1887

Abstract

Summary

Modern airborne transient electromagnetic surveys typically span thousands of line kilometres requiring careful data processing. When surveys are flown in populated areas, data processing becomes particularly time consuming, since the acquired data is contaminated by couplings to man made conductors (power lines, fences, pipes, etc.). Coupled soundings must be removed from the dataset prior to inversion, but since the signature of couplings can be subtle and difficult to describe in general terms it has so far remained mostly a manual task. We train an artificial neural network to recognize coupled soundings in previously processed data and use this network to identify couplings in other data. The approach provides a dramatic reduction in the time required for data processing, since one can directly apply the network to the raw data. We present the training state and performance of the network and compare inversions based on manually processed data and ANN processed data. The results show that a well trained network can produce a high quality processing of ATEM data, which are either ready for inversion or in need of minimal manual processing. The results are very promising and can significantly reduce the processing time and cost of large ATEM surveys.

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/content/papers/10.3997/2214-4609.201413876
2015-09-06
2020-07-14
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References

  1. Auken, E., Christiansen, A.V., Westergaard, J.A., Kirkegaard, C., Foged, N. and Viezzoli, A.
    [2009] An integrated processing scheme for high-resolution airborne electromagnetic surveys, the SkyTEM system. Exploration Geophysics, 40, 184–192.
    [Google Scholar]
  2. Danielsen, J.E., Auken, E., Jorgensen, F., Søndergaard, V.H. and Sorensen, K.I.
    [2003] The application of the transient electromagnetic method in hydrogeophysical surveys. Journal of Applied Geophysics, 53(4), 181–198.
    [Google Scholar]
  3. Jorgensen, F., Scheer, W., Thomsen, S., Sonnenborg, T. O., Hinsby, K., Wiederhold, H., Schamper, C., Roth, B., Kirsch, R. and Auken, E.
    [2012] Transboundary geophysical mapping of geological elements and salinity distribution critical for the assessment of future sea water intrusion in response to sea level rise. Hydrology and Earth System Sciences, 16, 1845–1962.
    [Google Scholar]
  4. Poulton, M.M.
    [2002] Neural networks as an intelligence amplification tool: A review of applications. Geophysics, 67(3), 979–993.
    [Google Scholar]
  5. Raiche, A.
    [1991] A pattern recognition approach to geophysical inversion using neural nets. Geophysical Journal International, 105(3), 629–648.
    [Google Scholar]
  6. Reninger, P.A., Martelet, G., Deparis, J., Perrin, J. and Chen, Y.
    [2011] Singular value decomposition as a denoising tool for airborne time domain electromagnetic data. Journal of Applied Geophysics, 75(2), 264–276.
    [Google Scholar]
  7. Zhu, K.G., Ma, M.Y., Che, H.W., Yang, E.W., Ji, Y.J., Yu, S.B. and Lin, J.
    [2012] PC-based artificial neural network inversion for airborne time-domain electromagnetic data. Applied Geophysics, 9(1), 1–8.
    [Google Scholar]
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