Research progress of random noise attenuation methods for seismic data based on deep learning
CUI Yang1,2, WANG Yannan2, CHEN Wanli2, ZHANG Hong2, ZHU Dandan2, BAI Min1,2
1. Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education, Wuhan, Hubei 430100, China; 2. School of Geophysics and Petroleum Resources, Yangtze University, Wuhan, Hubei 430100, China
Abstract：This paper analyzes the concept, development status, method principle, denoising performance, and advantages and disadvantages of deep learning methods represented by a convolutional neural network (CNN), denoised convolutional neural network (DnCNN), U-net deep neural network, forward feedback (BP) neural network, Dilated convolutional neural network (DCNN), residual network (ResNet), and transfer learning.The denoising effects of traditional denoising methods, dictionary learning, and deep learning methods are compared, and the development prospect of deep learning technology in the field of seismic denoising is forecasted. The following conclusions are obtained:①The actual denoising effect of the deep learning method is better than that of traditional methods and dictionary learning methods.It does not need to set the structural model and has stronger generalization, shorter computation time, and higher precision.②There are many shortcomings in deep learning methods:The denoising effect of actual data is often worse than that of synthetic data; the universality is not strong; the "black box" characteristic of the neural network makes its physical interpretability greatly reduced.Network performance is closely related to the generalization of training data.The data sets used to train the network vary from person to person, making it difficult to evaluate network performance.③It is expected that deep learning will make progress and breakthroughs in the following aspects:building a denoised neural network structure suitable for different noises and introducing a better network structure to suppress seismic random noise, constructing the loss function of the network by converting the seismic signal to the transform domain, improving the learning strategy and making a more representative data set, making training data cover all solutions as much as possible, enhancing network generalization, and achieving automatic parameter tuning and methods combining model-driven and data-driven features.
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CUI Yang, WANG Yannan, CHEN Wanli, ZHANG Hong, ZHU Dandan, BAI Min. Research progress of random noise attenuation methods for seismic data based on deep learning. Oil Geophysical Prospecting, 2023, 58(5): 1269-1283.
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