Deep-learning-based global feature capture for seismic data reconstruction

ABSTRACT Compared with regularly and irregularly missing traces, consecutively missing traces (also called large gaps) create a more challenging task in seismic data interpolation. Recently, deep-learning-based methods have proven to be an effective tool for seismic data interpolation. However, widely adopted convolutional neural network (CNN) architectures, which are constrained by their limited receptive fields, emphasize local features while overlooking global features. This inherent characteristic limits their ability to reconstruct the more challenging large gaps. Compared with CNN-based architectures, transformers can capture more global features using a multihead self-attention mechanism. Therefore, we develop a novel transformer-based architecture, the dense double-branch attention transformer (D2AT), to interpolate large gaps in seismic data when the model is properly trained. The backbone of D2AT is a double-branch structure based on the Swin transformer, which enables it to learn low- and high-resolution features. Moreover, in the high-resolution upper branch, we use dense connections to facilitate feature interactions at different stages, and a spatial attention block is used to refine the extracted features. Experimental results on synthetic and field seismic records demonstrate that D2AT is a potential solution for reconstructing the consecutively missing traces in 2D seismic data.