Abstract: In order to effectively remove the random noise in seismic data, a denoising algorithm based on a convolutional neural network (CNN) within the deep learning framework is proposed.The key requirement of the algorithm is to construct a CNN that is suitable for seismic data denoising, which includes the input layer, convolution layers, activation layer, and output layer.The CNN uses noisy seismic data as inputs, extracts and processes the seismic data via the multiple convolution layers, extracts the fluctuation characteristics of the data using the rectified linear units in the activation layer, accelerates the training convergence using the normalization layer, and finally uses residual learning to obtain the random noise as the output via the network output layer.Tests using pre-stack marine seismic data, post-stack seismic land data, and complex post-stack seismic land data illustrated the feasibility and effectiveness of the CNN for seismic denoising.Furthermore, the CNN outperformed some traditional denoising algorithms, such as the wavelet, dual-tree complex wavelet, and curvelet transforms in random noise attenuation.