The requirement of a large amount of noisy-clean training dataset is one of the main bottlenecks restricting deep learning denoising. A self-supervised pre-stack seismic random noise suppression method based on iterative data refinement is proposed, which uses only noise samples to train the deep neural networks. The method firstly uses the multiple regression theory to estimate the random noise in the common offset gathers, and then superimposes the noise components to the noisy samples to construct the strong noise samples. Taking the skip network as the model, each iteration is divided into two steps: (1) Taking the strong noise samples as input, the weak noise samples are predicted by the last iteration optimization model; (2) Constructing the nosier-noise training dataset, the network model is optimized with a supervised learning strategy. The advantages of the algorithm are as follows: (1) The noise samples that are drawn from the distribution, which is approximately the same as the actual noise, are estimated by using the characteristics of the flat events of the common offset gathers; (2) With the increase of the number of iterations, the predicted weak noise samples are similar to the actual clean samples, it is feasible to learn a self-supervised network approximating the optimal parameters of a supervised model; (3) The iterative data refinement strategy achieves data augmentation, increases the number of samples, and avoids overfitting. Experiments on synthetic and realistic noise removal demonstrate that the iterative data refinement approach achieves state-of-the-art performance.