@article {kumar2017hrc,
	title = {Highly repeatable 3D compressive full-azimuth towed-streamer time-lapse acquisition {\textendash}- a numerical feasibility study at scale},
	journal = {The Leading Edge},
	volume = {36},
	number = {8},
	year = {2017},
	note = {(The Leading Edge)},
	month = {08},
	pages = {677-687},
	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 in this special issue.},
	keywords = {3D, CS, marine, rank minimization, simultaneous long offset, time-lapse seismic},
	doi = {10.1190/tle36080677.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2017/kumar2017hrc/kumar2017hrc.html},
	author = {Rajiv Kumar and Haneet Wason and Shashin Sharan and Felix J. Herrmann}
}
@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}
}
@article {oghenekohwo2016GEOPctl,
	title = {Low-cost time-lapse seismic with distributed compressive sensing{\textendash}-Part 1: exploiting common information among the vintages},
	journal = {Geophysics},
	volume = {82},
	number = {3},
	year = {2017},
	note = {(Geophysics)},
	month = {05},
	pages = {P1-P13},
	abstract = {Time-lapse seismic is a powerful technology for monitoring a variety of subsurface changes due to reservoir fluid flow. However, the practice can be technically challenging when one seeks to acquire colocated time-lapse surveys with high degrees of replicability among the shot locations. We have determined that under "ideal" circumstances, in which we ignore errors related to taking measurements off the grid, high-quality prestack data can be obtained from randomized subsampled measurements that are observed from surveys in which we choose not to revisit the same randomly subsampled on-the-grid shot locations. Our acquisition is low cost because our measurements are subsampled. We have found that the recovered finely sampled prestack baseline and monitor data actually improve significantly when the same on-the-grid shot locations are not revisited. We achieve this result by using the fact that different time-lapse data share information and that nonreplicated (on-the-grid) acquisitions can add information when prestack data are recovered jointly. Whenever the time-lapse data exhibit joint structure{\textendash}-i.e., they are compressible in some transform domain and share information{\textendash}-sparsity-promoting recovery of the "common component" and "innovations," with respect to this common component, outperforms independent recovery of the prestack baseline and monitor data. The recovered time-lapse data are of high enough quality to serve as the input to extract poststack attributes used to compute time-lapse differences. Without joint recovery, artifacts{\textendash}-due to the randomized subsampling{\textendash}-lead to deterioration of the degree of repeatability of the time-lapse data. We tested this method by carrying out experiments with reliable statistics from thousands of repeated experiments. We also confirmed that high degrees of repeatability are achievable for an ocean-bottom cable survey acquired with time-jittered continuous recording.},
	keywords = {Acquisition, joint recovery method, marine, random sampling, time-lapse seismic},
	doi = {10.1190/geo2016-0076.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2017/oghenekohwo2016GEOPctl/oghenekohwo2016GEOPctl.html},
	author = {Felix Oghenekohwo and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@article {wason2016GEOPctl,
	title = {Low-cost time-lapse seismic with distributed compressive sensing{\textendash}-Part 2: impact on repeatability},
	journal = {Geophysics},
	volume = {82},
	number = {3},
	year = {2017},
	note = {(Geophysics)},
	month = {05},
	pages = {P15-P30},
	abstract = {Irregular or off-the-grid spatial sampling of sources and receivers is inevitable in field seismic acquisitions. Consequently, time-lapse surveys become particularly expensive since current practices aim to replicate densely sampled surveys for monitoring changes occurring in the reservoir due to hydrocarbon production. We demonstrate that under certain circumstances, high-quality prestack data can be obtained from cheap randomized subsampled measurements that are observed from nonreplicated surveys. We extend our time-jittered marine acquisition to time-lapse surveys by designing acquisition on irregular spatial grids that render simultaneous, subsampled and irregular measurements. Using the fact that different time-lapse data share information and that nonreplicated surveys add information when prestack data are recovered jointly, we recover periodic densely sampled and colocated prestack data by adapting the recovery method to incorporate a regularization operator that maps traces from an irregular spatial grid to a regular periodic grid. The recovery method is, therefore, a combined operation of regularization, interpolation (estimating missing fine-grid traces from subsampled coarse-grid data), and source separation (unraveling overlapping shot records). By relaxing the insistence on replicability between surveys, we find that recovery of the time-lapse difference shows little variability for realistic field scenarios of slightly nonreplicated surveys that suffer from unavoidable natural deviations in spatial sampling of shots (or receivers) and pragmatic compressed-sensing based nonreplicated surveys when compared to the "ideal" scenario of exact replicability between surveys. Moreover, the recovered densely sampled prestack baseline and monitor data improve significantly when the acquisitions are not replicated, and hence can serve as input to extract poststack attributes used to compute time-lapse differences. Our observations are based on experiments conducted for an ocean-bottom cable survey acquired with time-jittered continuous recording assuming source equalization (or same source signature) for the time-lapse surveys and no changes in wave heights, water column velocities or temperature and salinity profiles, etc.},
	keywords = {joint recovery method, Marine acquisition, off-the-grid recovery, Optimization, random sampling, time-lapse seismic},
	doi = {10.1190/geo2016-0252.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2017/wason2016GEOPctl/wason2016GEOPctl.html},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@mastersthesis {wason2017THsss,
	title = {Simultaneous-source seismic data acquisition and processing with compressive sensing},
	year = {2017},
	note = {(PhD)},
	month = {08},
	school = {The University of British Columbia},
	type = {phd},
	address = {Vancouver},
	abstract = {The work in this thesis adapts ideas from the field of compressive sensing (CS) that lead to new insights into acquiring and processing seismic data, where we can fundamentally rethink how we design seismic acquisition surveys and process acquired data to minimize acquisition- and processing-related costs. Current efforts towards dense source/receiver sampling and full azimuthal coverage to produce high-resolution images of the subsurface have led to the deployment of multiple sources across survey areas. A step ahead from multisource acquisition is simultaneous-source acquisition, where multiple sources fire shots at near-simultaneous/random times resulting in overlapping shot records, in comparison to no overlaps during conventional sequential-source acquisition. Adoption of simultaneous-source techniques has helped to improve survey efficiency and data density. The engine that drives simultaneous-source technology is simultaneous-source separation {\textendash}- a methodology that aims to recover conventional sequential-source data from simultaneous-source data. This is essential because many seismic processing techniques rely on dense and periodic (or regular) source/receiver sampling. We address the challenge of source separation through a combination of tailored simultaneous-source acquisition design and sparsity-promoting recovery via convex optimization using l1 objectives. We use CS metrics to investigate the relationship between marine simultaneous-source acquisition design and data reconstruction fidelity, and consequently assert the importance of randomness in the acquisition system in combination with an appropriate choice for a sparsifying transform (i.e., curvelet transform) in the reconstruction algorithm. We also address the challenge of minimizing the cost of expensive, dense, periodically-sampled and replicated time-lapse surveying and data processing by adapting ideas from distributed compressive sensing. We show that compressive randomized time-lapse surveys need not be replicated to attain acceptable levels of data repeatability, as long as we know the shot positions (post acquisition) to a sufficient degree of accuracy. We conclude by comparing sparsity-promoting and rank-minimization recovery techniques for marine simultaneous-source separation, and demonstrate that recoveries are comparable; however, the latter approach readily scales to large-scale seismic data and is computationally faster.},
	keywords = {Acquisition, Compressive Sensing, marine, Optimization, PhD, simultaneous source, source separation},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2017/wason2017THsss/wason2017THsss.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2017/wason2017THsss/wason2017THsss_pres.pdf},
	author = {Haneet Wason}
}
@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}
}
@conference {herrmann2016SEGWSctl,
	title = {Compressive time-lapse marine acquisition},
	booktitle = {SEG Workshop on Low cost geophysics: How to be creative in a cost-challenged environment; Dallas},
	year = {2016},
	note = {(SEG Workshop, Dallas)},
	month = {10},
	keywords = {Acquisition, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/herrmann2016SEGWSctl/herrmann2016SEGWSctl_pres.pdf},
	author = {Felix J. Herrmann and Rajiv Kumar and Felix Oghenekohwo and Shashin Sharan and Haneet Wason}
}
@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 {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}
}
@conference {kumar2016SEGtjm,
	title = {Time-jittered marine acquisition{\textendash}-a rank-minimization approach for 5D source separation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {119-123},
	abstract = {Simultaneous source marine acquisition has been recognized as an economic way of improving spatial sampling and speedup acquisition time, where a single- (or multiple-) source vessel fires at jittered source locations and time instances. Consequently, the acquired simultaneous data volume is processed to separate the overlapping shot records resulting in densely sampled data volume. It has been shown in the past that the simultaneous source acquisition design and source separation process can be setup as a compressed sensing problem, where conventional seismic data is reconstructed from simultaneous data via a sparsity-promoting optimization formulation. While the recovery quality of separated data is reasonably well, the recovery process can be computationally expensive due to transform-domain redundancy. In this paper, we present a computationally tractable rank-minimization algorithm to separate simultaneous data volumes. The proposed algorithm is suitable for large-scale seismic data, since it avoids singular-value decompositions and uses a low-rank based factorized formulation instead. Results are illustrated for simulations of simultaneous time-jittered continuous recording for a 3D ocean-bottom cable survey.},
	keywords = {5D, marine, SEG, source separation, time-jittered acquisition},
	doi = {10.1190/segam2016-13878249.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/kumar2016SEGtjm/kumar2016SEGtjm.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/kumar2016SEGtjm/kumar2016SEGtjm_pres.pdf},
	author = {Rajiv Kumar and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}
@conference {wason2015EAGEcsm,
	title = {Compressed sensing in 4-D marine{\textendash}-recovery of dense time-lapse data from subsampled data without repetition},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {We present an extension of our time-jittered marine acquisition for time-lapse surveys by working 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 are interested in recovering the densely sampled baseline and monitor, and then the (complete) 4-D difference from subsampled/incomplete baseline and monitor data.},
	keywords = {EAGE, NFFT, off-the-grid, simultaneous acquisition, time-lapse},
	doi = {10.3997/2214-4609.201413088},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/wason2015EAGEcsm/wason2015EAGEcsm.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/wason2015EAGEcsm/wason2015EAGEcsm_poster.pdf},
	author = {Haneet Wason and Felix Oghenekohwo 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}
}
@conference {kumar2015IIPFWIrmb,
	title = {Rank minimization based seismic data processing and inversion},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {We present the low-rank extensions of full-waveform inversion to mitigate the local-minima issues. The purpose formulation is computationally tractable and does not involve any extra computational cost compared to the adjoint-state method.},
	keywords = {FWI, inversion, Processing, rank minimization},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/kumar2015IIPFWIrmb/kumar2015IIPFWIrmb_pres.pdf},
	author = {Rajiv Kumar and Curt Da Silva and Oscar Lopez and Aleksandr Y. Aravkin and Hassan Mansour and Haneet Wason and Ernie Esser 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 {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 {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}
}
@article {kumar2015sss,
	title = {Source separation for simultaneous towed-streamer marine acquisition {\textendash}- a compressed sensing approach},
	journal = {Geophysics},
	volume = {80},
	number = {6},
	year = {2015},
	note = {(Geophysics)},
	month = {11},
	pages = {WD73-WD88},
	abstract = {Simultaneous marine acquisition is an economic way to sample seismic data and speed up acquisition, wherein single or multiple source vessels fire sources at near-simultaneous or slightly random times, resulting in overlapping shot records. The current paradigm for simultaneous towed-streamer marine acquisition incorporates {\textquotedblleft}low variability{\textquotedblright} in source firing times, i.e., 0 <= 1 or 2 s because the sources and receivers are moving. This results in a low degree of randomness in simultaneous data, which is challenging to separate (into its constituent sources) using compressed-sensing-based separation techniques because randomization is key to successful recovery via compressed sensing. We have addressed the challenge of source separation for simultaneous towed-streamer acquisitions via two compressed-sensing-based approaches, i.e., sparsity promotion and rank minimization. We have evaluated the performance of the sparsity-promotion- and rank-minimization-based techniques by simulating two simultaneous towed-streamer acquisition scenarios, i.e., over/under and simultaneous long offset. A field data example from the Gulf of Suez for the over/under acquisition scenario was also developed. We observed that the proposed approaches gave good and comparable recovery qualities of the separated sources, but the rank-minimization technique outperformed the sparsity-promoting technique in terms of the computational time and memory. We also compared these two techniques with the normal-moveout-based median-filtering-type approach, which had comparable results.},
	keywords = {2D, Acquisition, inversion, marine, Optimization, Rank, source separation, sparsity},
	doi = {10.1190/geo2015-0108.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2015/kumar2015sss/kumar2015sss_revised.pdf},
	url2 = {http://library.seg.org/doi/abs/10.1190/geo2015-0108.1},
	author = {Rajiv Kumar and Haneet Wason and Felix J. Herrmann}
}
@conference {lopez2015SEGWSsfm,
	title = {SVD-free matrix completion in randomized marine acquisition},
	booktitle = {SEG Workshop on Rank-Reduction and Other Sparse Transform Methods with Application to Data Reconstruction, De-Noising, De-Blending and Imaging; New Orleans},
	year = {2015},
	note = {(SEG, New Orleans)},
	month = {10},
	abstract = {During this presentation, we will illustrate how insights from matrix-free matrix completion can be used to solve problems in (on- and off-the-grid) missing trace interpolation and time-jittered (simultaneous) marine acquisition. In particular, we will demonstrate the importance of data organization to favor recovery by rank minimization and the use of deterministic spectral properties to predict the recovery. We also make make comparisons between wavefield reconstructions based on transform-domain sparsity promotion and matrix completion.},
	keywords = {Acquisition, interpolation, matrix completion, rank reduction, SEG, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2015/lopez2015SEGWSsfm/lopez2015SEGWSsfm_pres.pdf},
	author = {Oscar Lopez and Haneet Wason and Curt Da Silva and Rajiv Kumar and Felix J. Herrmann}
}
@techreport {kumar2015EAGEtjm,
	title = {Time-jittered marine acquisition: low-rank v/s sparsity},
	number = {TR-EOAS-2015-2},
	year = {2015},
	month = {01},
	institution = {UBC},
	abstract = {Time-jittered simultaneous marine acquisition has been recognized as an economic way of improving the spatial sampling, and speedup acquisition, where a single (or multiple) source vessel fires at {\textendash} jittered source locations and instances in time. It has been shown in the past that this problem can be setup as a {\textendash} compressed sensing problem, where conventional seismic data is reconstructed from blended data via a sparsity-promoting optimization formulation. While the recovery quality of deblended data is very good, the recovery process is computationally very expensive. In this paper, we present a computationally efficient rank-minimization algorithm to deblend the seismic data. The proposed algorithm is suitable for large-scale seismic data, since it avoids SVD computations and uses a low-rank factorized formulation instead. Results are illustrated with simulations of time-jittered marine acquisition, which translates to jittered source locations for a given speed of the source vessel, for a single source vessel with two airgun arrays.},
	keywords = {deblending, low-rank},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2015/kumar2015EAGEtjm/kumar2015EAGEtjm.html},
	author = {Rajiv Kumar and Haneet Wason 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 {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}
}
@conference {herrmann2014EAGEWSrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	booktitle = {EAGE Workshop on Land and Ocean Bottom; Broadband Full Azimuth Seismic Surveys; Spain},
	year = {2014},
	note = {(EAGE Workshop, Spain)},
	month = {04},
	keywords = {4D seismic, EAGE, Marine acquisition, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/herrmann2014EAGEWSrrt.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {wason2014SEGrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {46-51},
	abstract = {We present an extension of our time-jittered simultaneous marine acquisition to time-lapse surveys where the requirement for repeatability in acquisition can be waived provided we know the acquisition geometry afterwards. Our method, which does not require repetition, gives 4-D signals comparable to conventional methods where repeatability is key to their success.},
	keywords = {Acquisition, deblending, marine, SEG, time-laspe},
	doi = {http://dx.doi.org/10.1190/segam2014-1677.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/wason2014SEGrrt/wason2014SEGrrt.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/wason2014SEGrrt/wason2014SEGrrt_pres.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}
}
@conference {wason2014SEGsss,
	title = {Source separation via SVD-free rank minimization in the hierarchical semi-separable representation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {120-126},
	abstract = {Recent developments in matrix rank optimization have allowed for new computational approaches in the field of source separation or deblending. In this paper, we propose 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 structure (HSS) inside rank-regularized least-squares formulations. 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 performance of the new HSS-based deblending approach by simulating an over/under blended acquisition, wherein uniformly random time delays (of < 1 second) are applied to one of the sources.},
	keywords = {Acquisition, deblending, HSS, marine, Rank, SEG, source separation},
	doi = {http://dx.doi.org/10.1190/segam2014-1583.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/wason2014SEGsss/wason2014SEGsss.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/wason2014SEGsss/wason2014SEGsss_pres.pdf},
	author = {Haneet Wason and Rajiv Kumar and Aleksandr Y. Aravkin 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}
}
@conference {lin2013SEGdss,
	title = {Dense shot-sampling via time-jittered marine sources},
	booktitle = {SEG Workshop on Simultaneous Sources; Houston},
	year = {2013},
	month = {09},
	keywords = {Acquisition, SEG, simultaneous sources, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/lin2013SEGdss/lin2013SEGdss_pres.pdf},
	author = {Tim T.Y. Lin and Haneet Wason and Felix J. Herrmann}
}
@article {herrmann2013TLEffwi,
	title = {Frugal full-waveform inversion: from theory to a practical algorithm},
	journal = {The Leading Edge},
	volume = {32},
	number = {9},
	year = {2013},
	month = {09},
	pages = {1082-1092},
	abstract = {As conventional oil and gas fields are maturing, our profession is challenged to come up with the next-generation of more and more sophisticated exploration tools. In exploration seismology this trend has led to the emergence of wave-equation based inversion technologies such as reverse-time migration and full-waveform inversion. While significant progress has been made in wave-equation based inversion, major challenges remain in the development of robust and computationally feasible workflows that give reliable results in geophysically challenging areas that may include ultra-low shear velocity zones or high-velocity salt. Moreover, sub-salt production carries risks that needs mitigation, which raises the bar from creating sub-salt images to inverting for sub-salt overpressure.},
	keywords = {Optimization, waveform inversion},
	doi = {10.1190/tle32091082.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2013/herrmann2013ffwi/herrmann2013ffwi.html},
	author = {Felix J. Herrmann and 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}
}
@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}
}
@conference {wason2013EAGEobs,
	title = {Ocean bottom seismic acquisition via jittered sampling},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2013},
	month = {06},
	abstract = {We present a pragmatic marine acquisition scheme where multiple source vessels sail across an ocean-bottom array firing at airgunsjittered 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 time-jittered marine acquisition, which translates to jittered source locations for a given speed of the source vessel, for two source vessels.},
	keywords = {Acquisition, blended, deblending, EAGE, interpolation, marine},
	doi = {10.3997/2214-4609.20130379},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/wason2013EAGEobs/wason2013EAGEobs.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/wason2013EAGEobs/wason2013EAGEobs_pres.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
@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}
}
@conference {wason2013SEGtjo,
	title = {Time-jittered ocean bottom seismic acquisition},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {32},
	year = {2013},
	month = {09},
	pages = {1-6},
	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, for two source vessels.},
	keywords = {Acquisition, blending, deblending, interpolation, jittered sampling, marine, OBC, SEG},
	doi = {10.1190/segam2013-1391.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/wason2013SEGtjo/wason2013SEGtjo.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/wason2013SEGtjo/wason2013SEGtjo_pres.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}
}
@conference {herrmann2012EAGEcsm,
	title = {Compressive sensing in marine acquisition and beyond},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {Simultaneous-source marine acquisition is an example of compressive sensing where acquisition with a single vessel is replaced by simultaneous acquisition by multiple vessels with sources that fire at randomly dithered times. By identifying simultaneous acquisition as compressive sensing, we are able to design acquisitions that favour recovery by sparsity promotion. Compared to conventional processing that yields estimates for sequential data, sparse recovery leads to significantly improved results for simultaneous data volumes that are collected in shorter times. These improvements are the result of proper design of the acquisition, selection of the appropriate transform domain, and solution of the recovery problem by sparsity promotion. During this talk, we will show how these design principles can be applied to marine acquisition and to other problems in exploration seismology that can benefit from compressive sensing.},
	keywords = {Acquisition, EAGE, marine, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/herrmann2012EAGEcsm/herrmann2012EAGEcsm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/herrmann2012EAGEcsm/herrmann2012EAGEcsm_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=59854},
	author = {Felix J. Herrmann and Haneet Wason}
}
@conference {herrmann2012SEGfwi,
	title = {FWI from the West Coasts: lessons learned from "Gulf of Mexico Imaging Challenges: What Can Full Waveform Inversion Achieve?"},
	booktitle = {SEG Workshop on FWI; Las Vegas},
	year = {2012},
	publisher = {SEG},
	organization = {SEG},
	keywords = {FWI, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2012/herrmann2012SEGfwi/herrmann2012SEGfwi_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 {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}
}
@conference {wason2012EAGEode,
	title = {Only dither: efficient simultaneous marine acquisition},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {Simultaneous-source acquisition is an emerging technology that is stimulating both geophysical research and commercial efforts. The focus here is on simultaneous-source marine acquisition design and sparsity-promoting sequential-source data recovery. We propose a pragmatic simultaneous-source, randomized marine acquisition scheme where multiple vessels sail across an ocean-bottom array firing airguns at {\textendash}- sequential locations and randomly time-dithered instances. Within the context of compressive sensing, where the choice of the sparsifying transform needs to be incoherent with the compressive sampling matrix, we can significantly impact the reconstruction quality, and demonstrate that the compressive sampling matrix resulting from the proposed sampling scheme is sufficiently incoherent with the curvelet transform to yield successful recovery by sparsity promotion. Results are illustrated with simulations of {\textquoteleft}{\textquoteleft}purely" random marine acquisition, which requires an airgun to be located at each source location, and random time-dithering marine acquisition with one and two source vessels. Size of the collected data volumes in all cases is the same. Compared to the recovery from the former acquisition scheme (SNR = 10.5dB), we get good results by dithering with only one source vessel (SNR = 8.06dB) in the latter scheme, which improve at the cost of having an additional source vessel (SNR = 9.44dB).},
	keywords = {Acquisition, EAGE, marine, simultaneous},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/wason2012EAGEode/wason2012EAGEode.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/wason2012EAGEode/wason2012EAGEode_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=58915},
	author = {Haneet Wason and Felix J. Herrmann}
}
@conference {wason2012CSEGode,
	title = {Only dither: efficient simultaneous marine acquisition},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2012},
	month = {02},
	abstract = {Simultaneous-source acquisition is an emerging technology that is stimulating both geophysical research and commercial efforts. Simultaneous marine acquisition calls for the development of a new set of design principles and post-processing tools. The focus here is on simultaneous-source marine acquisition design and sparsity-promoting sequential-source data recovery. We propose a pragmatic simultaneous-source, randomized marine acquisition scheme where multiple vessels sail across an ocean-bottom array firing airguns at {\textendash}- sequential locations and randomly time-dithered instances. By leveraging established findings from the field of compressive sensing, where the choice of the sparsifying transform needs to be incoherent with the compressive sampling matrix, we can significantly impact the reconstruction quality, and demonstrate that the compressive sampling matrix resulting from the proposed sampling scheme is sufficiently incoherent with the curvelet transform to yield successful recovery by sparsity promotion. Results are illustrated with simulations of {\textquotedblleft}purely{\textquotedblright} random marine acquisition, which requires an airgun to be located at each source location, and random time-dithering marine acquisition with one and two source vessels. Size of the collected data volumes in all cases is the same. Compared to the recovery from the former acquisition scheme (SNR = 10.5dB), we get good results by dithering with only one source vessel (SNR = 8.06dB) in the latter scheme, which improve at the cost of having an additional source vessel (SNR = 9.85dB).},
	keywords = {Acquisition, CSEG, marine, simultaneous},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2012/wason2012CSEGode/wason2012CSEGode.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
@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}
}
@article {Mansour11TRssma,
	title = {Randomized marine acquisition with compressive sampling matrices},
	journal = {Geophysical Prospecting},
	volume = {60},
	number = {4},
	year = {2012},
	month = {07},
	pages = {648-662},
	abstract = {Seismic data acquisition in marine environments is a costly process that calls for the adoption of simultaneous-source or randomized acquisition - an emerging technology that is stimulating both geophysical research and commercial efforts. Simultaneous marine acquisition calls for the development of a new set of design principles and post-processing tools. In this paper, we discuss the properties of a specific class of randomized simultaneous acquisition matrices and demonstrate that sparsity-promoting recovery improves the quality of reconstructed seismic data volumes. We propose a practical randomized marine acquisition scheme where the sequential sources fire airguns at only randomly time-dithered instances. We demonstrate that the recovery using sparse approximation from random time-dithering with a single source approaches the recovery from simultaneous-source acquisition with multiple sources. Established findings from the field of compressive sensing indicate that the choice of the sparsifying transform that is incoherent with the compressive sampling matrix can significantly impact the reconstruction quality. Leveraging these findings, we then demonstrate that the compressive sampling matrix resulting from our proposed sampling scheme is incoherent with the curvelet transform. The combined measurement matrix exhibits better isometry properties than other transform bases such as a non-localized multidimensional Fourier transform. We illustrate our results with simulations of "ideal" simultaneous-source marine acquisition, which dithers both in time and space, compared with periodic and randomized time-dithering.},
	keywords = {curvelet transform, Fourier, Marine acquisition},
	doi = {10.1111/j.1365-2478.2012.01075.x},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2478.2012.01075.x/abstract},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2012/Mansour11TRssma/Mansour11TRssma.pdf},
	author = {Hassan Mansour and Haneet Wason and Tim T.Y. Lin and Felix J. Herrmann}
}
@article {herrmann2011RECORDERcsse2,
	title = {Compressive sensing in seismic exploration: an outlook on a new paradigm},
	journal = {CSEG Recorder},
	volume = {36},
	number = {6},
	year = {2011},
	note = {Part 1 was published in April and Part 2 was published in June},
	month = {06},
	pages = {34-39},
	abstract = {Many seismic exploration techniques rely on the collection of massive data volumes that are subsequently mined for information during processing. While this approach has been extremely successful in the past, current efforts toward higher resolution images in increasingly complicated regions of the Earth continue to reveal fundamental shortcomings in our workflows. Chiefly amongst these is the so-called "curse of dimensionality" exemplified by Nyquist{\textquoteright}s sampling criterion, which disproportionately strains current acquisition and processing systems as the size and desired resolution of our survey areas continues to increase. We offer an alternative sampling method leveraging recent insights from compressive sensing towards seismic acquisition and processing for data that, from a traditional point of view, are considered to be undersampled. The main outcome of this approach is a new technology where acquisition and processing related costs are decoupled the stringent Nyquist sampling criterion. At the heart of our approach lies randomized incoherent sampling that breaks subsampling-related interferences by turning them into harmless noise, which we subsequently remove by promoting sparsity in a transform-domain. Acquisition schemes designed to fit into this regime no longer grow significantly in cost with increasing resolution and dimensionality of the survey area, but instead its cost ideally only depends on transform-domain sparsity of the expected data. Our contribution is twofold. First, we demonstrate by means of carefully designed numerical experiments that ideas from compressive sensing can be adapted to seismic acquisition. Second, we leverage the property that seismic data volumes are well approximated by a small percentage of curvelet coefficients. Thus curvelet-domain sparsity allows us to recover conventionally-sampled seismic data volumes from compressively-sampled data volumes whose size exceeds this percentage by only a small factor. Because compressive sensing combines transformation and encoding by a single linear encoding step, this technology is directly applicable to seismic acquisition and therefore constitutes a new paradigm where acquisitions costs scale with transform-domain sparsity instead of the gridsize. We illustrate this principle by showcasing recovery of a real seismic line from simulated compressively sampled acquisitions.},
	url = {http://csegrecorder.com/articles/view/compressive-sensing-in-seismic-exploration-an-outlook-on-a-new-paradigm},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/CSEGRecorder/2011/herrmann2011RECORDERcsse/herrmann2011RECORDERcsse.pdf},
	author = {Felix J. Herrmann and Haneet Wason and Tim T.Y. Lin}
}
@article {herrmann2011RECORDERcsse1,
	title = {Compressive sensing in seismic exploration: an outlook on a new paradigm},
	journal = {CSEG Recorder},
	volume = {36},
	number = {4},
	year = {2011},
	note = {Part 1 was published in April and Part 2 was published in June},
	month = {04},
	pages = {19-33},
	abstract = {Many seismic exploration techniques rely on the collection of massive data volumes that are subsequently mined for information during processing. While this approach has been extremely successful in the past, current efforts toward higher resolution images in increasingly complicated regions of the Earth continue to reveal fundamental shortcomings in our workflows. Chiefly amongst these is the so-called "curse of dimensionality" exemplified by Nyquist{\textquoteright}s sampling criterion, which disproportionately strains current acquisition and processing systems as the size and desired resolution of our survey areas continues to increase. We offer an alternative sampling method leveraging recent insights from compressive sensing towards seismic acquisition and processing for data that, from a traditional point of view, are considered to be undersampled. The main outcome of this approach is a new technology where acquisition and processing related costs are decoupled the stringent Nyquist sampling criterion. At the heart of our approach lies randomized incoherent sampling that breaks subsampling-related interferences by turning them into harmless noise, which we subsequently remove by promoting sparsity in a transform-domain. Acquisition schemes designed to fit into this regime no longer grow significantly in cost with increasing resolution and dimensionality of the survey area, but instead its cost ideally only depends on transform-domain sparsity of the expected data. Our contribution is split into two part.},
	url = {http://csegrecorder.com/articles/view/compressive-sensing-in-seismic-exploration-outlook-on-a-new-paradigm},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/CSEGRecorder/2011/herrmann2011RECORDERcsse/herrmann2011RECORDERcsse.pdf},
	author = {Felix J. Herrmann and Haneet Wason and Tim T.Y. Lin}
}
@conference {mansour2011SBGFcspsma,
	title = {A compressive sensing perspective on simultaneous marine acquisition},
	booktitle = {SBGF},
	year = {2011},
	publisher = {SBGF},
	organization = {SBGF},
	abstract = {The high cost of acquiring seismic data in marine environments compels the adoption of simultaneous- source acquisition - an emerging technology that is stimulating both geophysical research and commercial efforts. In this paper, we discuss the properties of randomized simultaneous acquisition matrices and demonstrate that sparsity-promoting recovery improves the quality of the reconstructed seismic data volumes. Simultaneous marine acquisition calls for the development of a new set of design principles and post-processing tools. Leveraging established findings from the field of compressed sensing, the recovery from simultaneous sources depends on a sparsifying transform that compresses seismic data, is fast, and reasonably incoherent with the compressive sampling matrix. To achieve this incoherence, we use random time dithering where sequential acquisition with a single airgun is replaced by continuous acquisition with multiple airguns firing at random times and at random locations. We demonstrate our results with simulations of simultaneous Marine acquisition using periodic and randomized time dithering.},
	keywords = {Acquisition, Compressive Sensing, Presentation, SBGF},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SBGF/2011/Mansour11SBGFcspsma/Mansour11SBGFcspsma.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SBGF/2011/Mansour11SBGFcspsma/Mansour11SBGFcspsma.pdf},
	author = {Hassan Mansour and Haneet Wason and Tim T.Y. Lin and Felix J. Herrmann}
}
@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}
}
@conference {wason2011SEGsprsd,
	title = {Sparsity-promoting recovery from simultaneous data: a compressive sensing approach},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {30},
	year = {2011},
	month = {09},
	pages = {6-10},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Seismic data acquisition forms one of the main bottlenecks in seismic imaging and inversion. The high cost of acquisition work and collection of massive data volumes compel the adoption of simultaneous-source seismic data acquisition - an emerging technology that is developing rapidly, stimulating both geophysical research and commercial efforts. Aimed at improving the performance of marine- and land-acquisition crews, simultaneous acquisition calls for development of a new set of design principles and post-processing tools. Leveraging developments from the field of compressive sensing the focus here is on simultaneous-acquisition design and sequential-source data recovery. Apart from proper compressive sensing sampling schemes, the recovery from simultaneous simulations depends on a sparsifying transform that compresses seismic data, is fast, and reasonably incoherent with the compressive-sampling matrix. Using the curvelet transform, in which seismic data can be represented parsimoniously, the recovery of the sequential-source data volumes is achieved using the sparsity-promoting program {\textemdash} SPGL1, a solver based on projected spectral gradients. The main outcome of this approach is a new technology where acquisition related costs are no longer determined by the stringent Nyquist sampling criteria.},
	keywords = {Acquisition, Compressive Sensing, Presentation, SEG},
	doi = {10.1190/1.3628174},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/wason11SEGsprsd/wason11SEGsprsd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/wason11SEGsprsd/wason11SEGsprsd_pres.pdf},
	author = {Haneet Wason and Felix J. Herrmann and Tim T.Y. Lin}
}
@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}
}
