1887
Volume 51, Issue 2
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

ABSTRACT

Due to the rapid development and spread of deep learning technologies, potential applications of artificial intelligence technology in the field of geophysical inversion are being explored. In this study, we applied a deep neural network (DNN) to reconstruct one-dimensional electrical resistivity structures from airborne electromagnetic (AEM) data for varying sensor heights. We used numerical models and their simulated AEM responses to train the DNN to be an inversion operator, and determined that it was possible to train the DNN without the use of stabilisers on the subsurface structures. We compared the quantitative performance of DNN and Gauss–Newton inversion of synthetic datasets, and demonstrated that DNN inversion reconstructed the subsurface structure more accurately, and within a significantly shorter period. We subsequently applied DNN inversion to a field dataset to quantify the effectiveness and applicability of the proposed method for real data. The results of the current study will open new avenues for real-time imaging of subsurface structures from AEM data.

© 2019 Australian Society of Exploration Geophysics
Loading

Article metrics loading...

/content/journals/10.1080/08123985.2019.1668240
2020-03-03
2026-01-13

  • From This Site

    /content/journals/10.1080/08123985.2019.1668240
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Araya-Polo, M., J. Jennings, A. Adler, and T. Dahlke 2018 Deep-learning tomography. The Leading Edge37: 58–66. doi: 10.1190/tle37010058.1
     https://doi.org/10.1190/tle37010058.1 [Google Scholar]
  2. Constable, S., R. Parker, and C. Constable 1987 Occam’s inversion: a practical algorithm for generating smooth models from electromagnetic sounding data. Geophysics52: 289–300. doi: 10.1190/1.1442303
     https://doi.org/10.1190/1.1442303 [Google Scholar]
  3. Deszcz-Pan, M., D.V. Fitterman, and V.F. Labson 1998 Reduction of inversion errors in helicopter Em data using auxiliary information. Exploration Geophysics29: 142–6. doi: 10.1071/EG998142
     https://doi.org/10.1071/EG998142 [Google Scholar]
  4. Di, H., Z. Wang, and G. AlRegib 2018 Real-time seismic-image interpretation via deconvolutional neural network. SEG Technical Program Expanded Abstracts 2018, 2051–5.
  5. Dramsch, J., and M. Lüthje 2018 Deep-learning seismic facies on state-of-the-art Cnn architectures. SEG Technical Program Expanded Abstracts 2018, 2036–40.
  6. Farquharson, C.G., D.W. Oldenburg, and P.S. Routh 2003 Simultaneous 1d inversion of loop–loop electromagnetic data for magnetic susceptibility and electrical conductivity. Geophysics68: 1857–69. doi: 10.1190/1.1635038
     https://doi.org/10.1190/1.1635038 [Google Scholar]
  7. Fournier, D., S. Kang, M.S. McMillan, and D.W. Oldenburg 2017 Inversion of airborne geophysics over the DO-27/DO-18 kimberlites — part 2: electromagnetics. Interpretation5: T313–25. doi: 10.1190/INT‑2016‑0140.1
     https://doi.org/10.1190/INT-2016-0140.1 [Google Scholar]
  8. Fraser, D.C. 1978 Resistivity mapping with an airborne multicoil electromagnetic system. Geophysics43: 144–72. doi: 10.1190/1.1440817
     https://doi.org/10.1190/1.1440817 [Google Scholar]
  9. Goodfellow, I., Y. Bengio, and A. Courville 2016 Deep learning. Cambridge, MA: MIT Press.
  10. Gramstad, O., and M. Nickel 2018 Automated interpretation of top and base salt using deep-convolutional networks. SEG Technical Program Expanded Abstracts 2018, 1956–60.
  11. Guo, R., M. Li, F. Yang, S. Xu, G. Fang, and A. Abubakar 2018 Application of supervised descent method for transient Em data inversion. SEG Technical Program Expanded Abstracts 2018, 2126–30.
  12. Hansen, P.C. 2000 The L-curve and its use in the numerical treatment of inverse problems. In Computational inverse problems in electrocardiology, ed. P. Johnston, Advances in Computational Bioengineering, vol. 4, 119–142. WIT Press.
     [Google Scholar]
  13. Kingma, D.P., and J. Ba 2014 Adam: a method for stochastic optimization. [Online]. Available: https://arxiv.org/abs/1412.6980.
  14. LeCun, Y., Y. Bengio, and G. Hinton 2015 Deep learning. Nature521: 436–44. doi: 10.1038/nature14539
     https://doi.org/10.1038/nature14539 [Google Scholar]
  15. Liu, D., W. Wang, W. Chen, X. Wang, Y. Zhou, and Z. Shi 2018 Random-noise suppression in seismic data: what can deep learning do? SEG Technical Program Expanded Abstracts 2018, 2016–20.
  16. Noh, K., S. Oh, S.J. Seol, and J. Byun 2018 3D sequential inversion of frequency-domain airborne electromagnetic data to determine conductive and magnetic heterogeneities. Geophysics83: E357–69. doi: 10.1190/geo2017‑0668.1
     https://doi.org/10.1190/geo2017-0668.1 [Google Scholar]
  17. Oh, S., K. Noh, D. Yoon, S.J. Seol, and J. Byun 2019 Salt delineation from electromagnetic data using convolutional neural networks. IEEE Geoscience and Remote Sensing Letters16: 519–523. doi: 10.1109/LGRS.2018.2877155
     https://doi.org/10.1109/LGRS.2018.2877155 [Google Scholar]
  18. Pham, N., S. Fomel, and D. Dunlap 2018 Automatic channel detection using deep learning. SEG Technical Program Expanded Abstracts 2018, 2026–30.
  19. Sun, H., and L. Demanet 2018 Low-frequency extrapolation with deep learning. SEG Technical Program Expanded Abstracts 2018, 2011–15.
  20. Wu, X., Y. Shi, S. Fomel, and L. Liang 2018 Convolutional neural networks for fault interpretation in seismic images. SEG Technical Program Expanded Abstracts 2018, 1946–50.
  21. Yang, D., and D.W. Oldenburg 2012 Three-dimensional inversion of airborne time-domain electromagnetic data with applications to a porphyry deposit. Geophysics77: B23–34. doi: 10.1190/geo2011‑0194.1
     https://doi.org/10.1190/geo2011-0194.1 [Google Scholar]
  22. Zhdanov, M.S., and E. Tartaras 2002 Three-dimensional inversion of multitransmitter electromagnetic data based on the localized quasi-linear approximation. Geophysical Journal International148: 506–19. doi: 10.1046/j.1365‑246x.2002.01591.x
     https://doi.org/10.1046/j.1365-246x.2002.01591.x [Google Scholar]
/content/journals/10.1080/08123985.2019.1668240
Loading
Imaging subsurface resistivity structure from airborne electromagnetic induction data using deep neural network
Exploration Geophysics 51, 214 (2020); https://doi.org/10.1080/08123985.2019.1668240
/content/journals/10.1080/08123985.2019.1668240
/content/journals/10.1080/08123985.2019.1668240
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Airborne electromagnetics; electrical resistivity; inversion

Most Cited This Month Most Cited RSS feed

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