Seismic random noise suppression using deep convolutional autoencoder neural network - Song, Hui; Gao, Yang; Chen, Wei; Xue, Ya-juan; Zhang, Hua; Zhang, Xiang (Journal of applied geophysics, 2020)
Due to human or environmental factors, random noise will inevitably be introduced during seismic data acquisition. Contaminated seismic data seriously affect subsequent seismic data processing and imaging. In this paper, we propose a deep convolutional autoencoder neural network for denoising, which consists of encoding and decoding frameworks. The encoding framework consisting of convolution layers and pooling layers captures features of seismic data while eliminating noise. The decoding framework consisting of convolution layers and upsampling layers is capable of enlarging the feature map and recovering the small scale features of the seismic data. In order to represent the characteristics of seismic data optimally, we introduce L1 regularization to constrain the weights. In order to learn multi-scale features of seismic data, we use the multiple filters with different sizes in both encoding and decoding frameworks. The proposed method is tested on synthetic and field cases. The experimental results show that the proposed denoising method can suppress random noise effectively. •Convolutional autoencoder neural network is extended to attenuate seismic random noise based on patch-extraction method.