1887

Abstract

Summary

We use a CycleGAN to map acoustic synthetic data to elastic data, and to map elastic field data to acoustic data, and use the resulting data to perform acoustic FWI on a 3D field dataset that shows strong elastic effects at top chalk. Using machine learning to change the effective physics of field data has many other potential applications.

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/content/papers/10.3997/2214-4609.201901969
2019-06-03
2024-03-28
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References

  1. He, K., Zhang, X., Ren, S. and Sun, J.
    [2016] Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778.
    [Google Scholar]
  2. Hoffman, J., Tzeng, E., Park, T., Zh, J.Y., Isola, P., Saenko, K., Efros, A.A. and Darrell, T.
    [2017] Cycada: Cycle-consistent adversarial domain adaptation. arXiv preprint arXiv:1711.03213.
    [Google Scholar]
  3. Zhu, J.Y., Park, T., Isola, P. and Efros, A.A.
    [2017] Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint.
    [Google Scholar]
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