A Data Noise Suppression Method Based on Residual Block Fully Convolutional Neural Network

Abstract

The invention discloses a data noise suppression method based on a residual block full convolutional neural network. Both the training set and the test set for suppressing seismic noise by applying deep learning methods come from the same data set, which limits the generalization of the model. To solve the generalization problem, the design idea of ​​the network structure is to fuse double residual blocks on the basis of the Unet network to enhance the network's ability to capture random noise. The present invention is based on an end-to-end encoding-decoding network structure, takes noisy seismic data as input, and extracts the essential characteristics of random noise from multiple convolutional layers and residual blocks to form a code; The multilayer and residual blocks constitute the decoding, and the output of the network is the noise-suppressed seismic data. Compared with the current seismic data denoising method, due to the fusion of the double residual block, the extracted random noise features are digested and learned twice, and the essential features of the noise are learned more fully, so it has obvious generalization. Advantages, not only can effectively suppress random noise, but also protect effective signals.

Description

technical field [0001] The invention relates to the technical field of data noise suppression, in particular to seismic random noise suppression. Background technique [0002] Traditional seismic data denoising methods include f-k domain filtering, f-x domain denoising, wavelet transform, curvelet transform and discrete cosine transform, etc. The above methods have been widely used in the field of seismic data denoising, but there are still problems such as insufficient denoising ability and destruction of effective signals. [0003] In recent years, with the development of deep learning technology, researchers have proposed a method for denoising seismic data using deep learning technology. Different from traditional denoising methods, deep learning belongs to the category of statistical learning. Statistical learning can learn the essential characteristics of effective signals and noises based on noise samples, and fit a model that can classify effective signals and noise...

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Problems solved by technology

Due to this advantage of statistical learning, the paper "Wang Yuqing, Lu Wenkai, Liu Jinlin, et al. Seismic Random Noise Suppression Based on Data Augmentation and CNN [J]. Acta Geophysics, 2019, 62(1): 421-433." The method based on the Unet convolutional neural network suppresses the random noise of seismic data and has achieved certain results, but the problem is that in the post-stack seismic data test experiment, the training set and the test set come from the same data set, and the generalization of the model is limited. limit

The literature "Ma J.Deep Learning for Attenuating Random and Coherence Noise Simultaneously[C] / / 80th EAGEConference and Exhibition 2018.2018." proposes that the method of denoising seismic data based on DnCNN can suppress Gaussian noise, but the data sets used for training and testing are both For synthetic data, it may not necessarily achieve good noise suppression effect in real seismic data sets

The document "Liu J, Lu W, Zhang P. Random Noise Attenuation Using Convolutional Neural Networks[C] / / 80th EAGE Conference and Exhibition2018.2018." proposes a seismic random noise suppression method based on the Unet convolutional neural network, but the data sets are all from Synthetic data, generalization is limited in real seismic data sets

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