Data noise suppressing method based on residual block full convolutional neural network

Abstract

The invention discloses a data noise suppressing method based on a residual block full convolutional neural network. A training set and a test set for suppressing earthquake noise by using a deep learning method are selected from the same data set, so that the generalization performance of a model is limited. Specific to the problem of generalization, a double residual block is fused on the basisof a Unet network according to the design principle of a network structure in order to enhance the capturing performance of the network for random noise. The data noise suppressing method is established on an end-to-end coding-decoding network structure, noise-containing earthquake data is taken as input, and the substitutive characteristics of the random noise are extracted by a plurality of convolution layers and residual blocks to construct coding; and then a plurality of deconvolution layers and residual blocks are used for constructing decoding, so that the output of the network is the noise-suppressed earthquake data. Compared with the existing earthquake data denoising method, the data noise suppressing method has the advantages that the double residual block is fused to perform secondary digestion and learning on the extracted random noise characteristics, so that the substitutive characteristics of the noise are learned more fully. Thus, the data noise suppressing method has remarkable advantages on the aspect of generalization, and can effectively suppress the random noise and 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...

AI Technical Summary

Problems solved by technology

Due to this advantage of statistical learning, the paper "Wang Yuqing, Lu Wenkai, Liu Jinlin, etc. 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

Images