Song, H., Fang, M.H., Zhou, C. and Gao, H.Q., 2022. Seismic random noise suppression using denoising autoencoder. Journal of Seismic Exploration, 31: 203-218. In the seismic data acquisition phase, random noise will inevitably be introduced as undesirable components, which is not conducive to subsequent seismic signal processing and imaging tasks. In this article, we propose a denoising framework based on denoising autoencoder (DAE) for seismic random noise suppression. DAE can directly reconstruct noise-free seismic data from noisy seismic data in an unsupervised learning manner. The entire seismic data reconstruction requires three phases: corrupting phase, encoding phase, and decoding phase. In the corrupting phase, the original input is randomly corrupted, which helps the designed network to capture robust features. In the encoding phase, the corrupted input is encoded as a compressed representation that contains the important content of the seismic data. In the decoding stage, the compressed representation is decoded into reconstructed data. We choose the mean square error as the loss function, which is minimized by the back propagation algorithm to update the network parameters. The application of synthetic and real seismic data proves the effectiveness of the proposed method in suppressing seismic random noise.