Seismic data interpolation using a POCS-guided deep image prior

Min Jun Park, Joseph Jennings, Bob Clapp, Biondo Biondi

202030 citationsDOI

Abstract

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

Topics & Concepts

Deep learningComputer scienceInterpolation (computer graphics)Convolutional neural networkRegularization (linguistics)Prior probabilityArtificial intelligenceWorkflowDeep neural networksImage (mathematics)Bayesian probabilityDatabaseSeismic Imaging and Inversion TechniquesSparse and Compressive Sensing TechniquesImage and Signal Denoising Methods