Seismic data denoising is an important mean to extract useful information from seismic data, remove interference, and improve the SNR(signal-to-noise ratio) of seismic data. Therefore, the research on denoising methods of seismic data has always been a hot topic. At present, most convolutional neural networks for desert seismic data denoising use single scale convolutional kernels to extract feature information, which is prone to cause missing details. Therefore, we propose the Multi-scale Dilated Convolution Network (MDCN) to remove desert seismic noise. Dilational convolution operators of different sizes are used to autocratically extract features of different scales from seismic data. The extracted features are then connected in series and fused into multi-scale information used for denoising. Moreover, using dilated convolutions can increase the receptive field, so that the output of each convolution would contain a larger range of information than single scale convolutional neural networks, which means they have access to a larger window and as a result can use temporal information. In order to increase the receiving range of the network and obtain more context information, we cascade multiple modules to form a deep network. In this way, we can extract as much detailed information as possible from the desert seismic data. The results of the experiment show that our method effectively suppresses the desert noise and also better retains the effective signal. •The model can effectively remove noise in desert seismic records, recover the seismic events and enhance their continuity.•Combine multi-scale ideas with neural networks to extract and learn more abundant information.•The use of dilated convolutions is conducive to capture as much feature information from the noisy records.