LAUSR

Seismic data reconstruction is an important technology in the seismic data processing. Existing reconstruction methods have achieved promising performance for regularly/randomly missing cases. However, recovering consecutive missing data remains challenging due to the loss of large amounts of information in local regions. In this letter, we devise a novel network called recurrent residual multiscale feature inference network (RRMFI-Net), which is mainly constructed by a recurrent residual multiscale feature inference (RRMFI) module and a recurrence adjustment attention (RAA) module. The RRMFI module infers and fills the missing regions multiple times and uses the result as a clue for the next inference, which makes the result more elegant. To ensure that there is no ambiguity between the results of multiple inferences, we devise an RRA module, which is fused into the RRMFI module to obtain padding information from a long distance. Experimentally, we compare RRMFI-Net with supervised state-of-the-art methods, demonstrating that RRMFI-Net is more effective on multiple indicators. Furthermore, we conduct ablation studies discussing the impact of key network hyperparameters.