@presentation {herrmann2017SINBADFhrc,
	title = {Highly repeatable 3D compressive full-azimuth towed-streamer time-lapse acquisition {\textendash}- a numerical feasibility study at scale},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Most conventional 3D time-lapse (or 4D) acquisitions are ocean-bottom cable (OBC) or ocean-bottom node (OBN) surveys since these surveys are relatively easy to replicate compared to towed-streamer surveys. To attain high degrees of repeatability, survey replicability and dense periodic sampling has become the norm for 4D surveys that renders this technology expensive. Conventional towed-streamer acquisitions suffer from limited illumination of subsurface due to narrow azimuth. Although, acquisition techniques such as multi-azimuth, wide-azimuth, rich-azimuth acquisition, etc., have been developed to illuminate the subsurface from all possible angles, these techniques can be prohibitively expensive for densely sampled surveys. This leads to uneven sampling, i.e., dense receiver and coarse source sampling or vice-versa, in order to make these acquisitions more affordable. Motivated by the design principles of Compressive Sensing (CS), we acquire economic, randomly subsampled (or compressive) and simultaneous towed-streamer time-lapse data without the need of replicating the surveys. We recover densely sampled time-lapse data on one and the same periodic grid by using a joint-recovery model (JRM) that exploits shared information among different time-lapse recordings, coupled with a computationally cheap and scalable rank-minimization technique. The acquisition is low cost since we have subsampled measurements (about 70\% subsampled), simulated with a simultaneous long-offset acquisition configuration of two source vessels travelling across a survey area at random azimuths. We analyze the performance of our proposed compressive acquisition and subsequent recovery strategy by conducting a synthetic, at scale, seismic experiment on a 3D time-lapse model containing geological features such as channel systems, dipping and faulted beds, unconformities and a gas cloud. Our findings indicate that the insistence on replicability between surveys and the need for OBC/OBN 4D surveys can, perhaps, be relaxed. Moreover, this is a natural next step beyond the successful CS acquisition examples discussed during this session.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFhrc/herrmann2017SINBADFhrc.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFhrc/herrmann2017SINBADFhrc.mov},
	author = {Felix J. Herrmann and Rajiv Kumar and Haneet Wason and Shashin Sharan and Felix Oghenekohwo}
}
@presentation {witte2017SINBADFals,
	title = {A large-scale framework in Julia for fast prototyping of seismic inversion algorithms},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present our progress on a large-scale seismic modeling workflow in Julia for wave-equation based inversion. The software offers a range of high-level abstractions to easily express PDE constrained optimization problems in terms of linear algebra expressions, while utilizing the DSL Devito to symbolically express the underlying PDEs and to generate fast and parallel code for solving them. Data containers and linear operators can be set up without much effort from input SEG-Y data and scale to large-scale 3D applications. This talk provides an overview of the basic functionalities of our software and applications to least squares imaging and 3D FWI.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/witte2017SINBADFals/witte2017SINBADFals.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/witte2017SINBADFals/witte2017SINBADFals.mov},
	author = {Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {louboutin2017SINBADFldi,
	title = {Latest developments in Devito},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present an overview of the latest developments in Devito. We introduced Devito in the previous meeting as a prototype finite-difference DSL for seismic modelling and inversion. We are presenting here the latest improvements and functionalities of Devito. We will also discuss the current future plans as well as non-supported features that the audience may be interested in. This presentation will be followed by/mixed with a hands-in tutorial if the time and resources allows it.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFldi/louboutin2017SINBADFldi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFldi/louboutin2017SINBADFldi.mov},
	author = {Mathias Louboutin and Michael Lange and Fabio Luporini and Navjot Kurjeka and Jan Hueckelheim and Gerard Gorman and Philipp A. Witte and Felix J. Herrmann}
}
@presentation {wang2017SINBADFnra,
	title = {Noise robust and time-domain formulations of Wavefield Reconstruction Inversion},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We propose a wave-equation-based subsurface inversion method that in many cases is more robust than conventional Full-Waveform Inversion. The new formulation is written in a denoising form that allows the synthetic data to match the observed ones up to a small error. Compared to regular Full-Waveform Inversion, our method treats the noise arising from the data meassuring/recording process and that from the synthetic modelling process separately. Compared to Wavefields Reconstruction Inversion, the new formulation mitigates the difficulty of choosing the penalty parameter λ. To solve the proposed optimization problem, we develop an efficient frequency domain algorithm that alternatively updates the model and the data. Numerical experiments confirm strong stability of the proposed method by comparisons between the results of our algorithm with that from both plain FWI and a weighted formulation of the FWI. We also discuss a new memory efficient time-domain formulation for Wavefield Reconstruction Inversion based on duality.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/wang2017SINBADFnra/wang2017SINBADFnra.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/wang2017SINBADFnra/wang2017SINBADFnra.mov},
	author = {Rongrong Wang and Mathias Louboutin and Bas Peters and Emmanouil Daskalakis and Felix J. Herrmann}
}
@presentation {sharan2017SINBADFtts,
	title = {Tracking the spatial-temporal evolution of fractures by microseismic source collocation},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Unlike conventional reservoirs, unconventional plays are not naturally viable for economical production of oil and gas. They require stimulation by injecting high-pressure fluid causing fractures in the rocks. These fractures make the medium more permeable, hence, the extraction of oil and gas becomes feasible. For drilling purposes and to prevent potentially hazardous situations, we need to have good knowledge of the location of these fractures. Also, we need to have good knowledge about how these fractures originated in time. Hydraulic fracturing changes stress in rocks, which results in the emission of microseismic waves. The opening of cracks due to high pressure fluid injection during hydraulic fracturing mainly causes this change in stress in the rocks. Therefore, microseismic events are mostly localized along these fractures and have finite energy along time. To accurately track the evolution of fractures in both space and time, we need to locate closely spaced microseismic events along these fractures activating at very small time intervals. A naive approach can be the back propagation of the observed data to find out a point in space and time where maximum focusing of back propagating energy occurs. This point corresponds to the location and origin time of a microseismic source. This approach, although simpler, suffers from low resolution and requires scanning of complete 4D volume (3D in space and 1D in time). Hence, this method can be challenging when there are multiple closely spaced microseismic sources originating at different times. We in this work propose a sparsity promotion based method that can locate closely spaced microseismic events, with spatial separation as low as within half a wavelength, activating at small time intervals. We simultaneously estimate the origin time of microseismic events by estimating their source time functions. Our method exploits the fact that microseismic events are localized in space and have finite energy. We use accelerated Linearized Bregman algorithm with a preconditioning operator to arrive at a computationally feasible scheme.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/sharan2017SINBADFtts/sharan2017SINBADFtts.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/sharan2017SINBADFtts/sharan2017SINBADFtts.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
@presentation {oghenekohwo2016SINBADFast,
	title = {Asymmetric sampling for time-lapse surveys},
	year = {2016},
	publisher = {SINBAD},
	abstract = {In this talk, we use the joint recovery model (JRM), which derives from distributed compressed sensing, to reconstruct time-lapse data acquired via time-jittered marine sources that are significantly subsampled in the monitor survey(s) in comparison to the baseline survey, hence, saving acquisition cost. We analyze the effects of two or more asymmetrically subsampled monitor surveys on wavefield reconstruction via JRM. We leverage the common information shared amongst the vintages during our sparsity-promoting recovery.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/oghenekohwo2016SINBADFast/oghenekohwo2016SINBADFast.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/oghenekohwo2016SINBADFast/oghenekohwo2016SINBADFast.mov},
	author = {Felix Oghenekohwo and Haneet Wason and Felix J. Herrmann}
}
@presentation {kumar2016SINBADFels,
	title = {Efficient large-scale 5D seismic data acquisition and processing using rank-minimization},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Seismic data collection is becoming challenging because of increased demands for high-quality, long-offset and wide-azimuth data. Leveraging ideas from CS, in this work we establish a cost effective acquisition and processing techniques, which are no longer dominated by survey area size but by the sparsity of seismic data volumes. In the first part of abstract, we establish connections between random time dithering and jittered sampling in space. Specifically, we recover high-quality 5D seismic data volumes from time-jittered marine acquisition where the average inter-shot time is significantly reduced, leading to cheaper surveys due to fewer overlapping shots. The time-jittered acquisition, in conjunction with the shot separation by Singular-Value Decomposition (SVD)-free factorization based rank-minimization approach, allows us to recover high quality 5D seismic data volumes. Results are illustrated for simulations of simultaneous time-jittered continuous recording for a 3D ocean-bottom cable survey, where we outperforms existing techniques, by an order of magnitude computational speedup and using 1/20th of the memory, that use sparsity in transforms domains. The second part of abstract focussed on leveraging low-rank structure in seismic data to solve extremely large data recovery (interpolation) problems. We introduced a large{\textendash}scale SVD-free optimization framework that is robust with respect to outliers and that uses information on the support. We test the efficacy of the proposed interpolation framework on a large-scale 5D seismic data, generated from the geologically complex synthetic 3D Compass velocity model, where 80\% of the data has been removed. Our findings show that major computational and memory gains are possible compared to curvelet-based reconstruction.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFels/kumar2016SINBADFels.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFels/kumar2016SINBADFels.mov},
	author = {Rajiv Kumar and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}
@presentation {sharan2016SINBADFhrm,
	title = {High resolution microseismic source collocation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Microseismic waves generated by hydraulic fracturing are used to locate the fractures created in the reservoir. Most of the existing source collocation methods have their limitations in terms of resolution of very closely spaced microseismic events. In this work we propose a method to collocate microseismic events with high resolution along with simultaneous estimation of source time function. In the context of waves, the finite-difference modelling operator acts as an analysis operator for wavefields. This implies that wavefields under the action of a finite-difference modelling kernel can be focused to sources generating these wavefields. We exploit this property of wavefields to locate microseismic events and estimate the source time function jointly. To arrive at a computational feasible scheme, we use the accelerated version of Linearized Bregman (LBR) algorithm to solve the above mentioned problem. This is joint work with Rongrong Wang.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/sharan2016SINBADFhrm/sharan2016SINBADFhrm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/sharan2016SINBADFhrm/sharan2016SINBADFhrm.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
@presentation {witte2016SINBADFlst,
	title = {A large-scale time-domain modeling and inversion workflow in Julia},
	year = {2016},
	publisher = {SINBAD},
	abstract = {We present our initial steps towards the development of a large-scale seismic modeling workflow in Julia which provides a framework for wave-equation based inversion methods like full waveform inversion or least squares migration. Our framework is based on the Devito tool for optimized finite difference computations that generates highly optimized code from symbolic PDEs. We aim at developing a flexible workflow which is based on abstract operators and allows coding that is {\textquotedblleft}close to the math{\textquotedblright}, while at the same time relying on extremely fast and efficient, state of the art wave equation solvers.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFlst/witte2016SINBADFlst.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFlst/witte2016SINBADFlst.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {wason2016SINBADFlcr,
	title = {Low-cost, randomized 3D towed-marine time-lapse seismic acquisition},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Most often conventional 3D towed-marine seismic data acquisition suffers from limited illumination of subsurface due to narrow azimuth. Although, acquisition techniques such as Ocean Bottom Cable (OBC), Ocean Bottom Node (OBN), Rich-Azimuth (RAZ) acquisition, Multi-Azimuth (MAZ) acquisition have been developed to illuminate the subsurface from all possible angles, these acquisition techniques are expensive. Motivated by the design principles of compressed sensing, we acquire randomly subsampled and simultaneous (e.g., simultaneous long offset) 3D towed-marine time-lapse seismic data for few (random) azimuths, and recover densely sampled multiple azimuth baseline and monitor data via sparsity promotion and rank minimization. Our acquisition is low cost since we have subsampled measurements.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/wason2016SINBADFlcr.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/marine_acq_mask.pdf},
	author = {Rajiv Kumar and Felix Oghenekohwo and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}
@presentation {witte2016SINBADFpve,
	title = {Phase velocity error minimizing scheme for the anisotropic pure P-wave equation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Pure P-wave equations for acoustic modeling in transverse isotropic media are derived by approximating the exact pure P-wave dispersion relation. In this work, we present an alternative to the common Taylor expansion based equations, in which we approximate the exact dispersion relation through a polynomial expansion and determine its coefficients by solving a least squares problem that minimizes the phase velocity error over the complete range of phase angles. We show that this approach is up to an order of magnitude more accurate than other pure P-wave equations at a comparable computational cost.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFpve/witte2016SINBADFpve.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFpve/witte2016SINBADFpve.mov},
	author = {Philipp A. Witte and Christiaan C. Stolk and Felix J. Herrmann}
}
@presentation {witte2016SINBADFtdl,
	title = {Time-domain least-squares RTM with sparsity promotion on field data},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Least squares reverse time migration (LSRTM) is an inversion based imaging algorithm which can surpress acquisition related artifacts and provides images with increased frequency content, decreased noise level and balanced amplitudes. In this work demonstrate our time-domain LSRTM workflow using various pre-conditioners, source-subsampling and sparsity promotion and show its application to the 2D Machar field data set.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFtdl/witte2016SINBADFtdl.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFtdl/witte2016SINBADFtdl.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {yang2016SINBADFtds,
	title = {Time-domain sparsity-promoting least-squares migration with source estimation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Compared to traditional reverse-time migration (RTM), least-squares RTM (LS_RTM) is able to obtain true amplitude images as solutions of $\ell_2$-norm minimization problems by fitting the synthetic and observed reflection data. The shortcoming is that solutions of these $\ell_2$ problems tend to be overfitted and computationally too expensive. By working with randomized subsets of data only, the computational costs of LS-RTM can be brought down to an acceptable level, producing artifact-free high-resolution images. By including on-the-fly source-time function estimation into the method of Linearized Bregman (LB), we tackle the open issues of these "compressive imaging" in the aspects of algorithmic complexity of solver, guaranteed convergence and source estimation. We also inbvestigate whether the algorithm can be accelerated when we combine LB with the Nesterov method. Application of our algorithm on a 2D synthetic shows that we are able to get high-resolution images, with accurate estimates of the wavelet, for one single data pass.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/yang2016SINBADFtds/yang2016SINBADFtds.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/yang2016SINBADFtds/yang2016SINBADFtds.m4v},
	author = {Mengmeng Yang and Philipp A. Witte and Zhilong Fang and Felix J. Herrmann}
}
@presentation {wang2016SINBADFtmf,
	title = {Two methods for frequency down-extrapolation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {It is well known that low frequency data are essential to the success of the Full-Waveform Inversion. Field data from a usual marine acquisition, however, typically lack reliable low frequencies due to the existing physical limitations. One research direction focuses on studying low-frequency extrapolation, the process of extending reliable frequency bands of the raw data towards the lower end of the spectrum. We propose two optimization problems, by solving which low frequencies can be made out of the high SNR high frequency component. We present both theoretical explanation and numerical experiments to demonstrate the efficacy as well as limitation of these methods.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wang2016SINBADFtmf/wang2016SINBADFtmf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wang2016SINBADFtmf/wang2016SINBADFtmf.mov},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@presentation {witte2015SINBADFara,
	title = {Anisotropic RTM applied to field data},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk we present our latest reverse time-migration results of the BP Machar data set, a 2D seismic line from the North sea featuring a large chalk dome. In contrast to our prior imaging attempts, we now account for anisotropic effects in the data using our new TTI pure quasi-P wave modeling code in the time domain.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFara/witte2015SINBADFara.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFara/witte2015SINBADFara.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {wang2015SINBADFifw,
	title = {Improving full waveform inversion with spectral extrapolation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We develop a sparsity based frequency extrapolation method to obtain low frequency data from the high frequency ones. As other frequency extrapolation methods, the extrapolation introduces noise due to dispersion and possibly densely distributed reflectors. To mitigate such noise, we incorporate Curvlet coefficient regularization in the extrapolation algorithm. Numerical results demonstrate the effectiveness of the extrapolation in frequency domain FWI in models of moderate complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wang2015SINBADFifw/wang2015SINBADFifw.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wang2015SINBADFifw/wang2015SINBADFifw.mov},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@presentation {wason2015SINBADldr,
	title = {Latest developments in randomized marine acquisition and source separation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present the latest developments in randomized (simultaneous) marine acquisition for time-lapse surveys. We also show a comparison between two source separation algorithms {\textemdash} sparsity-promotion and rank-minimization {\textemdash} for simultaneous data.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/wason2015SINBADldr/wason2015SINBADldr.pdf},
	author = {Haneet Wason and Rajiv Kumar and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {wason2015SINBADFrtl,
	title = {Randomization of time-lapse marine surveys},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present an extension of our time-jittered (simultaneous) marine acquisition for time-lapse surveys, where we work on more realistic field acquisition scenarios by incorporating irregular spatial grids without insisting on repeatability between the surveys. Since we are always subsampled in both the baseline and monitor surveys, we recover the densely sampled baseline and monitor and then the (complete) 4-D difference from subsampled baseline and monitor data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFrtl/wason2015SINBADFrtl.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFrtl/wason2015SINBADFrtl.mov},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {oghenekohwo2015SINBADrdc,
	title = {Recent developments in compressive sensing for time-lapse studies},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present an overview of the advances we have made with respect to extending compressive sensing ideas to time-lapse studies. We show applications in compressive time-lapse seismic data acquisition, imaging and full-waveform inversion.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/oghenekohwo2015SINBADrdc/oghenekohwo2015SINBADrdc.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason and Ernie Esser and Ning Tu and Felix J. Herrmann}
}
@presentation {esser2015SINBADFrsa,
	title = {Resolving scaling ambiguities with the L1/L2 norm in a blind deconvolution problem with feedback},
	year = {2015},
	publisher = {SINBAD},
	abstract = {Compared to more mundane blind deconvolution problems, blind deconvolution in seismic applications involves a feedback mechanism related to the free surface. The presence of this feedback mechanism gives us an unique opportunity to remove ambiguities that have plagued blind deconvolution for a long time. While beneficial, this feedback by itself is insufficient to remove the ambiguities even with L1 constraints. However, when paired with an L1/L2 constraint the feedback allows us to resolve the scaling ambiguity under relatively mild assumptions. Inspired by lifting approaches, we propose to split the sparse signal 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. Numerical experiments demonstrate robustness to the initialization as well as to noise in the data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFrsa/esser2015SINBADFrsa.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFrsa/esser2015SINBADFrsa.mov},
	author = {Ernie Esser and Rongrong Wang and Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {dasilva2015SINBADFsss,
	title = {Scaling SINBAD software to 3-D on Yemoja},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present early results on the scalability of SINBAD{\textquoteright}s wavefield reconstruction and wave-equation based inversion technologies on Yemoja, a 17k core cluster made available to us by BG Group at SENAI CIMATEC Supercomputing Centre in Brazil.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFsss/dasilva2015SINBADFsss.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFsss/dasilva2015SINBADFsss.mov},
	author = {Curt Da Silva and Haneet Wason and Mathias Louboutin and Bas Peters and Shashin Sharan and Zhilong Fang and Felix J. Herrmann}
}
@presentation {sharan2015SINBADFscu,
	title = {Source collocation using the method of Linearized Bregman},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this work, we present a method to collocate sources from seismic reflections recorded at receiver locations. We use the method of linearized Bregman. This algorithm focuses unknown sources by promoting sparsity in the {\textquotedblleft}Helmholtz domain{\textquotedblright}{\textemdash}i.e., the wavefield under the action of the the Helmholtz system{\textemdash}under the constraint of a data misfit within a particular tolerance. We extend this method to noisy measurements. We are in particular interested in situations where the noise is coherent because it is in the range of the Helmholtz equation.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/sharan2015SINBADFscu/sharan2015SINBADFscu.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/sharan2015SINBADFscu/sharan2015SINBADFscu.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
@presentation {wason2015SINBADFsss,
	title = {Source separation for simultaneous towed-streamer acquisition via compressed sensing},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We show a comparison between two compressed-sensing based source-separation algorithms, i.e., sparsity promotion and rank minimization, for simultaneous data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFsss/wason2015SINBADFsss.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFsss/wason2015SINBADFsss.mov},
	author = {Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {witte2015SINBADFtdf,
	title = {Time-domain FWI in TTI media},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We develop an inversion workflow for tilted transverse isotropic (TTI) media using a purely acoustic formulation of the wave equation. The anisotropic modeling kernel is used for the forward modeling operator, as well as for the adjoint Jacobian to back propagate the data residual, thus providing the true gradient of the FWI objective function. We apply this workflow on a synthetic FWI example and perform RTM on a field data set. Furthermore, we discuss alternatives for the pseudo-spectral Laplacian operator of the current implementation.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFtdf/witte2015SINBADFtdf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFtdf/witte2015SINBADFtdf.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {lopez2015SINBADFumc,
	title = {Universal matrix completion: applications to seismic data acquisition},
	year = {2015},
	publisher = {SINBAD},
	abstract = {This talk discusses the potential applications of universal matrix completion for infill management and acquisition design. We consider recent developments in the theory of matrix completion that produce practical tools to quantify how successful a given sub sampling mask will be for seismic trace interpolation via nuclear norm minimization. This approach provides an on the fly infill management system as well as instruments to design acquisition schemes that favor interpolation via rank penalization techniques.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lopez2015SINBADFumc/lopez2015SINBADFumc.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lopez2015SINBADFumc/lopez2015SINBADFumc.mov},
	author = {Oscar Lopez and Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {warner2014SINBADawi,
	title = {Adaptive waveform inversion - FWI without cycle skipping},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Conventional FWI minimises the direct differences between observed and predicted seismic datasets. Because seismic data are oscillatory, this approach will suffer from the detrimental effects of cycle skipping if the starting model is inaccurate. We reformulate FWI so that it instead adapts the predicted data to the observed data using Wiener filters, and then iterates to improve the model by forcing the Wiener filters towards zero-lag delta functions. This adaptive FWI scheme is demonstrated on synthetic data where it is shown to be immune to cycle skipping, and is able to invert successfully data for which conventional FWI fails entirely. The new method does not require low frequencies or a highly accurate starting model to be successful. Adaptive FWI has some features in common with wave-equation migration velocity analysis, but it works for all types of arrivals including multiples and refractions, and it does not have the high computational costs of WEMVA in 3D.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/warner2014SINBADawi/warner2014SINBADawi.pdf},
	author = {Mike Warner}
}
@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}
}
@presentation {wason2014SINBADrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will show how randomization techniques from (distributed) compressed sensing affect the way we think about time-lapse surveys.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wason2014SINBADrrt/wason2014SINBADrrt.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {oghenekohwo2014SINBADrsw,
	title = {Randomized sampling without repetition in time-lapse seismic surveys},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk, we will show a method for acquiring time-lapse data, where we do not have to repeat the survey geometry. Our method works provided our acquisition is randomized, where sources and receivers are at random locations on a computational grid, and provided we know the spatial locations of the shots and receivers. In addition, we show the implications of either (sub)sampling more for the baseline and less for the monitor or vice-versa, and how this sampling scheme affects the 4-D signal.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/oghenekohwo2014SINBADrsw/oghenekohwo2014SINBADrsw.pdf},
	author = {Felix Oghenekohwo and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@presentation {oghenekohwo2014SINBADrst,
	title = {Randomized subsampling in time-lapse surveys and recovery techniques},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk centres on new insights on how we think of acquisition of time-lapse data using ideas from compressed sensing. Specifically, we will show how simultaneous processing of time-lapse data from randomized subsampling gives more reliable estimates of the vintages and time-lapse signal, compared to parallel processing.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/oghenekohwo2014SINBADrst/oghenekohwo2014SINBADrst.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@presentation {wason2014SINBADsss,
	title = {Source separation via SVD-free rank-minimization in the hierarchical semi-separable representation},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will show a source separation algorithm for blended marine acquisition, where two sources are deployed at different depths (over/under acquisition). The separation method incorporates the Hierarchical Semi-Separable (HSS) structure inside rank-regularized least-squares formulations. We also compare this deblending scheme with the sparsity-promoting one-norm minimization scheme.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wason2014SINBADsss/wason2014SINBADsss.pdf},
	author = {Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {kumar2014SINBADtjm,
	title = {Time-jittered marine acquisition: low-rank v/s sparsity},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this work we will show how simultaneous or blended acquisition can be setup as a rank-minimization (deblending) problem. The simultaneous acquisition scenario is a pragmatic time-jittered marine acquisition scheme, where a source vessel (with two airgun arrays) sails across an ocean-bottom array firing at jittered source locations and instances in time, resulting in better spatial sampling and acquisition speedup. We make comparisons with sparsity-promoting based techniques and demonstrate that rank-minimization based debelending techniques are computationally faster and memory efficient. This is joint work with Haneet Wason.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/kumar2014SINBADtjm/kumar2014SINBADtjm.pdf},
	author = {Rajiv Kumar and Haneet Wason and Felix J. Herrmann}
}
@presentation {li2013SINBADgom,
	title = {Lessons learned from Chevron Gulf of Mexico data set},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Chevron Gulf of Mexico data set is very challenging for FWI because of elastic phases, limited offset, lack of low frequencies and salt structure. To overcome these issue, we first use ray-based tomography on the hand-picked first breaks to generate initial model for FWI, and then we apply curvelet-denosing techniques to improve the poor signal-to-noise ratio of the observed data at low frequencies. Finally, Curvelet domain sparsity promoting Gauss-Newton FWI helps to suppress model space artifacts caused by elastic phases. This is joint work with Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Brendan R. Smithyman, Haneet Wason and Felix J. Herrmann},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/li2013SINBADgom/li2013SINBADgom.pdf},
	author = {Xiang Li and Andrew J. Calvert and Ian Hanlon and Mostafa Javanmehri and Rajiv Kumar and Tristan van Leeuwen and Brendan R. Smithyman and Haneet Wason and Felix J. Herrmann}
}
@presentation {wang2013SINBADnru,
	title = {Noise reduction by using interferometric measurements},
	year = {2013},
	publisher = {SINBAD},
	abstract = {The interferometric formulation of linear wave-based inversion problem was proposed by Demanet and Jugnon recently. Instead of directly fitting the data, they proposed to fit a subset of the data{\textquoteright}s cross-correlation. It can be verified that if the full cross-correlation is used, then the problem is equivalent to the usual least square problem. The subsampling, which is usually considered to cause instability to the solution, is surprisingly useful in this setting. Numerical experiments for the inverse source problem and the inverse scatting problem have both suggested that a {\textquoteright}good{\textquoteright} sampling strategy can actually increase the stability under modeling error caused by the uncertainty of a kinetic nature. We will study the mathematical mechanism behind this phenomenon, and try to see whether or not there exists a universally {\textquoteright}good{\textquoteright} sampling strategy independent of the types of forward operators we use.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/wang2013SINBADnru/wang2013SINBADnru.pdf},
	author = {Rongrong Wang and Bas Peters and Felix J. Herrmann}
}
@presentation {warner2013SINBADrfwi,
	title = {Reflection FWI with a poor starting model},
	year = {2013},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/warner2013SINBADrfwi/warner2013SINBADrfwi.pdf},
	author = {Mike Warner}
}
@presentation {wason2013SINBADtjm,
	title = {Time-jittered marine sources},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Current efforts towards dense shot (and/or receiver) sampling and full azimuthal coverage to produce higher-resolution images have led to the deployment of multiple source vessels across the survey area. A step ahead from multi-source seismic acquisition is simultaneous or blended acquisition where different source arrays/vessels fire shots at near-simultaneous or slightly random times. Seismic data acquisition with simultaneous (or blended) sources has helped improve acquisition efficiency and mitigate acquisition related costs. Deblending then aims to recover unblended data, as acquired during conventional acquisition, from blended data since many processing techniques rely on full, regular sampling. We present a simultaneous/blended marine acquisition setup where shots fire at significantly jittered instances in time resulting in jittered shot locations for a given speed of the source vessel. The conventional, unblended data is recovered from the blended, jittered/irregular data by sparsity-promoting inversion using the non-equispaced fast discrete curvelet transform. The optimization scheme aims to deblend the blended data along with regularization and interpolation to a (finer) regular grid.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/wason2013SINBADtjm/wason2013SINBADtjm.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
@presentation {wason2013SINBADtjo,
	title = {Time-jittered ocean bottom seismic acquisition},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Leveraging ideas from the field of compressed sensing, we show how simultaneous or blended acquisition can be setup as a {\textendash} compressed sensing problem. This helps us to design a pragmatic time-jittered marine acquisition scheme where multiple source vessels sail across an ocean-bottom array firing airguns at {\textendash} jittered source locations and instances in time, resulting in better spatial sampling, and speedup acquisition. Furthermore, we can significantly impact the reconstruction quality of conventional seismic data (from jittered data) and demonstrate successful recovery by sparsity promotion. In contrast to random (under)sampling, acquisition via jittered (under)sampling helps in controlling the maximum gap size, which is a practical requirement of wavefield reconstruction with localized sparsifying transforms. Results are illustrated with simulations of time-jittered marine acquisition, which translates to jittered source locations for a given speed of the source vessel.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/wason2013SINBADtjo/wason2013SINBADtjo.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
@presentation {warner2012SINBADafw,
	title = {Anisotropic 3D full-waveform inversion of the Tommeliten Alpha field},
	year = {2012},
	publisher = {SINBAD},
	abstract = {We have implemented a robust and practical scheme for anisotropic 3D acoustic full-waveform inversion. We demonstrate this scheme on a field data set, applying it to a four-component ocean-bottom survey over the Tommeliten Alpha field in the North Sea. This shallow-water data set provides good azimuthal coverage to offsets of 7 km, with reduced coverage to a maximum offset of about 11 km. The reservoir lies at the crest of a high-velocity antiformal chalk section, overlain by about 3000 m of clastics within which a low-velocity gas cloud produces a seismic obscured area. We inverted only the hydrophone data, and we retained free-surface multiples and ghosts within the field data. We invert in six narrow frequency bands, in the range 3 to 6.5 Hz. At each iteration, we selected only a subset of sources, using a different subset at each iteration; this strategy is more efficient than inverting all the data every iteration. Our starting velocity model was obtained using standard PSDM model building including anisotropic reflection tomography, and contained epsilon values as high as 20\%. We have also attempted full-elastic inversion of these data to recover a shallow isotropic model of both p and s-wave velocities. The final FWI velocity model shows a network of shallow high-velocity channels that match similar features in the reflection data. Deeper in the section, the FWI velocity model reveals a sharper and more-intense low-velocity region associated with the gas cloud in which low-velocity fingers match the location of gas-filled faults visible in the reflection data. The resulting velocity model provides a better match to well logs, and better flattens common image gathers, than does the starting model. Reverse-time migration, using the FWI velocity model, provides significant uplift to the migrated image, simplifying the planform of the reservoir section at depth. The workflows, inversion strategy, and algorithms that we have used have broad application to invert a wide-range of analogous field data sets.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/warner2012SINBADafw/warner2012SINBADafw_pres.pdf},
	author = {Mike Warner}
}
@presentation {wason2012SINBADobs,
	title = {Ocean bottom seismic acquisition via jittered sampling},
	year = {2012},
	publisher = {SINBAD},
	abstract = {We present a pragmatic marine acquisition scheme where a single (or multiple) vessel sails across an ocean-bottom array firing airguns at ? optimally jittered source locations and instances in time. Following the principles of compressive sensing, we can significantly impact the reconstruction quality of conventional seismic data (from jittered data) and demonstrate successful recovery by sparsity promotion. In contrast to random (under)sampling, acquisition via jittered (under)sampling helps in controlling the maximum gap size, which is a practical requirement of wavefield reconstruction with localized sparsifying transforms. Results are illustrated with simulations of optimally jittered marine acquisition, and periodic time-dithering marine acquisition.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/wason2012SINBADobs/wason2012SINBADobs_pres.pdf},
	author = {Haneet Wason}
}
@presentation {herrmann2012SINBADfwi,
	title = {Our findings on the Chevron benchmark dataset},
	year = {2012},
	publisher = {SINBAD},
	abstract = {During this presentation, we will review our findings working with the synthetic GOM data released as part of the post SEG workshop:"Gulf of Mexico Imaging Challenges: What Can Full Waveform Inversion Achieve?". This is joint work with Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Xiang Li, Brendan R. Smithyman, Eric Takam Takougang, and Haneet Wason.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADfwi/herrmann2012SINBADfwi_pres.pdf},
	author = {Andrew J. Calvert and Ian Hanlon and Mostafa Javanmehri and Rajiv Kumar and Tristan van Leeuwen and Xiang Li and Brendan R. Smithyman and Eric Takam Takougang and Haneet Wason and Felix J. Herrmann}
}
@presentation {wason2012SINBADrma,
	title = {Randomized marine acquisition for ocean-bottom surveys},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/wason2012SINBADrma/wason2012SINBADrma_pres.pdf},
	author = {Haneet Wason}
}
@presentation {wason2011SINBADode,
	title = {Only dither: efficient marine acquisition {\textquoteleft}{\textquoteleft}without" simultaneous sourcing},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/wason2011SINBADode/wason2011SINBADode_pres.pdf},
	author = {Haneet Wason}
}
@presentation {wason2010SINBADssd,
	title = {Sequential source data recovery from simultaneous acquisition through transform-domain sparsity promotion - curvelet versus shearlet transform},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/wason2010SINBADssd/wason2010SINBADssd_pres.pdf},
	author = {Haneet Wason}
}
@conference {wang2008SINBADrri,
	title = {Recent results in curvelet-based primary-multiple separation},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {We present a nonlinear curvelet-based sparsity-promoting formulation for the primary-multiple separation problem. We show that these coherent signal components can be separated robustly by explicitly exploiting the locality of curvelets in phase space (space-spatial frequency plane) and their ability to compress data volumes that contain wavefronts. This work is an extension of earlier results and the presented algorithms are shown to be stable under noise and moderately erroneous multiple predictions.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/wang2008SINBADrri/wang2008SINBADrri.pdf},
	author = {Deli Wang and Rayan Saab and Ozgur Yilmaz and Felix J. Herrmann}
}
