@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
