1887
Volume 40, Issue 2
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

Prevailing methods of magnetotelluric (MT) data analysis determine the spectra using variations of the Fourier Transform (FT), which is based on the principle of signal stationarity. However, MT data series are non-stationary random signals that do not meet the basic requirements of conventional methods based on the FT. In recent years, the Hilbert–Huang Transform (HHT) has been regarded as a powerful tool for adaptive analysis of non-linear and non-stationary signals. This paper proposes for the first time the adoption of a new method of analysis for MT data, and focuses on two aspects that are facilitated by applying the HHT. The first aspect is the pretreatment of the MT time series data through selecting MT data subsets, and noise suppression; the other concerns the determination of the impedance and apparent resistivity using the HHT instantaneous spectrum. The conclusion reached through discussion of the first aspect is that the proposed methods can greatly improve the quality of MT data. The conclusion drawn from the second aspect is that the HHT instantaneous spectrum method can overcome the problems described above, and obtain stable and reliable estimation of the impedance tensor, and thus naturally minimise the estimation bias brought about by the non-stationary characteristics of MT data. Therefore, the HHT method is effective in analysing MT data and is able to generate meaningful geological information.

Loading

Article metrics loading...

/content/journals/10.1071/EG08124
2009-06-01
2026-01-14
Loading full text...

Full text loading...

References

  1. Battista B. M. Knapp C. McGee T. Goebel V. 2007Application of the empirical mode decomposition and Hilbert–Huang transform to seismic reflection data: Geophysics 72 H29 H37 doi:10.1190/1.2437700
    [Google Scholar]
  2. Berdichevskii M.P. 1973Magnetotelluric sounding with applications to mathematical filters: Physics of the Earth 3 68 76 [in Russian]
    [Google Scholar]
  3. Chant I. J. Hastie L. M. 1990The Wigner-Ville analysis of magnetotelluric signals: Proceedings Geological Society of Australia 25 89 90
    [Google Scholar]
  4. Chant I. J. Hastie L. M. 1992Time-frequency analysis of magnetotelluric data: Geophysics 111 399 413
    [Google Scholar]
  5. Chen L. S. H. Bai G. X. 1984The transient spectrum method for analyzing Magnetotelluric data: Oil Geophysical Prospecting 12 562 574 [in Chinese]
    [Google Scholar]
  6. Coca D. Billings S. A. 1997Continuous-time system identification for linear and nonlinear systems using wavelet decompositions: International Journal of Bifurcation and Chaos in Applied Sciences and Engineering 1 87 96 doi:10.1142/S0218127497000066
    [Google Scholar]
  7. Flandrin P. Rilling G. Goncalves P. 2004Empirical mode decomposition as a filter bank: IEEE Signal Processing Letters 11 112 114 doi:10.1109/LSP.2003.821662
    [Google Scholar]
  8. Gurley K. Kareem A. 1999Applications of wavelet transforms in earthquake, wind and ocean engineering: Engineering Structures 21 149 167
    [Google Scholar]
  9. Huang N. E. Shen Z. Long S. R. Wu M. C. Shih H. H. 1998The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis: Proceedings of the Royal Society of London Series A 454 903 995 doi:10.1098/rspa.1998.0193
    [Google Scholar]
  10. Huang N. E. Wu M. C. Long S. R. Qu W. Gloersen P. 2003A confidence limit for the empirical mode decomposition and the Hilbert spectral analysis: Proceedings of the Royal Society of London Series A 459 2317 2345 doi:10.1098/rspa.2003.1123
    [Google Scholar]
  11. Huang N. E. 2005, The Hilbert–Huang transform in engineering, Boca Raton: CRC Press, p. 1–23.
  12. Huele R. Haes H. 1998Identification of individual sperm whales by wavelet transform of the trailing edge of the flukes: Marine Mammal Science 14 143 145 doi:10.1111/j.1748-7692.1998.tb00697.x
    [Google Scholar]
  13. Jiang R. Yan H. 2008Studies of spectral properties of short genes using the wavelet subspace Hilbert–Huang transform (WSHHT): Physica A 387 4223 4247 doi:10.1016/j.physa.2008.02.076
    [Google Scholar]
  14. Kauffman A. A. , and Keller G. V. 1987, Magnetotelluric Sounding Method, Beijing, Geological Publishing House, 432–475.
  15. Peng Z. K. Tse P. W. Chu F. L. 2005An improved Hilbert–Huang transform and its application in vibration signal analysis: Journal of Sound and Vibration 286 187 205 doi:10.1016/j.jsv.2004.10.005
    [Google Scholar]
  16. Qin S. R. Zhong Y. M. 2006A new envelope algorithm of Hilbert–Huang transform: Mechanical Systems and Signal Processing 20 1941 1952 doi:10.1016/j.ymssp.2005.07.002
    [Google Scholar]
  17. Rato R. T. Ortigueira M. D. Batista A. G. 2008On the HHT, its problems, and some solutions: Mechanical Systems and Signal Processing 22 1374 1394 doi:10.1016/j.ymssp.2007.11.028
    [Google Scholar]
  18. Shi C. X. Luo Q. F. 2003Hilbert–Huang transform and wavelet analysis of time history signal: Acta Seismologica Sinica 25 398 405 [in Chinese]
    [Google Scholar]
  19. Trad D. O. Travassos J. M. 2000Wavelet filtering of magnetotelluric data: Geophysics 65 482 491 doi:10.1190/1.1444742
    [Google Scholar]
  20. Wang S. M. Wang J. Y. 2004 a Analysis on statistic characteristics of magnetotelluric signal: Acta Seismologica Sinica 26 669 674 [in Chinese]
    [Google Scholar]
  21. Wang S. M. Wang J. Y. 2004 b Discussion on the non-minimum phase of magnetotelluric signals: Progress in Geophysics 19 216 221 [in Chinese]
    [Google Scholar]
  22. Zhang Q. S. Wang J. Y. 2004A method of noise elimination for magnetotelluric sounding data: Oil Geophysical Prospecting 39 17 23 [in Chinese]
    [Google Scholar]
/content/journals/10.1071/EG08124
Loading
/content/journals/10.1071/EG08124
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

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