Evolution of deghosting process for single‐sensor streamer data from 2D to 3D

Deghosting is an important technique in the marine seismic industry, as it plays a crucial role in mitigating the effects of ghost reflections from the sea surface, which can significantly impact the accuracy and resolution of subsurface imaging. In recent years, various acquisition‐based techniques have been developed to tackle the challenge of removing receiver–ghost reflections, which is the focus of our paper. These state‐of‐the‐art approaches, such as dual‐sensor or multicomponent towed streamer acquisitions, have demonstrated exceptional accuracy by combining pressure and particle motion data. However, such methods face limitations when dealing with low frequencies due to heavy noise contamination in the particle motion data. Consequently, ghost‐free data reconstruction at low frequencies typically relies on processing‐based approaches, which exclusively utilize recorded pressure data. This study presents a novel deghosting method for low‐frequency applications based on parabolic dictionary learning, which relies solely on recorded pressure data. The proposed method has the advantage of being applicable directly in a compressed domain, eliminating the need for data decompression prior to the deghosting process when compression is applied before the processing steps. This not only reduces costs related to data storage and transfer but also provides a cost‐effective alternative to conventional deghosting by operating directly on the compressed data format, which is smaller in size. The effectiveness of the proposed method was evaluated using both synthetic and field datasets. The results obtained from a synthetic data example indicate that the proposed method achieves similar results to an industry‐standard frequency–wavenumber method, while achieving a compression rate of over 7. Furthermore, the method was tested using a field dataset consisting of a full sail‐line of marine seismic acquisition. The comparison of 2D pre‐stack migrated images between the proposed method and the industry‐standard frequency–wavenumber method revealed insignificant differences, while achieving a compression ratio higher than 5 when our method was used.

Section: Deghosting Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low‐frequency seismic deghosting in a compressed domain using parabolic dictionary learning

Zizi,

Turquais,

Day

et al. 2024

“…In an ideal case of RTM, seismic data free of surface reflection are extrapolated from receivers, and purely down‐going waves are directly extrapolated from the source. This pre‐processing procedure is also referred to as the deghost processing (Zhang et al., 2018), which can be implemented in various ways, for example the deghost processing in τ‐p domain (Wang et al., 2013). On the other hand, as the development of advanced acquisition technology, the dual‐sensor streamers can record both acoustic pressures and vertical particle velocities at the same location, as well as 4C datasets are available with ocean‐bottom cables or nodes.…”

Section: Introductionmentioning

confidence: 99%

The effect of free‐surface reflections on depth images for acquisition with buried sources and receivers: Investigation through the point spread function#

Han

Xie

2023

In reverse time migration, if sources or receivers are buried at certain depths, the interference between the primary wave and the free‐surface reflection causes losing spectral contents in seismic data. If they are not properly compensated, errors will be carried to the target during the reverse time migration and cause a distorted image by so‐called ghost image. By invoking the point spread function, we investigate how distorted seismic data are mapped to the subsurface. Quantitative relations between the free‐surface parameters (i.e. source/receiver depth, free‐surface incident angle and near‐surface velocity) and wavenumber‐domain illumination can be created, which map missing frequency contents in seismic data to missing wavenumber contents in the point spread function. After transformed back to the space domain, we can investigate its effect on the image distortion. Numerical calculations expand the above approach to handle complex overburden structures and more realistic acquisition geometries. The proposed method provides us with a useful tool to investigate deghosting related problems, for example optimizing parameters of acquisition geometry, evaluating capabilities of existing acquisition systems and data processing technologies in suppressing the free‐surface reflection effect in the depth image, or developing new deghosting approaches.

“…For hydrophone‐only data, the simplest deghosting method uses the far‐field source signature and deterministic deconvolution (Jovanovich et al., 1983), assuming vertical ray paths. Recently, more advanced deghosting methods have been developed, such as frequency–slowness domain inversion (Zhang et al., 2018),

\tau - p

domain inversion (King & Poole, 2015; Masoomzadeh & Woodburn, 2013; Poole, 2013; Poole & Cooper, 2018; Rickett et al., 2014), inverse Fourier transform deghosting (Amundsen & Zhou, 2013), space‐domain deghosting based on Green's functions (Amundsen, Zhou, et al., 2013; Amundsen, Weglein, et al., 2013) or joint deconvolution of a migration and a mirror migration (Soubaras, 2010).…”

Section: Introductionmentioning

confidence: 99%

Deghosting dual‐component streamer data using demigration‐based supervised learning

Jonge

Vinje

Poole³

et al. 2023

Ghost reflections from the free surface distort the source signature and generate notches in the seismic amplitude spectrum. For this reason, removing ghost reflections is essential to improve the bandwidth and signal‐to‐noise ratio of seismic data. We have developed a novel approach that involves training a convolutional neural network to remove source and receiver ghosts from marine dual‐component data. High‐quality training data is essential for the network to produce accurate predictions on real data. We have used the demigration of a stacked depth‐migrated image to create training shot gathers. Demigrated pressure and vertical velocity data are used to train the network. We apply the trained network on real pressure and vertical velocity data with ghosts. The network's output may be either source deghosting and receiver deghosting, or both. We test our method on synthetic Marmousi and real North Sea data with dual‐component streamers. The method is compared with conventional dual‐component deghosting using the summation of pressure and vertical velocity. Results show that the method can accurately remove the ghosts with only minor errors in synthetic data. Based on a decimation test, the method is less affected by spatially aliased data than a conventional method, which could benefit data with high frequencies and/or large receiver or cable separations. On real data, the results show consistency with conventional deghosting, both within and outside the training area. This indicates that the method is a viable alternative to conventional methods on real data.