1887

Abstract

Summary

Transient electromagnetics (TEM) is a well-established method for imaging of sub-surface resistivity structures. The method is often used from a moving platform providing densely sampled measurements of the sub-surface structures in 2D or 3D. When measurements are performed close to man-made conductors e.g. buried cables and pipes, the data will be contaminated by coupling artefacts, which must be culled from data sets prior to inversion. This can be a labor-intensive task for large data sets and several methods for automation of this process has been investigated. Here we explore the use of a convolutional neural network (CNN) for detection of couplings in data from a towed, ground-based TEM system. The CNN consists of two 1D convolutional layers followed by three fully connected layers. The proposed method is evaluated by comparing with a neural network based approach that have previously been used to detect couplings in airborne TEM data and is found to have better performance. The performance is presently limited by the size of the training data set.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201902429
2019-09-08
2024-03-29
Loading full text...

Full text loading...

References

  1. Andersen, K. K., Kirkegaard, C., Foged, N., Christiansen, A. V., and Auken, E.
    [2015] Artificial neural networks for removal of couplings in air-borne transient electromagnetic data. Geophysical Prospecting, 64, 741–752.
    [Google Scholar]
  2. Auken, E., Foged, N., Dath, S. M., Lassen, K. V. T., Larsen, J. J., Eiskjœr, T. T., and Maurya, P. K.
    [2019] tTEM - a towed TEM-system for detailed 3D imaging of the top 70 meters of the subsurface. Geophysics, 84, E13-E22.
    [Google Scholar]
  3. Bedrosian, P., Schamper, C., Auken, E.
    [2016], A comparison of helicopter-borne electromagnetic systems for hydrogeologic studies, Geophysical Prospecting, 64, 192–215.
    [Google Scholar]
  4. Goodfellow, I., Bengio, Y. and Courville, A.
    [2016] Deep learning, MIT Press.
    [Google Scholar]
  5. Kingma, D. P., and Ba, J.
    Adam: A method for stochastic optimization [2014] Proceedings of the 3rd International Conference on Learning Representations.
    [Google Scholar]
  6. Nabighian, M. N., and Macnae, J. C.
    [1991], Time domain electromagnetic prospecting methods, in Nabighian, M. N., ed., Electromagnetic Methods in Applied Geophysics, volume 2: SEG, Investigations in Geophysics No. 3, 427–520.
    [Google Scholar]
  7. Pfaffhuber, A. A., Grimstad, E., Domaas, U., Auken, E., Foged, N., and Halkjaer, M.
    [2010] Airborne EM mapping of rockslides and tunneling hazards. The Leading Edge, 29, 956–959.
    [Google Scholar]
  8. Reninger, P.-A., Martelet, G., Deparis, J., Perrin, J., Chen, Y.
    [2011] Singular value decomposition as a denoising tool for airborne time domain electromagnetic data. Journal of Applied Geophysics, 75, 264–276.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201902429
Loading
/content/papers/10.3997/2214-4609.201902429
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