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.

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

Kong F.;Picetti F.;Lipari V.;Bestagini P.;Tubaro S.
2020

Abstract

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.
SEG Technical Program Expanded Abstracts
3D
Interpolation
Machine learning
Neural networks
Processing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1201551
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