Deep prior based seismic data interpolation via multi-res U-net

Interpolation of seismic data is an important pre-processing step in most seismic processing workflows. Through the deep image prior paradigm, it is possible to use Convolutional Neural Networks for seismic data interpolation without the costly and prone-to-overfitting training stage. The proposed method makes use of the multi-res U-net architecture as a deep prior to perform interpolation of time slices in order to reconstruct 3D shot gathers. Numerical examples on different corrupted synthetic datasets demonstrate the validity and effectiveness of the proposed approach.