@conference {zhang2023IMAGEssd,
	title = {3D seismic survey design by maximizing the spectral gap},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {The massive cost of 3D acquisition calls for methods to reduce the number of receivers by designing optimal receiver sampling masks. Recent studies on 2D seismic showed that maximizing the spectral gap of the subsampling mask leads to better wavefield reconstruction results. We enrich the current study by proposing a simulation-free method to generate optimal 3D acquisition by maximizing the spectral gap of the subsampling mask via a simulated annealing algorithm. Numerical experiments confirm improvement of the proposed method over receiver sampling locations obtained by jittered sampling.},
	keywords = {Acquisition, matrix factorization, SEG, spectral gap, survey design, wavefield reconstruction},
	doi = {10.1190/image2023-3895546.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/zhang2023IMAGEssd/zhang2023IMAGEssd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/zhang2023IMAGEssd/zhang2023IMAGEssd_pres.pdf},
	author = {Yijun Zhang and Ziyi Yin and Oscar Lopez and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@article {zhang2023otl,
	title = {Optimized time-lapse acquisition design via spectral gap ratio minimization},
	journal = {Geophysics},
	volume = {88},
	number = {4},
	year = {2023},
	note = {(Geophysics)},
	month = {07},
	pages = {A19-A23},
	abstract = {Modern-day reservoir management and monitoring of geological carbon storage increasingly call for costly time-lapse seismic data collection. In this letter, we show how techniques from graph theory can be used to optimize acquisition geometries for low-cost sparse 4D seismic. Based on midpoint-offset domain connectivity arguments, the proposed algorithm automatically produces sparse non-replicated time-lapse acquisition geometries that favor wavefield recovery.},
	keywords = {Acquisition, JRM, matrix factorization, spectral gap, survey design, time-lapse, wavefield reconstruction},
	doi = {10.1190/geo2023-0024.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2023/zhang2023otl/Spectral_Gap_Paper.html},
	author = {Yijun Zhang and Ziyi Yin and Oscar Lopez and Ali Siahkoohi and Mathias Louboutin and Rajiv Kumar and Felix J. Herrmann}
}
@conference {zhang2022SEGass,
	title = {A simulation-free seismic survey design by maximizing the spectral gap},
	booktitle = {International Meeting for Applied Geoscience and Energy Expanded Abstracts},
	year = {2022},
	note = {(IMAGE, Houston)},
	month = {05},
	abstract = {Due to the tremendous cost of seismic data acquisition, methods have been developed to reduce the amount of data acquired by designing optimal missing trace reconstruction algorithms. These technologies are designed to record as little data as possible in the field, while providing accurate wavefield reconstruction in the areas of the survey that are not recorded. This is achieved by designing randomized subsampling masks that allow for accurate wavefield reconstruction via matrix completion methods. Motivated by these recent results, we propose a simulation-free seismic survey design that aims at improving the quality of a given randomized subsampling using a simulated annealing algorithm that iteratively increases the spectral gap of the subsampling mask, a property recently linked to the quality of the reconstruction. We demonstrate that our proposed method improves the data reconstruction quality for a fixed subsampling rate on a realistic synthetic dataset.},
	keywords = {Acquisition, matrix factorization, SEG, spectral gap, survey design, wavefield reconstruction},
	doi = {10.1190/image2022-3751690.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/zhang2022SEGass/Yijun2022SEGass.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/zhang2022SEGass/index.html},
	software = {https://github.com/slimgroup/opt_spectral_gap},
	author = {Yijun Zhang and Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {zhang2022ML4SEISMICtss,
	title = {Time-lapse seismic survey design by maximizing the spectral gap},
	year = {2022},
	month = {11},
	abstract = {While time-lapse seismic has been applied successfully to CO2 sequestration monitoring, it remains a challenging problem since replicated dense surveys come at a very high cost in the field. Wavefield reconstruction based on matrix completion (MC) from randomized subsampled data is an efficient way to reduce operational costs. This technique allows for accurate time-lapse reconstruction by employing the joint recovery model (JRM), which capitalizes on the fact that different vintages share a common component. However, combining JRM with optimal time-lapse acquisition survey design remains an unexplored area of research. In expander graph theory, spectral gap (SG) reveals the source-receiver layout connectivity and is related to reconstruction quality during MC. Building on these insights, we proposed a simulation free time-lapse survey design based on JRM that aims to get similar reconstructed quality without insisting on replicate surveys, which significantly reduces the cost in the field. This approach uses the simulated annealing algorithm to find subsampling masks for each vintage. Numerical experiments confirm a direct correlation between increased spectral gap and promising time-lapse reconstruction quality.},
	keywords = {Acquisition, JRM, matrix factorization, ML4SEISMIC, SLIM, spectral gap, survey design, time-lapse, wavefield reconstruction},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/zhang2022ML4SEISMICtss/index.html},
	author = {Yijun Zhang and Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rajiv Kumar and Oscar Lopez and Felix J. Herrmann}
}
@presentation {zhang2021ML4SEISMICiss,
	title = {Improved seismic survey design by maximizing the spectral gap with global optimization},
	year = {2021},
	month = {11},
	abstract = {Random subsampling is increasingly being used in the acquisition of seismic data to shorten the acquisition time and to reduce costs. However, the design of optimal acquisition geometries is still an ongoing area of research. Matrix completion (MC) is a computationally efficient method to reconstruct fully sampled wavefields from sparsely sampled seismic data. In MC theory, the spectral gap (SG), which is a measure of the connectedness of the graph in expander graph theory, has been used to predict, and to some degree quantify, the quality of wavefield reconstruction, given a specific subsampling scheme (acquisition mask). Building on these insights, we propose an optimization scheme, based on simulated annealing, which finds subsampling masks with large SGs that improve the quality of wavefield reconstruction with MC. The experimental results show that the proposed method successfully increases the SG of the subsampling mask starting from randomly initialized masks. Increasing the SG leads to improved connectivity between the sources and receivers and therefore of the wavefield reconstruction. Numerical experiments confirm a direct relationship between increased SG and improved reconstruction quality. This confirms the value SG analysis brings to the design of seismic surveys without the need to carry out expensive wavefield reconstructions to optimize the acquisition design.},
	keywords = {Acquisition, Compressive Sensing, ML4SEISMIC, SLIM, survey design, wavefield reconstruction},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/zhang2021ML4SEISMICiss/Tue-10-30-Zhang.pdf},
	author = {Yijun Zhang and Felix J. Herrmann}
}
@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}
}
@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}
}
@conference {herrmann2015ASEGrae,
	title = {Randomized algorithms in exploration seismology},
	booktitle = {ASEG Annual Conference Proceedings},
	year = {2015},
	note = {(ASEG, Perth)},
	month = {02},
	abstract = {As in several other industries, progress in exploration seismology relies on the collection and processing of massive data volumes that grow exponentially in size as the survey area and desired resolution increase. This exponential growth {\textendash}- in combination with the increased complexity of the next-generation of iterative wave-equation based inversion algorithms {\textendash}- puts strains on our acquisition systems and computational back ends impeding progress in our field. During this talk, I will review how recent randomized algorithms from Compressive Sensing and Machine learning can be used to overcome some of these challenges by fundamentally rethinking how we sample and process seismic data. The key idea here is to reduce acquisition and computational costs by deliberately working on small randomized subsets of the data at a desired accuracy. I will illustrate these concepts using a variety of compelling examples on realistic synthetics and field data.},
	keywords = {Acquisition, ASEG, compressed sensing, FWI, Imaging, seismology},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ASEG/2015/herrmann2015ASEGrae/herrmann2015ASEGrae.pdf},
	author = {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}
}
@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 {oghenekohwo2014SEGrsw,
	title = {Randomized sampling without repetition in time-lapse surveys},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {4848-4852},
	abstract = {Vouching for higher levels of repeatability in acquisition and processing of time-lapse (4D) seismic data has become the standard with oil and gas contractor companies, with significant investment in the design of acquisition systems and processing algorithms that attempt to address some of the current 4D challenges, in particular, imaging weak 4D signals. Recent developments from the field of compressive sensing have shown the benefits of variants of randomized sampling in marine seismic acquisition and its impact for the future of seismic exploration. Following these developments, we show that the requirement for accurate survey repetition in time-lapse seismic data acquisition can be waived provided we solve a sparsity-promoting convex optimization program that makes use of the shared component between the baseline and monitor data. By setting up a framework for inversion of the stacked sections of a time-lapse data, given the pre-stack data volumes, we are able to extract 4D signals with relatively highfidelity from significant subsamplings. Our formulation is applied to time-lapse data that has been acquired with different source/receiver geometries, paving the way for an efficient approach to dealing with time-lapse data acquired with initially poor repeatability levels, provided the survey geometry details are known afterwards.},
	keywords = {4D, Acquisition, random, repetition, SEG, time-lapse},
	doi = {http://dx.doi.org/10.1190/segam2014-1627.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/oghenekohwo2014SEGrsw/oghenekohwo2014SEGrsw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/oghenekohwo2014SEGrsw/oghenekohwo2014SEGrsw_pres.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar 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 {oghenekohwo2013CSEGnratld,
	title = {Assessing the need for repeatability in acquisition of time-lapse data},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2013},
	month = {05},
	abstract = {There are several factors that affect the repeatability of 4D(time-lapse) seismic data. One of the most significant factors is the repeatability of the acquisition, particularly the locations of the sources and receivers. It is important to repeat the source-receiver locations, used during the baseline survey, in the monitor or repeat survey. Also, it is essential that the stacked data volumes used for time-lapse analysis are created using the same offset ranges for each survey. This condition is crucial in order to be able to produce an image of the same location over a period of time and enhances proper reservoir characterization. The cost of repeating the seismic acquisition is very expensive, as often times, the receiver array has to be left at the same location over the period for which the data will be acquired. In other words, it is important to repeat the acquisition geometry as much as possible. In this talk, we investigate the results of changing the acquisition geometry, by a random placement of the receivers for both the baseline surveys and newer (monitor) surveys. Results show that we are still able to observe any time-lapse effects from the proposed acquisition geometry. Our experiments have been performed on a synthetic model.},
	keywords = {Acquisition, CSEG, migration, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2013/oghenekohwo2013CSEGnratld/oghenekohwo2013CSEGnratld.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2013/oghenekohwo2013CSEGnratld/oghenekohwo2013CSEGnratld_pres.pdf},
	author = {Felix Oghenekohwo 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}
}
@techreport {oghenekohwo2013SEGtlswrs,
	title = {Time-lapse seismics with randomized sampling},
	number = {TR-EOAS-2013-3},
	year = {2013},
	institution = {UBC},
	abstract = {In time-lapse or 4D seismics, repeatability of the acquisition is a very crucial step, as we do not want spurious events that are not there. In this paper, we propose an approach which avoids any requirement to repeat the surveys, by using randomized sampling technique which allows us to be more efficient in the acquisition. Our method applies to sampling data using ocean bottom nodes (OBN) as receivers. We test the efficacy of our proposed randomized acquisition geometry for time-lapse survey on two different models. In the first example, model properties does not change with time, while in the second example, model exhibit a time-lapse effect which may be caused by the migration of fluid within the reservoir. We perform two types of randomized sampling - uniform randomized sampling and jittered sampling to visualize the effects of non-repeatability in time-lapse survey. We observe that jittered randomized sampling is a more efficient method compared to randomized sampling, due to it{\textquoteright}s requirement to control the maximum spacing between the receivers. The results are presented, in the image space, as a least-squares migration of the model perturbation and they are shown for a subset of a synthetic model - the Marmousi model},
	keywords = {Acquisition, migration, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2013/oghenekohwo2013SEGtlswrs/oghenekohwo2013SEGtlswrs.pdf},
	author = {Felix Oghenekohwo 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}
}
@article {herrmann2010GEOPrsg,
	title = {Randomized sampling and sparsity: getting more information from fewer samples},
	journal = {Geophysics},
	volume = {75},
	number = {6},
	year = {2010},
	month = {12},
	pages = {WB173-WB187},
	publisher = {SEG},
	abstract = {Many seismic exploration techniques rely on the collection of massive data volumes that are subsequently mined for information during processing. Although 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 among these is the so-called {\textquotedblleft}curse of dimensionality{\textquotedblright} 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 continue to increase. We offer an alternative sampling method leveraging recent insights from compressive sensing toward seismic acquisition and processing for data that are traditionally considered to be undersampled. The main outcome of this approach is a new technology where acquisition and processing related costs are no longer determined by overly stringent sampling criteria, such as Nyquist. 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 transform-domain sparsity. Now, costs no longer grow significantly with resolution and dimensionality of the survey area, but instead depend only on transform-domain sparsity. Our contribution is twofold. First, we demonstrate by means of carefully designed numerical experiments that compressive sensing can successfully be adapted to seismic exploration. Second, we show that accurate recovery can be accomplished for compressively sampled data volumes sizes that exceed the size of conventional transform-domain data volumes by only a small factor. Because compressive sensing combines transformation and encoding by a single linear encoding step, this technology is directly applicable to acquisition and to dimensionality reduction during processing. In either case, sampling, storage, and processing costs scale with transform-domain sparsity. We illustrate this principle by means of number of case studies.},
	keywords = {Acquisition, Compressive Sensing, data acquisition, geophysical techniques, Nyquist criterion, Optimization, sampling methods, seismology, SLIM},
	doi = {10.1190/1.3506147},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2010/herrmann2010GEOPrsg/herrmann2010GEOPrsg.pdf},
	author = {Felix J. Herrmann}
}
@article {herrmann2008GEOPcbs,
	title = {Curvelet-based seismic data processing: a multiscale and nonlinear approach},
	journal = {Geophysics},
	volume = {73},
	number = {1},
	year = {2008},
	month = {03},
	pages = {A1-A5},
	publisher = {SEG},
	abstract = {Mitigating missing data, multiples, and erroneous migration amplitudes are key factors that determine image quality. Curvelets, little {\textquoteleft}{\textquoteleft}plane waves,{\textquoteright}{\textquoteright} complete with oscillations in one direction and smoothness in the other directions, sparsify a property we leverage explicitly with sparsity promotion. With this principle, we recover seismic data with high fidelity from a small subset (20\%) of randomly selected traces. Similarly, sparsity leads to a natural decorrelation and hence to a robust curvelet-domain primary-multiple separation for North Sea data. Finally, sparsity helps to recover migration amplitudes from noisy data. With these examples, we show that exploiting the curvelet{\textquoteright}s ability to sparsify wavefrontlike features is powerful, and our results are a clear indication of the broad applicability of this transform to exploration seismology. \copyright2008 Society of Exploration Geophysicists},
	keywords = {Acquisition, curvelet transform, Processing, SLIM},
	doi = {10.1190/1.2799517},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/herrmann08GEOcbs/herrmann08GEOcbs.pdf},
	author = {Felix J. Herrmann and Deli Wang and Gilles Hennenfent and Peyman P. Moghaddam}
}
@article {hennenfent2008GEOPnii,
	title = {New insights into one-norm solvers from the Pareto curve},
	journal = {Geophysics},
	volume = {73},
	number = {4},
	year = {2008},
	month = {07},
	pages = {A23-A26},
	abstract = {Geophysical inverse problems typically involve a trade off between data misfit and some prior. Pareto curves trace the optimal trade off between these two competing aims. These curves are commonly used in problems with two-norm priors where they are plotted on a log-log scale and are known as L-curves. For other priors, such as the sparsity-promoting one norm, Pareto curves remain relatively unexplored. We show how these curves lead to new insights in one-norm regularization. First, we confirm the theoretical properties of smoothness and convexity of these curves from a stylized and a geophysical example. Second, we exploit these crucial properties to approximate the Pareto curve for a large-scale problem. Third, we show how Pareto curves provide an objective criterion to gauge how different one-norm solvers advance towards the solution.},
	keywords = {Acquisition, Geophysics, Optimization, Pareto, Processing, SLIM},
	doi = {10.1190/1.2944169},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/hennenfent08GEOnii/hennenfent08GEOnii.pdf},
	author = {Gilles Hennenfent and Ewout van den Berg and Michael P. Friedlander and Felix J. Herrmann}
}
@article {herrmann2008GJInps,
	title = {Non-parametric seismic data recovery with curvelet frames},
	journal = {Geophysical Journal International},
	volume = {173},
	year = {2008},
	month = {04},
	pages = {233-248},
	abstract = {Seismic data recovery from data with missing traces on otherwise regular acquisition grids forms a crucial step in the seismic processing flow. For instance, unsuccessful recovery leads to imaging artifacts and to erroneous predictions for the multiples, adversely affecting the performance of multiple elimination. A non-parametric transform-based recovery method is presented that exploits the compression of seismic data volumes by recently developed curvelet frames. The elements of this transform are multidimensional and directional and locally resemble wavefronts present in the data, which leads to a compressible representation for seismic data. This compression enables us to formulate a new curvelet-based seismic data recovery algorithm through sparsity-promoting inversion. The concept of sparsity-promoting inversion is in itself not new to geophysics. However, the recent insights from the field of {\textquoteleft}compressed sensing{\textquoteright} are new since they clearly identify the three main ingredients that go into a successful formulation of a recovery problem, namely a sparsifying transform, a sampling strategy that subdues coherent aliases and a sparsity-promoting program that recovers the largest entries of the curvelet-domain vector while explaining the measurements. These concepts are illustrated with a stylized experiment that stresses the importance of the degree of compression by the sparsifying transform. With these findings, a curvelet-based recovery algorithms is developed, which recovers seismic wavefields from seismic data volumes with large percentages of traces missing. During this construction, we benefit from the main three ingredients of compressive sampling, namely the curvelet compression of seismic data, the existence of a favorable sampling scheme and the formulation of a large-scale sparsity-promoting solver based on a cooling method. The recovery performs well on synthetic as well as real data and performs better by virtue of the sparsifying property of curvelets. Our results are applicable to other areas such as global seismology.},
	keywords = {Acquisition, curvelet transform, reconstruction, SLIM},
	doi = {10.1111/j.1365-246X.2007.03698.x},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalJournalInternational/2008/herrmann2008GJInps.pdf},
	author = {Felix J. Herrmann and Gilles Hennenfent}
}
@article {hennenfent2008GEOPsdw,
	title = {Simply denoise: wavefield reconstruction via jittered undersampling},
	journal = {Geophysics},
	volume = {73},
	number = {3},
	year = {2008},
	month = {05},
	pages = {V19-V28},
	publisher = {SEG},
	abstract = {In this paper, we present a new discrete undersampling scheme designed to favor wavefield reconstruction by sparsity-promoting inversion with transform elements that are localized in the Fourier domain. Our work is motivated by empirical observations in the seismic community, corroborated by recent results from compressive sampling, which indicate favorable (wavefield) reconstructions from random as opposed to regular undersampling. As predicted by theory, random undersampling renders coherent aliases into harmless incoherent random noise, effectively turning the interpolation problem into a much simpler denoising problem. A practical requirement of wavefield reconstruction with localized sparsifying transforms is the control on the maximum gap size. Unfortunately, random undersampling does not provide such a control and the main purpose of this paper is to introduce a sampling scheme, coined jittered undersampling, that shares the benefits of random sampling, while offering control on the maximum gap size. Our contribution of jittered sub-Nyquist sampling proves to be key in the formulation of a versatile wavefield sparsity-promoting recovery scheme that follows the principles of compressive sampling. After studying the behavior of the jittered undersampling scheme in the Fourier domain, its performance is studied for curvelet recovery by sparsity-promoting inversion (CRSI). Our findings on synthetic and real seismic data indicate an improvement of several decibels over recovery from regularly-undersampled data for the same amount of data collected.},
	keywords = {Acquisition, Compressive Sensing, Geophysics, Optimization, Processing, sampling, SLIM},
	doi = {10.1190/1.2841038},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/hennenfent08GEOsdw/hennenfent08GEOsdw.pdf},
	html_version = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/hennenfent08GEOsdw/paper_html/paper.html},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
