Seismic data interpolation based on U-net with texture loss

Abstract

Seismic data interpolation is an effective way of recovering missing traces and obtaining enough information for subsequent processing. Unlike traditional methods, deep neural network (DNN)-based methods do not need to make assumptions because they can self-learn the relationship between sampled data and complete data using large training data sets and complete the interpolation with a small computational burden. However, current DNN-based approaches only focus on reducing the difference between the recovered and original data during training, which helps to improve the quality of the reconstructed seismic data as a whole, while ignoring the characteristics of the local structure. We have developed a novel seismic U-net interpolator (SUIT) algorithm based on the framework of the U-net DNN in combination with a texture loss, rather than only optimizing for reconstruction loss. Texture loss is proposed to ensure the accuracy of local structural information, which is calculated by a pretrained texture extraction neural network. Furthermore, we use a trade-off parameter to balance the reconstruction error and texture loss, and a practical technique for selecting the associated weighting parameter. The feasibility of our method is assessed via synthetic and field data examples. Numerical tests show that SUIT is robust in noisy environments and that the trained network can reconstruct irregularly or regularly sampled seismic data. Our proposed algorithm performed better than DNN-based approaches that only use reconstruction loss and the traditional low-rank matrix fitting method.