Consecutively Missing Seismic Data Reconstruction Via Wavelet-Based Swin Residual Network

Missing traces reconstruction is a key step for seismic data processing. In recent years, researchers have proposed various interpolation methods for seismic trace reconstruction. However, their models are hard to recover the weak signals in the consecutively missing case. Moreover, convolution operation used in these models is not sensitive to long-term dependencies and global information, which affects the reconstruction of the middle part of the missing area. To solve these problems, we propose a wavelet-based swin residual network (WSRN) for seismic data reconstruction. The swin residual block is designed into the U-net framework to improve the local and non-local modeling ability. Furthermore, by replacing the normal sampling layer, the multi-level wavelet transform is introduced to enhance the recovery ability of weak signals, and data augmentation strategy and a hybrid loss function are used to improve the reconstruction performance of WSRN. Experimental results on synthetic and field datasets illustrate that WSRN achieves significant improvement over some representative deep learning methods.