Abstract: In seismic survey, seismic data are often polluted by serious random noise, which has a negative impact on subsequent processing and interpretation. The existing noise suppression methods of seismic data cannot effectively separate signals and noise, leading to the problems such as loss of image details and the introduction of false textures. In order to improve the quality of seismic images, a method for suppressing random noise of seismic data based on multi-scale information perception generative adversarial network (MSIP GAN) is proposed. Firstly, a multi-scale information perception generative network is designed to remove random noise from seismic data. The denoising network is based on the traditional deep convolutional neural network(CNN), and combines parallel multi-scale module, multi-channel information fusion module and consistency regularization module to improve the accuracy of texture structure and retain more details. Secondly, the label data of the discriminative network and the images of the generative network are constructed to assist in training the generative network. Finally, a composite loss function is designed to guide the generative network, thus improving the ability of the generative network to recover image details. The experiments on actual data from the Daqing Oilfield demonstrate that the proposed method achieves significantly enhanced noise suppression effectiveness compared to existing mainstream models and industrial software when removing noise of varying intensities. Furthermore, the experiments on the data from the offshore F3 block in the Netherlands reveal that this method exhibits a robust generalization capability.