Recent advances on deep residual learning based seismic data reconstruction: an overview

Seismic data reconstruction is an essential process to reduce the effects of missing traces in field acquisition. Imputation and interpolation techniques have been proposed to reconstruct the subtle missing features, which is a challenging task that remains to be solved, such as the recovery capability for weak reflections and consecutively missing parts and the computational efficiency. In recent years, deep learning (DL), especially the residual network (ResNet), has gained remarkable success in seismic data reconstruction because it can precisely extract seismic features. In this overview, we probe into the residual module architecture and discuss in-depth the characteristics of the state-of-the-art residual modules, mainly including the split-transform-merge and the squeeze-and-attention. We use the detailed ablation experiments to investigate the roles of several key hyperparameters for each residual-based model. We explore the mechanism of the residual module's effectiveness based on extensive qualitative and quantitative comparisons involving various residual networks and state-of-the-art models for seismic data reconstruction. The irregularly sampled and consecutively sampled scenarios demonstrate that the reconstruction performance of the residual modules is promising and superior to that of state-of-the-art deep networks.