Reflection residual statics estimated using a high-resolution neural network

Abstract

Performing reflection statics corrections for improving seismic imaging is an important step in land and shallow marine data processing. The reflection residual statics solution is commonly derived from stack power maximization with hyperbolic assumptions that are valid for normal moveout (NMO) correction and stacking. We have developed a deep learning method for deriving surface-consistent reflection residual statics directly from common-shot, common-receiver, or common-midpoint gathers without velocity analysis and NMO correction. To mitigate overfitting and improve generalization ability, we augment training data by using only a few gathers but massive synthetic statics values. Because the magnitude of residual statics is often small, we apply a high-resolution neural network as a backbone to learn detailed features. We also design the head of the neural network to allow multiscale training and multiscale testing, which enables different acquisition geometries for prediction. Training with labeled samples from synthetic or real data has been tested with new real data as prediction inputs. In both cases, residual statics help improve stacked sections. If the NMO assumption is valid, our method produces results comparable to stack power maximization with much less computation time. If the NMO assumption is invalid, our method produces better results than stack power maximization.