We present an algorithm for seismic data interpolation that combines the use of a deep image prior (DIP) and projection onto convex sets (POCS). Deep image priors form part of an optimization problem in which they reparameterize the interpolated data as the output of a convolutional network. While they are able to provide accurate reconstructions of seismic data without the need for any training data, they tend to suffer when large gaps are present in the missing data. We observe significant improvements in the reconstructed data when a POCS regularization term is introduced to the DIP. We demonstrate the improvements of our approach on both synthetic and field data.

Presentation Date: Wednesday, October 14, 2020

Session Start Time: 8:30 AM

Presentation Time: 10:10 AM

Location: 360D

Presentation Type: Oral

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