity promoting minimization.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/moghaddam2006SINBADsac/moghaddam2006SINBADsac.pdf},
	author = {Peyman P. Moghaddam}
}
