Equivariant imaging for self-supervised regularly undersampled seismic data interpolation

Because of the restriction of complex field conditions and economic circumstance, seismic data is usually undersampled in the spatial domain, which needs to be interpolated to meet the requirements of following seismic data processing such as seismic imaging. In this abstract, we present a seismic data interpolation method via an end-to-end self-supervised deep learning framework. Specifically, a CNN is trained only using the observed undersampled seismic data itself. Furthermore, based on the equivariance of seismic data with respect to shift and undersampling, a training strategy that enforces both the measurement consistency and the equivalence is utilized. Experiments on regularly undersampled synthetic and field data interpolation show the effectiveness of our presented method in comparison with deep image prior (DIP) based interpolation method