1887
Volume 19, Issue 5
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

Electromagnetic instrument responses suffer from signal drift that results in a variable response at a given location over time. If left uncorrected, spatiotemporal aliasing can manifest and global trends or abrupt changes might be observed in the data, which are independent of subsurface electromagnetic variations. By performing static ground measurements, we characterized drift patterns of different electromagnetic instruments. Next, we performed static measurements at an elevated height, approximately 4 metre above ground level, to collect a data set that forms the basis of a new absolute calibration methodology. By additionally logging ambient temperature variations, battery voltage and relative humidity, a relation between signal drift and these parameters was modelled using a machine learning (ML) approach. The results show that it was possible to mitigate the effects of signal drift; however, it was not possible to completely eliminate them. The reason is three‐fold: (1) the ML algorithm is not yet sufficiently adapted for accurate prediction; (2) signal instability is not explained sufficiently by ambient temperature, relative humidity and battery voltage; and (3) the black‐box internal (factory) calibration impeded direct access to raw data, which prevents accurate evaluation of the proposed methodology. However, the results suggest that these challenges are not insurmountable and that ML can form a viable approach in tackling the drift problem instrument specific in the near future.

Loading

Article metrics loading...

/content/journals/10.1002/nsg.12160
2021-09-12
2021-09-27
Loading full text...

Full text loading...

References

  1. Abadi, M., Agarwal, A., Barham, A., Brevdo, E., Chen, Z., Citro, C., et al. (2015) TensorFlow: Large‐Scale Machine Learning on Heterogeneous Distributed Systems. TensorFlow White Papers.
    [Google Scholar]
  2. Abdu, H., Robinson, D.A. and Jones, S.B. (2007) Comparing bulk soil electrical conductivity determination using the DUALEM‐1S and EM38‐DD electromagnetic induction instruments. Soil Science Society of America Journal, 71, 189.
    [Google Scholar]
  3. Al‐Maskari, S., Li, X. and Liu, Q. (2014) An Effective Approach to Handling Noise and Drift in Electronic Noses. Cham: Springer International Publishing, pp. 223–230.
    [Google Scholar]
  4. Bishop, C. (2006) Pattern Recognition and Machine Learning, New York: Springer.
    [Google Scholar]
  5. Bobe, C. and Van De Vijver, E. (2019) Offset errors in probabilistic inversion of small‐loop frequency‐domain electromagnetic data: A synthetic study on their influence on magnetic susceptibility estimation. GEM 2019 Xi'an: International Workshop and Gravity, Electrical & Magnetic Methods and Their Applications, Chenghu, China.
  6. Butterworth, S. (1930) On the theory of filter amplifiers. Wireless Engineer, 7, 536–541.
    [Google Scholar]
  7. De Smedt, P., Delefortrie, S. and Wyffels, F. (2016) Identifying and removing micro‐drift in ground‐based electromagnetic induction data. Journal of Applied Geophysics, 131, 14–22.
    [Google Scholar]
  8. Delefortrie, S., De Smedt, P., Saey, T., Van De Vijver, E. and Van Meirvenne, M. (2014) An efficient calibration procedure for correction of drift in EMI survey data. Journal of Applied Geophysics, 110, 115–125.
    [Google Scholar]
  9. Delefortrie, S., Hanssens, D. and De Smedt, P. (2018) Low signal‐to‐noise FDEM in‐phase data: potential for magnetic susceptibility modelling. Journal of Applied Geophysics, 152, 17–25.
    [Google Scholar]
  10. Delefortrie, S., Hanssens, D., Saey, T., Van De Vijver, E., Smetryns, M., Bobe, C. and De Smedt, P. (2019) Validating land‐based FDEM data and derived conductivity maps: assessment of signal calibration, signal attenuation and the impact of heterogeneity. Journal of Applied Geophysics, 164, 197–190.
    [Google Scholar]
  11. Everett, M.E. (2012) Theoretical developments in electromagnetic induction geophysics with selected applications in the near surface. Surveys in Geophysics, 33, 29–63.
    [Google Scholar]
  12. Gebbers, R., Lück, E., Dabas, M. and Domsch, H. (2009) Comparison of instruments for geoelectrical soil mapping at the field scale. Near Surface Geophysics, 7, 179–190.
    [Google Scholar]
  13. Green, A. and Lane, R. (2003) Estimating noise levels in AEM data. ASEG Extended Abstracts. Adelaide, Australia.
  14. Grellier, S., Florsch, N., Camerlynck, C., Janeau, J.L., Podwojewski, P. and Lorentz, S. (2013) The use of Slingram EM38 data for topsoil and subsoil geoelectrical characterization with a Bayesian inversion. Geoderma, 200–201, 140–155.
    [Google Scholar]
  15. Hochreiter, S. and Schmidhuber, J. (1997) Long short‐term memory. Neural Computation, 9, 1735–1780.
    [Google Scholar]
  16. Hossain, M.B., Lamb, D.W., Lockwood, P.V. and Frazier, P. (2010) EM38 for volumetric soil water content estimation in the root‐zone of deep vertosol soils. Computers and Electronics in Agriculture, 74, 100–109.
    [Google Scholar]
  17. Huang, H. (2005) Depth of investigation for small broadband electromagnetic sensors. Geophysics, 70, 135–142.
    [Google Scholar]
  18. Huang, J., Minasny, B., Whelan, B.M., Mcbratney, A.B. and Triantafilis, J. (2017) Temperature‐dependent hysteresis effects on EM induction instruments: an example of single‐frequency multi‐coil array instruments. Computers and Electronics in Agriculture, 132, 76–85.
    [Google Scholar]
  19. Keller, G.V. and Frischknecht, F.C. (1996) Electrical Methods in Geophysical Prospecting, London: Pergamon Press.
    [Google Scholar]
  20. Kitagawa, G., Takanami, T. and Matsumoto, N. (2004) State space approach to signal extraction problems in seismology. In: Brillinger, D.R., Robinson, E.A. and Schoenberg, F. P. (Eds.) Time Series Analysis and Applications to Geophysical Systems. New York: Springer.
    [Google Scholar]
  21. Kumar, D. and Ahmed, I. (2011) Seismic noise. In: Gupta, H.K. (Ed.) Encyclopedia of Solid Earth Geophysics. Dordrecht: Springer.
    [Google Scholar]
  22. Lavoué, F., Van Der Kruk, J., Rings, J., André, F., Moghadas, D., Huisman, J.A., et al. (2010) Electromagnetic induction calibration using apparent electrical conductivity modelling based on electrical resistivity tomography. Near Surface Geophysics, 8, 553–561.
    [Google Scholar]
  23. Mcneill, J.D. (1980) Electromagnetic terrain conductivity measurement at low induction numbers. Geonics Technical Note TN‐6.
  24. Mester, A., Zimmermann, E., Van Der Kruk, J., Vereecken, H. and Van Waasen, S. (2014) Development and drift‐analysis of a modular electromagnetic induction system for shallow ground conductivity measurements. Measurement Science and Technology, 25, 055801.
    [Google Scholar]
  25. Minsley, B.J., Smith, B.D., Hammack, R., Sams, J.I. and Veloski, G. (2012) Calibration and filtering strategies for frequency domain electromagnetic data. Journal of Applied Geophysics, 80, 56–66.
    [Google Scholar]
  26. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011) Scikit‐learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
    [Google Scholar]
  27. Pellerin, L. (2002) Applications of electrical and electromagnetic methods for environmental and geotechnical investigations. Surveys in Geophysics, 23, 101–132.
    [Google Scholar]
  28. Robinson, D.A., Lebron, I., Lesch, S.M. and Shouse, P. (2004) Minimizing drift in electrical conductivity measurements in high temperature environments using the EM‐38. Soil Science Society of America Journal, 68, 339.
    [Google Scholar]
  29. Scales, J.A. and Snieder, R. (1998) What is noise?Geophysics, 63, 1122–1124.
    [Google Scholar]
  30. Sherstinsky, A. (2018) Fundamentals of Recurrent Neural Network (RNN) and Long Short‐Term Memory (LSTM) Network. Elsevier.
  31. Simpson, D., Van Meirvenne, M., Saey, T., Vermeersch, H., Bourgeois, J., Lehouck, A., et al. (2009) Evaluating the multiple coil configurations of the EM38DD and DUALEM‐21S sensors to detect archaeological anomalies. Archaeological Prospection, 16, 91–102.
    [Google Scholar]
  32. Sudduth, K.A., Drummond, S.T. and Kitchen, N.R. (2001) Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture. Computers and Electronics in Agriculture, 31, 239–264.
    [Google Scholar]
  33. Tan, X., Mester, A., Von Hebel, C., Zimmermann, E., Vereecken, H., Van Waasen, S. and Van Der Kruk, J. (2019) Simultaneous calibration and inversion algorithm for multiconfiguration electromagnetic induction data acquired at multiple elevations. Geophysics, 84, EN1–EN14.
    [Google Scholar]
  34. Thiesson, J., Kessouri, P., Schamper, C. and Tabbagh, A. (2014) Calibration of frequency‐domain electromagnetic devices used in near‐surface surveying. Near Surface Geophysics, 12, 481–491.
    [Google Scholar]
  35. Von Hebel, C., Van Der Kruk, J., Huisman, J.A., Mester, A., Altdorff, D., Endres, A.L., et al. (2019) Calibration, Conversion, and quantitative multi‐layer inversion of multi‐coil rigid‐boom electromagnetic induction data. Sensors, 19, 4753.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1002/nsg.12160
Loading
/content/journals/10.1002/nsg.12160
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Calibration , Electromagnetic induction , Machine learning and Temperature
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