@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}
}
@article {oghenekohwo2017hrt,
	title = {Highly repeatable time-lapse seismic with distributed Compressive Sensing{\textendash}-mitigating effects of calibration errors},
	journal = {The Leading Edge},
	volume = {36},
	number = {8},
	year = {2017},
	note = {(The Leading Edge)},
	month = {08},
	pages = {688-694},
	abstract = {Recently, we demonstrated that combining joint recovery with low-cost non-replicated randomized sampling tailored to time-lapse seismic can give us access to high fidelity, highly repeatable, dense prestack vintages, and high-grade time-lapse. To arrive at this result, we assumed well-calibrated surveys{\textendash}-i.e., we presumed accurate post-plot source/receiver positions. Unfortunately, in practice seismic surveys are prone to calibration errors, which are unknown deviations between actual and post-plot acquisition geometry. By means of synthetic experiments, we analyze the possible impact of these errors on vintages and on time-lapse data obtained with our joint recovery model from compressively sampled surveys. Supported by these experiments, we demonstrate that highly repeatable time-lapse vintages are attainable despite the presence of unknown calibration errors in the positions of the shots. We assess the repeatability quantitatively for two scenarios by studying the impact of calibration errors on conventional dense but irregularly sampled surveys and on low-cost compressed surveys. To separate time-lapse effects from calibration issues, we consider the idealized case where the subsurface remains unchanged and the practical situation where time-lapse changes are restricted to a subset of the data. In both cases, the quality of the recovered vintages and time-lapse decreases gracefully for low-cost compressed surveys with increasing calibration errors. Conversely, the quality of vintages from expensive densely periodically sampled surveys decreases more rapidly as unknown and difficult to control calibration errors increase.},
	keywords = {calibration errors, joint-recovery method, marine, random sampling, time-lapse seismic},
	doi = {10.1190/tle36080688.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2017/oghenekohwo2017hrt/oghenekohwo2017hrt.html},
	author = {Felix Oghenekohwo 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}
}
@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}
}
