1887

Abstract

Summary

We investigate the potential of deep learning based approaches to 1D EM inversion. Our approach to inversion based on deep neural networks is entirely data-driven, does not employ traditional gradient-based techniques and provides a guess to the model instantaneously in a single step. The results of this study show that a deep neural network can accurately reconstruct resistivity distribution in the subsurface from data measured at the seafloor. Shallow resistive and conductive structures do not significantly impede the detection of deeper targets. Once trained, the network can predict resistivity models from new data in a few milliseconds. For multidimensional problems, data-driven inversion based on deep learning is expected to deliver sufficiently accurate results orders of magnitude faster than conventional inversion methods.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201901485
2019-06-03
2020-05-26
Loading full text...

Full text loading...

References

  1. Araya-Polo, M., Dahlke, T., Frogner, C., Zhang, C., Poggio, T. and Hohl, D.
    [2017] Automated fault detection without seismic processing. The Leading Edge, 36(3), 208–214.
    [Google Scholar]
  2. Auken, E., Christiansen, A.V., Jacobsen, B.H., Foged, N. and Sørensen, K.I.
    [2005] Piecewise 1D laterally constrained inversion of resistivity data. Geophysical Prospecting, 53(4), 497–506.
    [Google Scholar]
  3. Farquharson, C.G., Oldenburg, D.W. and Routh, P.S.
    [2003] Simultaneous 1D inversion of loop-loop electromagnetic data for magnetic susceptibility and electrical conductivity. Geophysics, 68(6), 1857–1869.
    [Google Scholar]
  4. He, K., Zhang, X., Ren, S. and Sun, J.
    [2016] Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778.
    [Google Scholar]
  5. Huang, L., Dong, X. and Clee, T.E.
    [2017] A scalable deep learning platform for identifying geologic features from seismic attributes. The Leading Edge, 36(3), 249–256.
    [Google Scholar]
  6. Key, K.
    [2009] 1D inversion of multicomponent, multifrequency marine CSEM data: Methodology and synthetic studies for resolving thin resistive layers. Geophysics, 74(2), F9–F20.
    [Google Scholar]
  7. LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P.
    [1998] Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
    [Google Scholar]
  8. Løseth, L.O. and Ursin, B.
    [2007] Electromagnetic fields in planarly layered anisotropic media. Geophysical Journal International, 170(1), 44–80.
    [Google Scholar]
  9. Puzyrev, V.
    [2018] Deep learning electromagnetic inversion with convolutional neural networks. Submitted. arXiv preprint arXiv:1812.10247.
    [Google Scholar]
  10. Streich, R. and Becken, M.
    [2011] Sensitivity of controlled-source electromagnetic fields in planarly layered media. Geophysical Journal International, 187(2), 705–728.
    [Google Scholar]
  11. Swidinsky, A., Kohnke, C. and Edwards, R.N.
    [2018] The electromagnetic response of a horizontal electric dipole buried in a multi‐layered earth. Geophysical Prospecting, 66(1), 240–256.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201901485
Loading
/content/papers/10.3997/2214-4609.201901485
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error