Deep Unsupervised 4-D Seismic 3-D Time-Shift Estimation With Convolutional Neural Networks

Jesper Soren Dramsch, Anders Nymark Christensen, Colin MacBeth, Mikael Lüthje

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
86 Downloads (Pure)


We present a novel 3-D warping technique for the estimation of 4-D seismic time-shift. This unsupervised method provides a diffeomorphic 3-D time shift field that includes uncertainties, therefore, it does not need prior time-shift data to be trained. This results in a widely applicable method in time-lapse seismic data analysis that is not implicitly biased by supervised time-shifts from other methods. We explore the generalization of the method to unseen data both in the same geological setting and in a different field, where the generalization error stays constant and within an acceptable range across test cases. We further explore upsampling of the warp field from a smaller network to decrease computational cost and see some deterioration of the warp field quality as a result. This method provides an accurate 3-D seismic registration method, where the heavy computation can be preexecuted and the inference of the network taking seconds on consumer hardware.

Original languageEnglish
Article number5903116
JournalIEEE Transactions on Geoscience and Remote Sensing
Early online date31 May 2021
Publication statusPublished - 2022


  • 3-D time-shift
  • 4-D seismic
  • Deep learning
  • Neural network
  • Time-lapse
  • Unsupervised learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


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