Data Sheet 1_Adaptive seismic diffraction separation via a hybrid attention-based CASCU network.docx

However, existing deep learning methods still suffer from insufficient separation accuracy when dealing with complex seismic data. In this study, we propose a hybrid model named CASCU, which integrates a U-shaped convolutional architecture with the Swin Transformer.

The model overcomes the inherent limitations of conventional convolutional neural networks (CNNs), including restricted local receptive fields and fixed-size kernels. By enhancing the capture of long-range dependencies and global contextual information, CASCU more thoroughly exploits the spatiotemporal continuity features within seismic data. We also efficiently constructed a training dataset based on convolutional modeling and used CASCU to achieve high-fidelity diffraction separation from the total wavefield.