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Abstract

Summary

Natural and instrumental conditions during field seismic survey lead to noise and irregularities in acquired seismic data. In this work, we explore challenges and opportunities related to denoising and interpolation of seismic data by deep convolutional neural networks. In particular, we apply three network configurations to field data and match them with suitable applications. We show that U-Net is beneficial for denoising applications while adversarial generative networks (GAN) are superior in interpolation tasks. Enhanced interpolation capability of GANs, however, comes at cost of increased uncertainty in the results and we raise awareness about this observation. In the end, we consider the pitfalls of conventional metrics and outline the requirements for data-driven approaches to be suitable in production applications.

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/content/papers/10.3997/2214-4609.202032054
2020-11-30
2024-04-24

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References

  1. Gadylshin, K., Silvestrov, I., & Bakulin, A.
    [2019] Inpainting of local wavefront attributes using artificial intelligence. 89th SEG Conference and Exhibition, Extended abstract.
     [Google Scholar]
  2. Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C. and Bengio, Y.
    [2014] Generative Adversarial Nets. NIPS.
     [Google Scholar]
  3. Kokhlikyan, N., Miglani, V., Martin, M., Wang, E., Reynolds, J., Melnikov, A., Lunova, N. and Reblitz-Richardson, O.
    [2019] PyTorch Captum. https://github.com/pytorch/captum.
     [Google Scholar]
  4. Li, X. R., Mitsakos, N., Lu, P., Xiao, Y., Zhan, C., & Zhao, X.
    [2019]. Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling. Workshop on Solving Inverse Problems with Deep Networks, NeurIPS
     [Google Scholar]
  5. Mandelli, S., Lipari, V., Bestagini, P. and Tubaro, S.
    [2019] Interpolation and Denoising of Seismic Data using Convolutional Neural Networks. arXiv, abs/1901.07927.
     [Google Scholar]
  6. Richardson, A. and Feller, C.
    [2019] Seismic data denoising and deblending using deep learning. arXiv, abs/1907.01497.
     [Google Scholar]
  7. Ronneberger, O., Fischer, P. and Brox, T.
    [2015] U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv, abs/1505.04597.
     [Google Scholar]
  8. Siahkoohi, A., Kumar, R., & Herrmann, F.
    [2018] Seismic data reconstruction with generative adversarial networks. 80th EAGE Conference and Exhibition, Extended abstract.
     [Google Scholar]
  9. Wang, Y., Tao, X., Qi, X., Shen, X. and Jia, J.
    [2018] Image Inpainting via Generative Multi-column Convolutional Neural Networks. Advances in Neural Information Processing Systems, 331–340.
     [Google Scholar]
  10. Xie, P., Boelle, J. L., & Puntous, H.
    [2018] Generative adversarial network based fast noise removal on land seismic data. 88th SEG Conference and Exhibition, Extended abstract.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202032054
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Deep Learning for Seismic Data Reconstruction: Opportunities and Challenges
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202032054
/content/papers/10.3997/2214-4609.202032054
/content/papers/10.3997/2214-4609.202032054
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