Multi-scale residual attention network for seismic data interpolation

Abstract

Data interpolation is a very important step in seismic data processing. At present, there are many methods based on deep learning to solve the problem of seismic data interpolation, but few studies adopt the idea of multi-scale residuals combined with attention mechanism. In this paper, a seismic data interpolation algorithm based on multi-scale residual attention network is proposed. Multi-scale convolution is applied to residual structure for the first time, and the information of seismic data is extracted adaptively by cascade of multi-scale residual blocks. Then, the output of each residual block is used as hierarchical feature for global feature fusion. In the stage of residual mapping, the residual channel attention block is introduced to promote the flow of information features and make the channel attention mechanism focus on the high frequency and edge information of seismic data, thus enhancing the ability of network representation. A lot of numerical experiments show that the algorithm have short computation time and can achieve good reconstruction results.