Random noise suppression of seismic data based on joint deep learning
ZHANG Yan1, LI Xinyue1, WANG Bin1, LI Jie1, DONG Hongli2
1. Institute of Computer and Information Techno-logy, Northeast Petroleum University, Daqing, Heilongjiang 163318, China; 2. Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, Heilongjiang 163318, China
Abstract:Random noise suppression is the key task of seismic data processing. To improve SNR (signal-to-noise ratio) and increase the efficiency and accuracy of following processing and interpretation, appropriate suppression methods should be used for noises induced by different mechanisms and with different characteristics. Applicable denoising methods based on deep learning usually focus on feature extraction in time or frequency domain, which result in over-smoothed or blurred textures in local zones. In addition, the kernel of a traditional convolution neural network is usually set to be a small and fixed block, which limits the size of the receptive field and reduces the diversity of the target characteristics extracted from seismic data. This paper proposes a method of random noise suppression based on joint deep learning. Firstly, features in both time domain and frequency domain are considered, and the joint error is used to define the loss function to improve effect of various extracted features. Secondly, by analyzing the influence of the kernel size and network depth on the size of the receptive field, the method of expanding convolution is used to extract more diverse features and reduce the loss of details of seismic data. Thirdly, according to the similarity between the input and output samples of the network, a residual learning strategy is introduced. Finally, the batch normalization (BN) algorithm is used to accelerate the convergence of the model and improve denoising efficiency. Compared with similar algorithms, the method proposed in this paper has a better effect on preserving the features of events and provides higher SNR.
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