Deep learning is now one of the most powerful techniques for solving various scientific and engineering problems. These deep learning techniques have recently begun to be applied in the field of subsurface imaging. As a part of the effort, we have applied the deep learning techniques to the imaging of subsurface from electromagnetic (EM) data. This presentation introduces three cases of the application: salt delineation and monitoring of injected CO using towed streamer EM data sets and kimberlite exploration using airborne EM data set. The results with significant qualities open up the possibility of the deep learning as an alternative of the conventional inversion techniques.


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  1. Abadi, M. et al.
    [2016] TensorFlow: Large-scale machine learning on heterogeneous distributed systems. Available:https://arxiv.org/abs/1603.04467.
    [Google Scholar]
  2. Araya-Polo, M., Jennings, J., Adler, A. and Dahlke, T.
    [2018] Deep-learning tomography. The Leading Edge, 37(1), 58–66.
    [Google Scholar]
  3. Ioffe, S. and Szegedy, C.
    [2015] Batch normalization: Accelerating deep network training by reducing internal covariate shift. Available:https://arxiv.org/abs/1502.03167.
    [Google Scholar]
  4. Kang, S., Seol, S. J. and Byun, J.
    [2012] A feasibility study of CO2 sequestration monitoring using the mCSEM method at a deep brine aquifer in a shallow sea. Geophysics, 77(2), E117–E126.
    [Google Scholar]
  5. Kingma, D. P. and Ba, J.
    [2014] Adam: A method for stochastic optimization. Available:https://arxiv.org/abs/1412.6980.
    [Google Scholar]
  6. Liu, D., Wang, W., Chen, W., Wang, X., Zhou, Y. and Shi, Z.
    [2018] Random-noise suppression in seismic data: What can deep learning do?SEG Technical Program Expanded Abstracts2018, 2016–2020.
    [Google Scholar]
  7. Long, J., Shelhamer, E. and Darrell, T.
    [2015] Fully convolutional networks for semantic segmentation. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
    [Google Scholar]
  8. Oh, S., Noh, K., Yoon, D., Seol, S. J. and Byun, J.
    [2018] Salt delineation from electromagnetic data using convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, early access.
    [Google Scholar]
  9. Sun, H. and Demanet, L.
    [2018] Low-frequency extrapolation with deep learning, SEG Technical Program Expanded Abstracts2018, 2011–2015.
    [Google Scholar]
  10. Waldeland, A. U., Jensen, A. C., Gelius, L.-J. and Solberg, A. H. S.
    [2018] Convolutional neural networks for automated seismic interpretation. The Leading Edge, 37(7), 529–537.
    [Google Scholar]
  11. Wang, Z., Di, H., Shafiq, M. A., Alaudah, Y. and AlRegib, G.
    [2018] Successful leveraging of image processing and machine learning in seismic structural interpretation: A review. The Leading Edge, 37(6), 451–461.
    [Google Scholar]

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