Robust<i>f</i>‐<i>x</i>projection filtering for simultaneous random and erratic seismic noise attenuation

Chen, Ke; Sacchi, Mauricio D.

doi:10.1111/1365-2478.12429

Cited by 50 publications

(6 citation statements)

References 48 publications

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“…For random noise, we can preprocess and remove the random noise in CSG using the noise suppression method in transform domains (Herrmann et al., 2007b). For swell noise, we can preprocess and remove it in the CSG using the robust f – x projection filtering (Chen & Sacchi, 2017) or dictionary learning (Vaezi & Kazemi, 2016) methods. Signature variations do not affect our proposed method for distinguishing continuous signals from discrete crosstalk noises.…”

Section: Discussionmentioning

confidence: 99%

A multi‐data training method for a deep neural network to improve the separation effect of simultaneous‐source data

Wang

Mao

Song

et al. 2022

Geophysical Prospecting

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Within the field of seismic data acquisition with active sources, the technique of acquiring simultaneous data, also known as blended data, offers operational advantages. The preferred processing of blended data starts with a step of deblending, that is separation of the data acquired by the different sources, to produce data that mimic data from a conventional seismic acquisition and can be effectively processed by standard methods. Recently, deep learning methods based on the deep neural network have been applied to the deblending task with promising results, in particular using an iterative approach. We propose an enhancement to deblending with an iterative deep neural network, whereby we modify the training stage of the deep neural network in order to achieve better performance through the iterations. We refer to the method that only uses the blended data as the input data as the general training method. Our new multi-data training method allows the deep neural network to be trained by the data set with the input patches composed of blended data, noisy data with low amplitude crosstalk noise, and unblended data, which can improve the ability of the deep neural network to remove crosstalk noise and protect weak signal. Based on such an extended training data set, the multi-data training method embedded in the iterative separation framework can result in different outputs at different iterations and converge to the best result in a shorter iteration number. Transfer learning can further improve the generalization and separation efficacy of our proposed method to deblend the simultaneous-source data. Our proposed method is tested on two synthetic data and two field data to prove the effectiveness and superiority in the deblending of the simultaneous-source data compared with the general training method, generic noise attenuation network and low-rank matrix factorization methods.

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Section: Discussionmentioning

confidence: 99%

A multi‐data training method for a deep neural network to improve the separation effect of simultaneous‐source data

Wang

Mao

Song

et al. 2022

Geophysical Prospecting

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“…Canales and Lu (1993) first time proved the feasibility of predictive filtering technology in seismic data denoising field. Chen and Sacchi (2017) proposed a predictive filtering approach to simultaneously suppress mixed noises. This approach utilizes the hybrid L1/L2 norm to design a robust M-estimate of a special autoregressive moving-average model.…”

Section: Introductionmentioning

confidence: 99%

Prestack seismic random noise attenuation using the wavelet-inspired invertible network with atrous convolutions spatial pyramid

Wu²,

Xu³

2023

Front. Earth Sci.

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Convolutional Neural Network (CNN) is widely used in seismic data denoising due to its simplicity and effectiveness. However, traditional seismic denoising methods based on CNN ignore multi-scale features of seismic data in the wavelet domain. The lack of these features will decrease the accuracy of denoising results. To address this barrier, a seismic denoise method based on the wavelet-inspired invertible network with atrous convolutions spatial pyramid (WINNet_ACSP) is proposed. WINNet_ACSP follows the principle of lifting wavelet transform. The proposed method utilizes the redundant orthogonal wavelet transform to obtain frequency multi-scale information from noisy seismic data. Then predict update network (PUNet) extracts spatial multi-scale features of approximate and detailed parts. The sparse driven network (SDN) learns the complex multi-scale information and obtains sparse features. These sparse features are processed to eliminate random noise. Compared to standard convolution, the atrous convolutions spatial pyramid (ACSP) can extract more features. The redundant features are the key to ensure the precision of multi-scale information. Therefore, the introduction of ACSP in PUNet can guarantee the denoising effect of the network. WINNet_ASCP combines the characteristics of wavelet transform and neural network and has a high generalization. Besides, transfer learning is used to overcome the difficulty caused by the training sample size of seismic data. The training process includes pre-training and post-training. The former is trained to obtain the initial denoising network by natural image samples. The latter is trained with a small sample of seismic data to enhance stratigraphic continuity. Finally, the proposed method is tested with synthetic and field data. The experimental results show that the proposed method can effectively remove random noise and reduce the loss of detailed information in prestack seismic data. In the future, we will make further improvements on this basis and conduct experiments on 3D prestack data.

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“…It is possible to construct a convex optimization problem for simultaneously obtaining the low-rank component, that is, the signal and the sparse erratic noise. Chen and Sacchi (2017) further developed an effective method to simultaneously remove erratic and random noise. The estimation of the prediction error filter and the additive noise are performed in an alternating fashion.…”

Section: Introductionmentioning

confidence: 99%

Erratic noise suppression using iterative structure‐oriented space‐varying median filtering with sparsity constraint

Huang

Zhao

Chen

et al. 2020

Geophysical Prospecting

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Erratic noise often has high amplitudes and a non-Gaussian distribution. Leastsquares-based approaches therefore are not optimal. This can be handled better with non-least-squares approaches, for example based on Huber norm which is computationally expensive. An alternative method has been published which involves transforming the data with erratic noise to pseudodata that have Gaussian distributed noise. It can then be attenuated using traditional least-squares approaches. This alternative method has previously been used in combination with a curvelet transform in an iterative scheme. In this paper, we introduce a median-filtering step in this iterative scheme. The median filter is applied following the slope direction of the seismic data to maximally preserve the energy of useful signals. The new method can suppress stronger erratic noise compared with the previous iterative method, and can better deal with random noise compared with the single-step implementation of the median filter. We apply the proposed robust denoising algorithm to a synthetic dataset and two field data examples and demonstrate its advantages over three different noise attenuation algorithms.

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Robustf‐xprojection filtering for simultaneous random and erratic seismic noise attenuation

Cited by 50 publications

References 48 publications

A multi‐data training method for a deep neural network to improve the separation effect of simultaneous‐source data

A multi‐data training method for a deep neural network to improve the separation effect of simultaneous‐source data

Prestack seismic random noise attenuation using the wavelet-inspired invertible network with atrous convolutions spatial pyramid

Erratic noise suppression using iterative structure‐oriented space‐varying median filtering with sparsity constraint

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