Skip to main content
SpringerLink
Log in

(Pseudo-)3D Inversion of Geophysical Electromagnetic Induction Data by Using an Arbitrary Prior and Constrained to Ancillary Information

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14111))

Included in the following conference series:

  • 357 Accesses

Abstract

Electromagnetic induction (EMI) methods are often used to map rapidly large areas with minimal logistical efforts. However, they are limited by a small number of frequencies and by their severe ill-posedness. On the other hand, electrical resistivity tomography (ERT) results are generally considered more reliable, with no need for specific calibration procedures and easy 2D/3D inversion. Still, ERT surveys are definitely more time-consuming, and, ideally, an approach with the advantages of both EMI and ERT would be optimal. The present research addresses this issue by incorporating realistic constraints into EMI inversion, going beyond simplistic spatial constraints like smooth or sharp regularization terms, while taking into consideration the ancillary information already available about the investigated site. We demonstrate how additional pre-existing information, such as a reference model (i.e., an existing ERT section) can enhance the EMI inversion. The study verifies the results against observations from boreholes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Guillemoteau, J., Christensen, N.B., Jacobsen, B.H., Tronicke, J.: Fast 3D multichannel deconvolution of electromagnetic induction loop-loop apparent conductivity data sets acquired at low induction numbers. Geophysics 82(6), E357–E369 (2017)

    Article  Google Scholar 

  2. Koganti, T., Van De Vijver, E., Allred, B.J., Greve, M.H., Ringgaard, J., Iversen, B.V.: Mapping of agricultural subsurface drainage systems using a frequency-domain ground penetrating radar and evaluating its performance using a single-frequency multi-receiver electromagnetic induction instrument. Sensors 20(14), 3922 (2020)

    Article  Google Scholar 

  3. Karshakov, E.V., Podmogov, Y.G., Kertsman, V.M., Moilanen, J.: Combined frequency domain and time domain airborne data for environmental and engineering challenges. J. Environ. Eng. Geophys. 22(1), 1–11 (2017)

    Article  Google Scholar 

  4. Yin, C., Hodges, G.: 3D animated visualization of EM diffusion for a frequency-domain helicopter EM system. Geophysics 72(1), F1–F7 (2007)

    Article  Google Scholar 

  5. Won, I.J., Oren, A., Funak, F.: GEM-2A: A programmable broadband helicopter-towed electromagnetic sensorGEM-2A HEM Sensor. Geophysics 68(6), 1888–1895 (2003)

    Article  Google Scholar 

  6. Dzikunoo, E.A., Vignoli, G., Jørgensen, F., Yidana, S.M., Banoeng-Yakubo, B.: New regional stratigraphic insights from a 3D geological model of the Nasia sub-basin, Ghana, developed for hydrogeological purposes and based on reprocessed B-field data originally collected for mineral exploration. Solid Earth 11(2), 349–361 (2020)

    Article  Google Scholar 

  7. Thiesson, J., Kessouri, P., Schamper, C., Tabbagh, A.: Calibration of frequency-domain electromagnetic devices used in near-surface surveying. Near Surface Geophys. 12(4), 481–491 (2014)

    Article  Google Scholar 

  8. Foged, N., Auken, E., Christiansen, A.V., Sørensen, K.I.: Test-site calibration and validation of airborne and ground-based TEM systems. Geophysics 78(2), E95–E106 (2013)

    Article  Google Scholar 

  9. Ley-Cooper, Y., Macnae, J., Robb, T., Vrbancich, J.: Identification of calibration errors in helicopter electromagnetic (HEM) data through transform to the altitude-corrected phase-amplitude domain. Geophysics 71(2), G27–G34 (2006)

    Article  Google Scholar 

  10. Triantafilis, J., Laslett, G.M., McBratney, A.B.: Calibrating an electromagnetic induction instrument to measure salinity in soil under irrigated cotton. Soil Sci. Soc. Am. J. 64(3), 1009–1017 (2000)

    Article  Google Scholar 

  11. Minsley, B.J., Smith, B.D., Hammack, R., Sams, J.I., Veloski, G.: Calibration and filtering strategies for frequency domain electromagnetic data. J. Appl. Geophys. 80, 56–66 (2012)

    Article  Google Scholar 

  12. Bai, P., Vignoli, G., Hansen, T.M.: 1D stochastic inversion of airborne time-domain electromagnetic data with realistic prior and accounting for the forward modeling error. Remote Sensing 13(19), 3881 (2021)

    Article  Google Scholar 

  13. Hansen, T.M., Cordua, K.S., Jacobsen, B.H., Mosegaard, K.: Accounting for imperfect forward modeling in geophysical inverse problems—exemplified for crosshole tomography. Geophysics 79(3), H1–H21 (2014)

    Article  Google Scholar 

  14. Viezzoli, A., Auken, E., Munday, T.: Spatially constrained inversion for quasi 3D modelling of airborne electromagnetic data–an application for environmental assessment in the Lower Murray Region of South Australia. Explor. Geophys. 40(2), 173–183 (2009)

    Article  Google Scholar 

  15. Brodie, R., Sambridge, M.: A holistic approach to inversion of frequency-domain airborne EM data. Geophysics 71(6), G301–G312 (2006)

    Article  Google Scholar 

  16. McLachlan, P., Blanchy, G., Binley, A.: EMagPy: open-source standalone software for processing, forward modeling and inversion of electromagnetic induction data. Comput. Geosci. 146, 104561 (2021)

    Article  Google Scholar 

  17. Vignoli, G., Fiandaca, G., Christiansen, A.V., Kirkegaard, C., Auken, E.: Sharp spatially constrained inversion with applications to transient electromagnetic data. Geophys. Prospect. 63(1), 243–255 (2015)

    Article  Google Scholar 

  18. Klose, T., Guillemoteau, J., Vignoli, G., Tronicke, J.: Laterally constrained inversion (LCI) of multi-configuration EMI data with tunable sharpness. J. Appl. Geophys. 196, 104519 (2022)

    Article  Google Scholar 

  19. Vignoli, G., Sapia, V., Menghini, A., Viezzoli, A.: Examples of improved inversion of different airborne electromagnetic datasets via sharp regularization. J. Environ. Eng. Geophys. 22(1), 51–61 (2017)

    Article  Google Scholar 

  20. Ley-Cooper, A.Y., et al.: Airborne electromagnetic modelling options and their consequences in target definition. Explor. Geophys. 46(1), 74–84 (2015)

    Article  Google Scholar 

  21. Klose, T., Guillemoteau, J., Vignoli, G., Walter, J., Herrmann, A., Tronicke, J.: Structurally constrained inversion by means of a Minimum Gradient Support regularizer: examples of FD-EMI data inversion constrained by GPR reflection data. Geophys. J. Int. 233(3), 1938–1949 (2023)

    Article  Google Scholar 

  22. Pagliara, G., Vignoli, G.: Focusing inversion techniques applied to electrical resistance tomography in an experimental tank. In: XI International Congress Proceedings of the International Association for Mathematical Geology, Liege, Belgium (2006)

    Google Scholar 

  23. Thibaut, R., Kremer, T., Royen, A., Ngun, B.K., Nguyen, F., Hermans, T.: A new workflow to incorporate prior information in minimum gradient support (MGS) inversion of electrical resistivity and induced polarization data. J. Appl. Geophys. 187, 104286 (2021)

    Article  Google Scholar 

  24. Fiandaca, G., Doetsch, J., Vignoli, G., Auken, E.: Generalized focusing of time-lapse changes with applications to direct current and time-domain induced polarization inversions. Geophys. J. Int. 203(2), 1101–1112 (2015)

    Article  Google Scholar 

  25. Karaoulis, M., Revil, A., Tsourlos, P., Werkema, D.D., Minsley, B.J.: IP4DI: a software for time-lapse 2D/3D DC-resistivity and induced polarization tomography. Comput. Geosci. 54, 164–170 (2013)

    Article  Google Scholar 

  26. Zhou, J., Revil, A., Karaoulis, M., Hale, D., Doetsch, J., Cuttler, S.: Image-guided inversion of electrical resistivity data. Geophys. J. Int. 197(1), 292–309 (2014)

    Article  Google Scholar 

  27. Vignoli, G., Guillemoteau, J., Barreto, J., Rossi, M.: Reconstruction, with tunable sparsity levels, of shear wave velocity profiles from surface wave data. Geophys. J. Int. 225(3), 1935–1951 (2021)

    Article  Google Scholar 

  28. Hansen, T.M.: Efficient probabilistic inversion using the rejection sampler - exemplified on airborne EM data. Geophys. J. Int. 224(1), 543–557 (2021)

    Article  Google Scholar 

  29. Hansen, T.M., Minsley, B.J.: Inversion of airborne EM data with an explicit choice of prior model. Geophys. J. Int. 218(2), 1348–1366 (2019)

    Article  Google Scholar 

  30. Mosegaard, K., Tarantola, A.: Monte Carlo sampling of solutions to inverse problems. J. Geophys. Res. Solid Earth 100(B7), 12431–12447 (1995)

    Article  Google Scholar 

  31. Tikhonov, A.N., Arsenin, V.Y.: Solutions of Ill-Posed Problems, 1st edn. V. H. Winston & Sons, Washington, U.S.A. (1977)

    Google Scholar 

  32. Rücker, C., Günther, T., Wagner, F.M.: pyGIMLi: an open-source library for modelling and inversion in geophysics. Comput. Geosci. 109, 106–123 (2017)

    Article  Google Scholar 

  33. Høyer, A.S., Vignoli, G., Hansen, T.M., Vu, L.T., Keefer, D.A., Jørgensen, F.: Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies. Hydrol. Earth Syst. Sci. 21(12), 6069–6089 (2017)

    Article  Google Scholar 

  34. Bai, P., Vignoli, G., Viezzoli, A., Nevalainen, J., Vacca, G.: (Quasi-) real-time inversion of airborne time-domain electromagnetic data via artificial neural network. Remote Sens. 12(20), 3440 (2020)

    Article  Google Scholar 

  35. Neal, R.M.: Bayesian Learning for Neural Networks, vol. 118. Springer, Germany (2012). https://doi.org/10.1007/978-1-4612-0745-0

  36. Zhdanov, M.S., Vignoli, G., Ueda, T.: Sharp boundary inversion in crosswell travel-time tomography. J. Geophys. Eng. 3(2), 122–134 (2006)

    Article  Google Scholar 

  37. Haynie, K.L., Khan, S.D.: Shallow subsurface detection of buried weathered hydrocarbons using GPR and EMI. Mar. Pet. Geol. 77, 116–123 (2016)

    Article  Google Scholar 

  38. Rashed, M., Niyazi, B.: Environmental impact assessment of the former Al-Musk lake wastewater dumpsite using electromagnetic induction technique. Earth Syst. Environ. 1(1), 1–10 (2017)

    Article  Google Scholar 

  39. Torin, L., Davidsson, L., Nilsson, M.: Inledandeprojektering av Nisses kemtvätt i Osby. Report for Sveriges Geologiska Undersökning by WSP Environmental Sverige, Sweden (2021)

    Google Scholar 

Download references

Acknowledgments

The authors are very grateful to Dr. Henning Persson (Geological Survey of Sweden, SGU) for his support during the survey and for providing the background material. In addition, many thanks are due to the Engineering Geology Division of Lund University for its logistical support.

Author information

Authors and Affiliations

  1. University of Cagliari, 09124, Cagliari, Italy

    Nicola Zaru, Giuseppina Vacca & Giulio Vignoli

  2. Lund University, 22100, Lund, Sweden

    Matteo Rossi

  3. GEUS, 8000, Aarhus, Denmark

    Giulio Vignoli

Authors
  1. Nicola Zaru
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matteo Rossi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giuseppina Vacca
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giulio Vignoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giulio Vignoli .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zaru, N., Rossi, M., Vacca, G., Vignoli, G. (2023). (Pseudo-)3D Inversion of Geophysical Electromagnetic Induction Data by Using an Arbitrary Prior and Constrained to Ancillary Information. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14111. Springer, Cham. https://doi.org/10.1007/978-3-031-37126-4_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37126-4_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37125-7

  • Online ISBN: 978-3-031-37126-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation