Consecutively Missing Seismic Data Interpolation Based on Coordinate Attention Unet

Missing traces interpolation is a basic step in the seismic data processing workflow. Recently, many seismic data interpolation methods based on different neural networks have been proposed. The existing research shows that when the seismic data are consecutively missing, the larger gap for missing traces, the more difficult task of interpolation, due to convolution operation in the neural network can only capture local relations. In this letter, we incorporate the coordinate attention block to the Unet for 2-D successive missing traces interpolation. The hybrid loss function combined with structural similarity (SSIM) and $\text {L}_{ {1}}$ norm is used as the loss function to further improve the interpolation performance of the designed network. Comparison experiments on 2-D synthetic and field seismic data show that the interpolation results obtained by the proposed method are more accurate and reasonable compared with Unet and Unets equipped state-of-the-art similar modules.