Interpolation of missing shots via Plug and Play method with CSGS trained deep denoiser

Due to the restriction of complex field conditions, the trace interval in common receiver gathers (CRGs) is often larger than that in common shot gathers (CSGs). This impacts on the stability and precision of the following seismic data processing steps. To solve this issue, we present a Plug and Play method CSGs-trained deep denoiser for the interpolation of missing shots. Specifically, based on the spatial reciprocity theorem, instead of collecting or constructing training datasets, CSGs are used as the training dataset to train a deep convolutional neural network (CNN) Gaussian denoiser. This trained denoiser is then plugged into the alternating direction method of multiplier (ADMM) framework to solve the interpolation inverse problem. A numerical example on field data shows the effectiveness of the presented method in comparison to CNN-POCS method.