Convolutional neural network seismic signal denoising method based on attention guidance

A convolutional neural network, seismic signal technology, applied in the field of seismic signal processing, can solve the problems of inability to remove unknown types of noise, easy loss of feature data, lack of generalization ability, etc., to reduce training complexity and good denoising performance. , the effect of improving denoising performance and efficiency

Pending Publication Date: 2021-07-23
JILIN UNIV
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Problems solved by technology

DCAENN based on self-encoding has no generalization ability; DnCNN based on residual convolutional neural network can handle Gaussian noise, but cannot remove unknown types of noise, and with the increase of network depth, the influence of shallow network on deep network will weaken ; Based on the SGAN of the generated confrontation network, the network is not easy to train, and the feature data is easy to lose during denoising

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  • Convolutional neural network seismic signal denoising method based on attention guidance
  • Convolutional neural network seismic signal denoising method based on attention guidance
  • Convolutional neural network seismic signal denoising method based on attention guidance

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[0039] In order to make the purpose, technical solution and advantages of the present invention clearer, the embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0040] Please refer to figure 1 , the present invention provides a method for denoising seismic signals based on attention-guided convolutional neural networks, including:

[0041] S1: Using the effective signal in seismic records synthesized by Lake wavelet, 20 clean seismic signals with a size of 640×128 are obtained, and 20 noise signals with a size of 1500×5000 are synthesized with Gaussian white noise;

[0042] Among them, 20 effective seismic data are synthesized by the Lake wavelet, and the main frequency is between 15Hz and 30Hz. The formula is as follows:

[0043]

[0044] A is the amplitude, t 0 Indicates the start time, f 0 Indicates the main frequency.

[0045] S2: Preprocess the synthetic seismic data set, obtain 9600 254×60 pre-train...

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Abstract

The invention provides a convolutional neural network seismic signal denoising method based on attention guidance. The method comprises the following steps: synthesizing effective signals in a seismic record by adopting Ricker wavelets; preprocessing the synthesized seismic data set, and constructing a training data set containing a noise set and a signal set; and inputting training data into an ADNet network model composed of four modules including a sparse block (SB), a feature enhancement block (FEB), an attention block (AB) and a reconstruction block (RB) for training, and after training is completed, suppressing the noise of seismic signals by using the ADNet network. According to the denoising method provided by the invention, the noise in the seismic signal can be suppressed, detail information is reserved, and the processing effect is relatively good.

Description

technical field [0001] The present invention relates to the technical field of seismic signal processing, in particular to an Attention-guided CNN for seismic signal denoising method for suppressing seismic data noise. Background technique [0002] In seismic exploration, the existence of noise will greatly affect the quality of seismic data. With the reduction of resources, the signal-to-noise ratio of the collected seismic data will decrease, and the noise properties will become more complex, especially the random noise in some areas has low-frequency, non-Gaussian , non-stationary, and high-energy characteristics, there is serious aliasing between the effective signal and random noise in the frequency domain. Therefore, in order to obtain high-quality seismic signals, it is necessary to denoise the seismic signals while maintaining the original information as much as possible. Traditional noise removal methods include: polynomial fitting method, Curvelet transform, wavel...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/36G06N3/04G06N3/08
CPCG01V1/362G01V1/364G06N3/04G06N3/08G01V2210/324G01V2210/322
Inventor 龙云闫爽宾康成韩国庆
Owner JILIN UNIV
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