Seismic Data Reconstruction Using a Hybrid Dimension Attention Restormer via Progressive Learning

Seismic data interpolation remains a critical challenge in geophysical data processing, particularly for irregular or consecutive missing traces. In this study, we propose the Hybrid-Dimension Attention Restormer (HDAR), which integrates Multi-Dconv Head Transposed Attention (MDTA) for global cross-channel dependencies and Overlapping Cross-Attention (OCA) for local spatial continuity, coupled with a progressive learning strategy that gradually increases missing trace rates during training to enhance generalization across diverse gap patterns. Evaluated on synthetic datasets, HDAR achieves an SNR of 21.84 dB for irregular missing patterns and 10.39 dB for consecutive gaps, outperforming the Swin Transformer by 1.75 dB and 1.18 dB, respectively. On field data, while ideal phase consistency remains challenging due to real-world noise and subsurface heterogeneity—a limitation shared by all tested methods—HDAR demonstrates relatively better preservation of scattered-wave continuity. These results suggest that HDAR is a competitive approach for seismic data reconstruction, particularly in scenarios where scattered-wave recovery is prioritized.