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Seismic random noise attenuation is a key step in seismic data processing. The random seismic data recorded by the detector tends to have strong noise, and this noisy seismic ratio can be seen as a low signal-to-noise ratio (SNR). Low SNR data can seriously affect the subsequent processing of seismic data, such as migration and imaging. Therefore, it is crucial to eliminate random noise in seismic data. In this paper, we aimed to improve the SNR of seismic data, and proposed an intelligent convolutional neural network noise reduction framework to adaptively capture seismic signals from seismic data with noise. The eponential linear unit (ELU) activation function and the Adam optimization algorithm were used to train the network, which increased the effective information extraction of the network in the negative interval. In order to speed up network training, we added residual learning and batch normalization methods to the network. In addition, three datasets were used to train and test the network. The experimental results show that the method proposed in this paper is better than feed-forward denoising convolutional neural networks (DnCNNs) and other contrast methods in denoising performance. More importantly, a well-trained network not only preserves weak features in learning, but also removes spatially random noise. First, the proposed method is fully trained to extract random noise from the training data, then we retain the data features learned in the training, and estimate the waveform characteristics in the test set by reconstructing the recorded seismic data. Secondly, the characteristics of seismic data in the field record are quite different from those of the training set. However, the proposed adaptive denoising framework can still capture the connection between prediction and reality from the difference. The processing results of theoretical pure record, common-shot-point record, and field data showed great potential in random noise attenuation applications.

Random Noise Attenuation Based on Residual Convolutional Neural Network in Seismic Datasets