Abstract: Seismic data denoising is an important processing step to improve data quality. The key to the denoising method is to retain effective signals as much as possible while suppressing the noise. Currently, denoising methods based on deep learning primarily use fixed-scale convolutional kernels to extract local features, which may result in incomplete events. Therefore, we proposed a denoising method based on Dense Dilated Convolutional Residual Network (DDCRN). In this method, multiple dense dilated convolutional feature fusion blocks (DDCFFB) cascade to form a deep network, thereby increasing the information reception range of DDCRN. DDCFFB is mainly composed of two parts that extracted features in parallel. The first part was a dense block that connected different convolutional layers to learn features. Complex information could be extracted efficiently by propagation and reusing of local features. The other part was multi-scale dilation convolution that could access a wide range of information windows. Dilated convolution provided a more comprehensive range of information. The fusion structure combined the features extracted from the two parts. The residual structure accelerated the network training convergence and avoided network degradation by skipping the connection channel. We evaluated the k-singular value decomposition (KSVD), f-x deconvolution (f-x decon), denoising convolutional neural network (DnCNN), u-shaped convolutional neural network (Unet), and DDCRN denoising methods, on the synthetic and real seismic data. The results show that the DDCRN effectively suppresses random noise while preserving the continuity of events compared to the other method.