Seismic data denoising by deep-residual networks

Jin, Yuchen; Wu, Xuqing; Chen, Jiefu; Han, Zhu; Hu, Wenyi

doi:10.1190/segam2018-2998619.1

Cited by 26 publications

(12 citation statements)

References 8 publications

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“…It can obtain valuable information from the data by learning the abstract representation in the form of combination of multiple layers (LeCun et al ., 2015). Currently, label‐based machine learning approaches have been successfully applied to suppress seismic data noise (Zhao et al ., 2018; Liu et al ., 2019; Zhang et al ., 2019b; Si and Yuan, 2018; Liu et al ., 2018; Jin et al ., 2018). According to the strategy for generating labels, these approaches can mainly be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, after learning and extracting features from high signal‐to‐noise ratio (SNR) seismic data labels, it is difficult for the neural network to output better results than the labeled. The second category generates labels by creating synthetic data (Si and Yuan, 2018; Jin et al ., 2018; Zhao et al ., 2018). The advantage of this category is that the labels are completely clean.…”

Section: Introductionmentioning

confidence: 99%

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Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach

Gao

Zhao²,

et al. 2021

Geophysical Prospecting

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Random noise attenuation is an essential step in seismic data processing for improving seismic data quality and signal‐to‐noise ratio. We adopt an unsupervised machine learning approach to attenuate random noise via signal reconstruction strategy. This approach can be accomplished in the following steps: Firstly, we randomly mute a part of the input data of the neural network according to a certain percentage, and then the network outputs the reconstructed data influenced by this randomly mute. The objective function measures the distance between the input data and the reconstructed data. Secondly, we use the adaptive moment estimation algorithm to minimize the distance, and the network adjusts its internal parameters so that sparse representations can be captured by the multiple processing layers of the neural network. Finally, we take the same proportion of random mute on the raw seismic data which are fed to the trained neural network. Through this network, reconstruction of seismic data and attenuation of random noise are completed simultaneously. We use both synthetic and field data to testify the feasibility and applicability of the proposed method. Synthetic data experiment indicates that the proposed method achieves better denoised results than the conventional methods. Field data applications further demonstrate its superiority and practicality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach

Gao

Zhao²,

et al. 2021

Geophysical Prospecting

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“…It also has the advantages of requiring no manual participation and having low computing costs. Therefore, a large number of scholars have begun to try to apply deep neural networks to seismic signal denoising (Jin et al, 2018;Yu et al, 2018;Dong et al, 2020). In 2018, Yu et al (2018) used a multilayer convolutional neural network to perform the self-adaptive denoising of seismic signals containing different kinds of noise.…”

Section: Introductionmentioning

confidence: 99%

“…However, increasing the number of network layers will lead to gradient disappearance and other problems. In the same year, Jin et al (2018) used a new autoencoder based on a deep residual network to achieve the random noise reduction of seismic signals. Their algorithm combined a convolutional autoencoder with the residual network to avoid the problem of gradient disappearance found in the deep network.…”

Section: Introductionmentioning

confidence: 99%

Research on Deep Convolutional Neural Network Time-Frequency Domain Seismic Signal Denoising Combined With Residual Dense Blocks

et al. 2021

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Deep Convolutional Neural Networks (DCNN) have the ability to learn complex features and are thus widely used in the field of seismic signal denoising with low signal-to-noise ratio (SNR). However, the current convolutional deep network used for seismic signal noise reduction does not make full use of the feature information extracted from all convolution layers in the network, and thus cannot fit the seismic signal with high SNR. To deal with this problem, this paper proposes the DnRDB model, a convolutional deep network time-frequency domain seismic signal denoising model combined with residual dense blocks (RDB). The model is mainly composed of several RDB in series. The input of each convolution layer in each RDB module is formed by the output of all the previous convolution layers. Meanwhile, even if the number of layers is increased, the fusion of the seismic signal features learned by the RDB modules can still achieve full extraction of seismic signals. Furthermore, deepening the model structure by concatenating multiple RDB modules enables further useful feature information to be extracted, which improves the SNR of seismic signals. The DnRDB model was trained and tested using the Stanford Global Seismic Dataset. The experimental results show that the DnRDB model can effectively recover seismic signals and remove various forms of noise. Even in the case of high noise, the denoised signal still has a high SNR. When the DnRDB model is compared with other denoising approaches such as wavelet threshold, empirical mode decomposition, and different deep learning methods, the results indicate that it performs best overall in denoising the same segment of the noisy seismic signal; the denoised signal also has less waveform distortion. Use of the DnRDB model in subsequent seismic signal processing work indicates that it can help the phase recognition algorithm improve the accuracy of seismic recognition through noise reduction.

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“…Taking the same strategy as [45] and [46], [47] employed synthetic noise-free records as labels for training a residual learning network [50] to attenuate seismic random noise, but the applications of this method on real data are not good enough. Additionally, [44], [48] and [49] selected denoised data with high SNR by conventional denoising method as labels to train CNN.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Seismic Random Noise Attenuation Based on Deep Convolutional Neural Network

2019

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Random noise attenuation is one of the most essential steps in seismic signal processing. We propose a novel approach to attenuate seismic random noise based on deep convolutional neural network (CNN) in an unsupervised learning manner. First, normalization and patch sampling are required to build training dataset and test dataset from raw noisy data. Instead of using synthetic noise-free data or denoised results via conventional methods as training labels, we adopt only the training set constructed from the raw noisy data as the input and design a robust deep CNN that just relies on the noisy input to learn the hidden features. The cross-entropy is chosen as the error criterion for establishing the cost function, which is minimized by the back-propagation algorithm to obtain the optimized parameters of the network. Then, we can reconstruct all patches of the test dataset via the optimized CNN. After patching processing and inverse normalization, the final denoised result can be obtained from reconstructed patches. Experimental tests on synthetic and real data demonstrate the effectiveness and superiority of the proposed method compared with state-of-the-art denoising methods. INDEX TERMS Seismic data, noise attenuation, deep convolutional neural network, unsupervised learning.

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Seismic data denoising by deep-residual networks

Cited by 26 publications

References 8 publications

Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach

Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach

Research on Deep Convolutional Neural Network Time-Frequency Domain Seismic Signal Denoising Combined With Residual Dense Blocks

Unsupervised Seismic Random Noise Attenuation Based on Deep Convolutional Neural Network

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