Source deghosting of coarsely sampled common-receiver data using a convolutional neural network

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.

Section: Methodsmentioning

confidence: 99%

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

Jonge

Vinje

Poole³

et al. 2023

“…Moreover, this paper focuses on the receiver ghost problem, but the source ghost problem can also be handled in a similar way as the source and receiver ghosts share similar characteristics. The main difference will be to apply our method on receiver gathers instead of common shot gathers (Vrolijk & Blacquière, 2021) and expect Equation ( 12) to be applied for ghost notches at higher frequencies, as the source depth is generally shallower than the streamer depth.…”

Section: Overlappingmentioning

confidence: 99%

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

Zizi,

Turquais,

Day

et al. 2024

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.

“…Processing‐based solutions include, but are not limited to, deriving a low‐frequency deghosting filter (Amundsen & Zhou, 2013) and joint deconvolution (Soubaras, 2010). Recently, Almuteri and Sava (2021) and Vrolijk and Blacquière (2021) have also utilized machine learning applications for removing ghost reflections from seismic data.…”

Section: Introductionmentioning

confidence: 99%

Attenuating free‐surface multiples and ghost reflection from seismic data using a trace‐by‐trace convolutional neural network approach

Kiraz,

Snieder,

Sheiman

2023

The presence of the air‐water interface (or free‐surface) creates two major problems in marine seismic data for conventional seismic processing and imaging: free‐surface multiples and ghost reflections. The attenuation of free‐surface multiples remains one of the most challenging noise attenuation problems in seismic data processing. Current solutions suffer from the removal of the primary events along with the multiple events especially when the primary and multiple events overlap (e.g., adaptive subtraction). The effective attenuation of ghost reflections (or deghosting) requires acquisition‐ and/or processing‐related solutions which generally address the source‐side and receiver‐side ghosts separately. Additionally, an essential requirement for a successful implementation of free‐surface multiple attenuation and seismic dehosting is the requirement of dense seismic data acquisition parameters which is not realistic for 2D and/or 3D marine cases. We present a Convolutional Neural Network (CNN) approach for free‐surface multiple attenuation and seismic deghosting. Unlike the existing solutions, our approach operates on a single trace at a time, and neither relies on the dense acquisition parameters nor requires a subtraction process to eliminate free‐surface multiples, and it removes both the source ghost and receiver ghost simultaneously. We train a network using subsets of the Marmousi and Pluto velocity models, and make predictions using subsets of the Sigsbee velocity model. We show that the CNN predictions give a correlation coefficient of 0.97 on average with the numerically modeled data for the synthetic examples. We illustrate the efficacy of our CNN‐based technique using the Mobil AVO Viking Graben field data set. The application of our algorithm demonstrates that our CNN‐based approach removes different orders of free‐surface multiples (e.g., 1 and 2 orders) and recovers the low‐frequency content of the seismic data (which is essential for, for instance, full‐waveform inversion applications and broadband processing) by successfully removing the ghost reflections while preserving and increasing the continuity of the primary reflections.This article is protected by copyright. All rights reserved