Shooting over the Streamer Spread - A Novel Approach in Seismic Marine Acquisition and Imaging

Vinje, Vetle; Lie, J.E.; Danielsen, V.; Dhelie, P.E.; Siliqi, R.; Nilsen, C.I.; Hicks, Erik; Walters, Cathy; Camerer, A.

doi:10.3997/2214-4609.201700838

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…The data component in Figure 1a represents the desired signal in a noisy shot gather. As we can see, the simulated desired signal has events with hyperbolic curvatures as if it were acquired from a source-over-streamer (Vinje et al, 2017) acquisition. The data component in Figure 1b represents a type of hyperbolic coherent noise, which can be regarded as blending noise from a blended acquisition using a short shot point interval or SI noise coming from the side.…”

Section: Training Datamentioning

confidence: 93%

Improving signal fidelity for deep learning‐based seismic interference noise attenuation

Sun

Hou²

2022

Geophysical Prospecting

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Deep learning has shown a considerable potential to significantly improve processing efficiency but has not yet been widely deployed to production projects of seismic signal separation such as seismic interference attenuation. The main reasons are: First, the industry has high standards for signal fidelity, which are critical for the success of subsequent seismic imaging, and deep neural network methods have not yet matched the required level; second, the network's interpretability issue has affected many geophysicists and sponsors' trust in the deep learning technique. To develop deep neural network methods towards the end of benefiting real-world production, we first attempt to better understand their performance, especially in how they make use of local and global features of the data. A novel quantitative research of the overall network model behaviour on synthetic data is conducted. We simulate three types of coherent seismic data components in the shot domain, blend them together and then train a network to separate them. In this process, random noise, a component having only learnable local features, is selectively injected into the network's training pairs. Three network models sharing the same architecture are trained individually, and they show distinctive behaviours when applied to the same test data. Step-by-step analysis of each of them reveals that training the network with additional random noise injected into both the input and the output channel of the desired signal can lead to a decent prediction of the coherent noise based on good learning of the global features and, in the meantime, preserve almost all the data information from being lost. We propose this key lesson we learnt as a new method to improve the network's signal fidelity for shot-domain seismic interference attenuation, which is essentially a signal separation task. Its effectiveness is demonstrated on field data from Africa with a comparison to a conventional physics-based seismic interference attenuation method used in production.

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Section: Training Datamentioning

confidence: 93%

Improving signal fidelity for deep learning‐based seismic interference noise attenuation

Sun

Hou²

2022

Geophysical Prospecting

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“…The bin centre locations of each of the 3D constant OFCs ranged from 37.5 m to 712.5 m with an increment of 75 m. The size of each OFC was approximately 4 × 13 km with 750‐time samples using a 2 ms temporal sampling. We then modelled a new sparsely sampled data set

{{{\bf d}}_{r,h}},

in which we had the source‐over‐streamer (Vinje et al ., 2017) survey layout shown in Figure 2(a). This layout gave sparsely populated near OFCs with varying offset and azimuth.…”

Section: Synthetic and Real Data Examplesmentioning

confidence: 99%

De‐migration‐based supervised learning for interpolation and regularization of 3D offset classes

Hlebnikov

Greiner

Vinje³

et al. 2022

Geophysical Prospecting

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Regularization and interpolation of 3D offset classes prior to imaging are an important and challenging step in the marine seismic data processing flow. Here we describe how to perform this task using a deep neural network, and we explain how to overcome the challenge of creating a suitable training data set. The training data set is generated by de-migrating stacked pre-stack depth migration images. For each offset class volume, we de-migrate the pre-stack depth migrated stacked image into two configurations: (i) the original survey configuration consisting of the recorded source/receiver positions and (ii) an 'Ideal' survey configuration with constant offset and azimuth for each 3D offset class. The training creates a 3D convolutional encoder-decoder model that will regularize and interpolate seismic data. The convolutional encoderdecoder is trained on 3D sliding windows in each 3D offset cube to map from (i) to (ii), i.e. to map the original survey configuration with irregular and sparse sampling into the fully sampled regular offset cubes suitable for offset-based migration, such as Kirchhoff migration. Such migration algorithms rely on regular and sufficiently dense sampling to achieve constructive interference to image the structures and destructive interference to suppress migration noise. We test the new method on one synthetic and one field data example and show that it performs better than a standard regularization/interpolation method based on anti-leakage Fourier transform, especially for the smallest offset classes. On the synthetic data, we also demonstrate that the convolutional encoder-decoder method preserves the amplitude versus offset as well as the standard method.

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“…This survey was acquired with an unconventional marine split-spread geometry denoted as TopSeis. For a description of the TopSeis acquisition geometry in general and the dataset employed in this work specifically, the reader is referred to Vinje et al (2017) and Thorkildsen (2019) respectively.…”

Section: Data Examplementioning

confidence: 99%

Revisiting holistic migration

Thorkildsen

Gelius

Robinson³

2021

The Leading Edge

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If an optical hologram is broken into pieces, a virtual object can still be reconstructed from each of the fragments. This reconstruction is possible because each diffraction point emits waves that reach every point of the hologram. Thus, the entire object is encoded into each subset of the hologram. Analogous to the broken hologram, the use of undersampled seismic data violating the Nyquist-Shannon sampling theorem may still give a well-resolved image of the subsurface. A theoretical framework of this idea has already been introduced in the literature and denoted as holistic migration. However, the general lack of seismic field data demonstrations has inspired the study presented here. Since the optical hologram is diffraction-driven, we propose to employ diffraction-separated data and not conventional reflection data as input for holistic migration. We follow the original idea and regularly undersample the data spatially. Such a sampling strategy will result in coherent noise in the image domain. We therefore introduce a novel signal processing technique to remove such noise. The feasibility of the proposed approach is demonstrated employing the Sigsbee2a controlled data set and field data from the Barents Sea.

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Shooting over the Streamer Spread - A Novel Approach in Seismic Marine Acquisition and Imaging

Cited by 6 publications

References 3 publications

Improving signal fidelity for deep learning‐based seismic interference noise attenuation

Improving signal fidelity for deep learning‐based seismic interference noise attenuation

De‐migration‐based supervised learning for interpolation and regularization of 3D offset classes

Revisiting holistic migration

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