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.

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 “low variability” in source firing times, i.e., [Formula: see text] 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.

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 leastsquares 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 the one of the sources.

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Irregular or off-the-grid spatial sampling of sources and receivers is inevitable in field seismic acquisitions. Consequently, time-lapse surveys become particularly expensive because current practices aim to replicate densely sampled surveys for monitoring changes occurring in the reservoir due to hydrocarbon production. We have determined 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 with 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 they 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 the same source signature) for the time-lapse surveys and no changes in wave heights, water column velocities or temperature, and salinity profiles.