A Multidirectional Deep Neural Network for Self-Supervised Reconstruction of Seismic Data

Abedi, Mohammad Mahdi; Pardo, David

doi:10.1109/tgrs.2022.3227212

Cited by 13 publications

(3 citation statements)

References 35 publications

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“…The demonstrated method resulted in accurate near offset reconstruction using a relatively simple machine learning model (the U‐Net CNN). In recent years, more sophisticated methods have been developed for image reconstruction tasks, such as improvements on the U‐Net architecture like the U‐Net3+ (Huang et al., 2020), multidirectional CNNs (Abedi & Pardo, 2022), and vision transformers (Ali et al., 2023). So although the main focus of this study was on the geophysical workflow of utilizing high‐quality synthetic data and interpolation in the CMP domain, future improvements could potentially be made by utilizing the same workflow with more advanced machine learning models.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, there has been an increase in the usage of deep learning methods for seismic interpolation and near offset reconstruction, where neural networks are trained to interpolate or generate missing traces. Examples of this have included a generative adversarial network for trace interpolation (Kaur et al, 2021), a convolutional neural network (CNN) for near offset extrapolation at shallow subsurface depths (<0.1 s of traveltime) (Qu et al, 2021), a CNN for interpolation of successively sampled seismic data (Li et al, 2023) and a multidirectional CNN for self-supervised reconstruction of gaps in seismic data (Abedi & Pardo, 2022). One of the main challenges of a deep learning approach is in creating or finding training data with rich near offset information, as source-over-cable field data is a rarity, and synthetic training data often lacks the heterogeneity and complexity of real seismic data.…”

Section: Introductionmentioning

confidence: 99%

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Near offset reconstruction for marine seismic data using a convolutional neural network

Huff,

Thorkildsen,

Greiner

et al. 2024

Geophysical Prospecting

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Marine seismic data is often missing near offset information due to separation between the source and receiver cables. To solve this problem, a convolutional neural network is trained on synthetic seismic data to reconstruct the near offset gap. The synthetic data is created using a two‐dimensional finite difference method within a heterogeneous velocity model. These synthetics are generated with a source‐over‐receiver acquisition geometry so that they contain complete near offset data. The convolutional neural network is then trained on input‐target synthetic pairs where the inputs are common midpoint gathers with the near offset section removed, and the targets are the same gathers with the near offset section retained. Following training, the robustness of the method is investigated with regards to common midpoint data sorting, normal moveout correction and changes in the velocity model. It is found that training on common midpoint‐sorted data results in 2.8 times lower error than training on shot gathers, that normal moveout correction of the training data makes no significant difference in error levels, and that the model can reconstruct realistic near offsets on synthetic data generated 10 km away within the heterogeneous velocity model. In field data testing, first a dataset with source‐over‐cable acquisition geometry from the Barents Sea is used to compare the reconstructed wavefields to ground truth values. Although the reconstructed amplitudes require minor scaling to match the true values, predictions on this dataset yield 2.5 times lower near offset reconstruction error compared to a simple Radon transform interpolation method. Furthermore, amplitude versus offset gradient and intercept sections from the Barents Sea dataset are estimated with half the error when including the convolutional neural network‐predicted near offset data, compared to only using the conventionally‐acquirable portion of the data (beyond 112.5 m of offset). In a secondary field data test, a conventional northern North Sea dataset is used to demonstrate how the method may be applied in practice. Here, the convolutional neural network generates more realistic predictions than the Radon method, and the gradient and intercept sections calculated using the convolutional neural network‐predicted traces have higher signal‐to‐noise ratios than the sections calculated using only the original data. The combination of high‐quality synthetic training data and interpolation in the common midpoint domain enables near offset reconstruction at significant depth (1 s of two‐way traveltime or more), which is demonstrated in both synthetic and field examples.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Near offset reconstruction for marine seismic data using a convolutional neural network

Huff,

Thorkildsen,

Greiner

et al. 2024

Geophysical Prospecting

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“…We design a U-net with convolutional blocks and skip connections following Ronneberger et al (2015), Isola et al (2017), and Abedi and Pardo (2022). The skip connections facilitate the flow of low-level information, avoid information loss during the downsampling process and stabilize the training process by mitigating the vanishing/exploding gradient problem.…”

Section: Blind-trace Denoisingmentioning

confidence: 99%

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

Abedi,

Pardo,

Alkhalifah

2023

Geophysical Prospecting

Self Cite

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Trace‐wise noise is a type of noise often seen in seismic data, which is characterized by vertical coherency and horizontal incoherency. Using self‐supervised deep learning to attenuate this type of noise, the conventional blind‐trace deep learning trains a network to blindly reconstruct each trace in the data from its surrounding traces; it attenuates isolated trace‐wise noise but causes signal leakage in clean and noisy traces and reconstruction errors next to each noisy trace. To reduce signal leakage and improve denoising, we propose a new loss function and masking procedure in a semi‐blind‐trace deep learning framework. Our hybrid loss function has weighted active zones that cover masked and non‐masked traces. Therefore, the network is not blinded to clean traces during their reconstruction. During training, we dynamically change the masks' characteristics. The goal is to train the network to learn the characteristics of the signal instead of noise. The proposed algorithm enables the designed U‐net to detect and attenuate trace‐wise noise without having prior information about the noise. A new hyperparameter of our method is the relative weight between the masked and non‐masked traces' contribution to the loss function. Numerical experiments show that selecting a small value for this parameter is enough to significantly decrease signal leakage. The proposed algorithm is tested on synthetic and real off‐shore and land datasets with different noises. The results show the superb ability of the method to attenuate trace‐wise noise while preserving other events. An implementation of the proposed algorithm as a Python code is also made available.This article is protected by copyright. All rights reserved

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A comparative study over improved fast iterative shrinkage-thresholding algorithms: an application to seismic data reconstruction

Khatami,

Riahi,

Abedi

et al. 2024

Stud Geophys Geod

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A Multidirectional Deep Neural Network for Self-Supervised Reconstruction of Seismic Data

Cited by 13 publications

References 35 publications

Near offset reconstruction for marine seismic data using a convolutional neural network

Near offset reconstruction for marine seismic data using a convolutional neural network

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

A comparative study over improved fast iterative shrinkage-thresholding algorithms: an application to seismic data reconstruction

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