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

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  • Dramsch, Jesper Sören
  • Christensen, Anders Nymark
  • MacBeth, Colin
  • Lüthje, Mikael

Abstract

We present a novel 3D warping technique for the estimation of 4D seismic time-shift. This unsupervised method provides a diffeomorphic 3D 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. 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.

Suggested Citation

  • Dramsch, Jesper Sören & Christensen, Anders Nymark & MacBeth, Colin & Lüthje, Mikael, 2019. "Deep Unsupervised 4D Seismic 3D Time-Shift Estimation with Convolutional Neural Networks," Earth Arxiv 82bnj, Center for Open Science.
    • Handle: RePEc:osf:eartha:82bnj
    DOI: 10.31219/osf.io/82bnj
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