Time-jittered ocean bottom seismic acquisition

Wason, Haneet; Herrmann, Felix J.

doi:10.1190/segam2013-1391.1

Cited by 20 publications

(4 citation statements)

References 21 publications

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“…The JRM which derives from distributed compressive sensing (DCS) (Baron et al, 2009), recovers a common part and innovations with respect to the common part, leading to improved recovery of time-lapse vintages when the surveys are not replicated. The authors confirmed this observation using an example of a CS-based acquisition design-time-jittered sources in marine (Wason and Herrmann, 2013). However, accurate recovery of seismic data in CS hinges on precise knowledge of the acquisition parameters including the relative precise information on source/receiver coordinates.…”

Section: Introductionmentioning

confidence: 77%

“…We consider a fixed spread seismic data acquisition with ocean bottom receivers, using time-jittered sources-a CS inspired acquisition design. Here, a receiver gather, for instance, shows overlapping shots that are unraveled and interpolated during the seismic data recovery stage (Wason and Herrmann, 2013). In the noise-free case and without calibration, Oghenekohwo et al (2014) and Wason et al (2015) showed that independent and non-replicated acquisitions lead to improved recovery of the time-lapse vintages using the JRM over IRS.…”

Section: Introductionmentioning

confidence: 99%

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Improved Time-lapse Data Repeatability with Randomized Sampling and Distributed Compressive Sensing

Herrmann¹,

Oghenekohwo²

2017

Proceedings

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Recently, new ideas on randomized sampling for time-lapse seismic acquisition have been proposed to address some of the challenges of replicating time-lapse surveys. These ideas, which stem from distributed compressed sensing (DCS) led to the birth of a joint recovery model (JRM) for processing time-lapse data (noise-free) acquired from non-replicated acquisition geometries. However, when the earth does not change-i.e. no time-lapse-the recovered vintages from two non-replicated surveys should show high repeatability measured in terms of normalized RMS, which is a standard metric for quantifying time-lapse data repeatability. Under this assumption of no time-lapse change, we demonstrate improved repeatability (with JRM) of the recovered data from non-replicated random samplings, first with noisy data and secondly in situations where there are calibration errors i.e., where the acquisition parameters such as source/receiver coordinates are not precise.

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Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Improved Time-lapse Data Repeatability with Randomized Sampling and Distributed Compressive Sensing

Herrmann¹,

Oghenekohwo²

2017

Proceedings

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“…This requires a randomized sampling scheme to break the sparsity in the transform domain. There has been efforts to randomize the acquisition of seismic data (Wason and Herrmann, 2013) and it requires special acquisition design. This means vintage data that already has been shot does not satisfy the underlying assumption in sparse signal recovery.…”

Section: Introductionmentioning

confidence: 99%

Seismic Data Reconstruction with Generative Adversarial Networks

2018

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A main challenge in seismic imaging is acquiring densely sampled data. Compressed Sensing has provided theoretical foundations upon which desired sampling rate can be achieved by applying a sparsity promoting algorithm on sub-sampled data. The key point in successful recovery is to deploy a randomized sampling scheme. In this paper, we propose a novel deep learning-based method for fast and accurate reconstruction of heavily under-sampled seismic data, regardless of type of sampling. A neural network learns to do reconstruction directly from data via an adversarial process. Once trained, the reconstruction can be done by just feeding the frequency slice with missing data into the neural network. This adaptive nonlinear model makes the algorithm extremely flexible, applicable to data with arbitrarily type of sampling. With the assumption that we have access to training data, the quality of reconstructed slice is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method.

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“…By acquiring seismic data with simultaneous sources, larger areas can be covered in shorter acquisition times (Beasley, 2008). Wason and Herrmann (2013) have shown that data can be deblended with sparsity promotion in the curvelet domain. Since our final goal is to reconstruct up-and down-going S-waves, we implement the source separation and decomposition in a one-step approach by solving a sparsity promoting joint source separation decomposition.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of S-waves from low-cost randomized and simultaneous acquisition by joint sparse inversion

Alfaraj

Herrmann

Kumar

2017

SEG Technical Program Expanded Abstracts 2017

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Spatial Nyquist sampling rate, which is proportional to the apparent subsurface velocity, is the key deciding cost factor for conventional seismic acquisition. Due to the lower shear wave velocity compared with compressional waves, finer spatial sampling is required to properly record the earlier, which increases the acquisition costs. This is one of the reasons why shear waves are usually not considered in practice. To avoid having the Nyquist criterion as the deciding cost factor and to utilize multicomponent data to its available full extent, we propose acquiring randomly undersampled ocean bottom seismic data. Each component can then be interpolated separately, followed by elastic decomposition to recover up-and down-going S-waves. Instead, we jointly interpolate and decompose the recorded multicomponent data by solving one sparsity promoting optimization problem. This way we ensure that the relative amplitudes of the multicomponent data is preserved. We compare two sparsifying transforms: the curvelet transform and the frequency-wavenumber transform. Another key cost deciding factor for seismic acquisition is the efficiency of acquiring data. This calls for simultaneous acquisition, which requires a source separation step. Similarly, instead of taking a two-step approach, we perform a sparsity-promoting joint source separation decomposition. Results on economically and efficiently acquired synthetic data of both joint methods show their ability of reconstructing accurate up-and down-going S-waves.

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Time-jittered ocean bottom seismic acquisition

Cited by 20 publications

References 21 publications

Improved Time-lapse Data Repeatability with Randomized Sampling and Distributed Compressive Sensing

Improved Time-lapse Data Repeatability with Randomized Sampling and Distributed Compressive Sensing

Seismic Data Reconstruction with Generative Adversarial Networks

Reconstruction of S-waves from low-cost randomized and simultaneous acquisition by joint sparse inversion

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