1887

Abstract

Summary

Despite the magnetotelluric method (MT) is one of the most prominent geophysical technique for deep subsoil exploration, it is not yet completely reliable when applied in urban or industrialized areas due to the presence of anthropic electromagnetic noise. The latter, indeed, may severely affect the MT recordings and, as a consequence, the impedance tensor estimates, which allow to retrieve the apparent resistivity values describing the underground electrical behaviour. In this work, a new filtering approach for MT data denoising is proposed. The procedure is based on the clustering of the impedance tensor estimates by using the Self-Organizing Map (SOM) neural network model. The clustering is performed in the time-frequency domain by a discrete wavelet transformation of the MT signals. In addition, a criterion for selecting, in each wavelet scale, the clusters that lead to the most reliable apparent resistivity estimates has been suggested. The application of the proposed filtering approach to synthetic MT signals has shown that the SOM clustering is very sensitive to the presence of noise and that it is possible to get consistent apparent resistivity curves.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201700564
2017-06-12
2020-06-03
Loading full text...

Full text loading...

References

  1. Carbonari, R., D’Auria, L., Di Maio, R. and Petrillo, Z.
    [2017] Denoising of magnetotelluric signals by polarization analysis in the discrete wavelet domain. Computers & Geosciences, 100, 135–141.
    [Google Scholar]
  2. Cottrell, M., Fort, J. C. and Pagès, G.
    [1998] Theoretical aspects of the SOM algorithm. Neurocomputing, 21(1), 119–138.
    [Google Scholar]
  3. D’Auria, L., Esposito, A. M., Petrillo, Z. and Siniscalchi, A.
    [2015] Denoising magnetotelluric recordings using Self-Organizing Maps. Advances in Neural Networks: Computational and Theoretical Issues, 137–147, extended abstract. Springer International Publishing.
    [Google Scholar]
  4. Egbert, G. D.
    [1997] Robust multiple-station magnetotelluric data processing. Geophysical Journal International, 130(2), 475–496.
    [Google Scholar]
  5. Escalas, M., Queralt, P., Ledo, J. and Marcuello, A.
    [2013] Polarisation analysis of magnetotelluric time series using a wavelet-based scheme: a method for detection and characterisation of cultural noise sources. Physics of the Earth and Planetary Interiors, 218, 31–50.
    [Google Scholar]
  6. Gamble, T., Goubau, W. M. and Clarke, J.
    [1979] Magnetotellurics with a remote magnetic reference. Geophysics, 44(1), 53–68.
    [Google Scholar]
  7. Kohonen, T.
    [1990] The self-organizing map. Proceedings of the IEEE, 78(9), 1464–1480.
    [Google Scholar]
  8. Kumar, P. and Foufoula-Georgiou, E.
    [1997] Wavelet analysis for geophysical applications. Reviews of Geophysics, 35(4), 385–412.
    [Google Scholar]
  9. Ritter, O., Junge, A. and Dawes, G. J.
    [1998] New equipment and processing for magnetotelluric remote reference observations. Geophysical Journal International, 132(3), 535–548.
    [Google Scholar]
  10. Sutarno, D. and Vozoff, K.
    [1989] Robust M-estimation of magnetotelluric impedance tensors. Exploration Geophysics, 20(3), 383–398.
    [Google Scholar]
  11. Vesanto, J. and Alhoniemi, E.
    [2000] Clustering of the self-organizing map. IEEE transactions on neural networks, 11(3), 586–600.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201700564
Loading
/content/papers/10.3997/2214-4609.201700564
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