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Applied Geophysics

, Volume 16, Issue 2, pp 160–170 | Cite as

Magnetotelluric signal-noise separation method based on SVM–CEEMDWT

  • Jin LiEmail author
  • Jin Cai
  • Jing-Tian Tang
  • Guang Li
  • Xian Zhang
  • Zhi-Min Xu
Electrical & Electromagnetic Methods

Abstract

To better retain useful weak low-frequency magnetotelluric (MT) signals with strong interference during MT data processing, we propose a SVM-CEEMDWT based MT data signal-noise separation method, which extracts the weak MT signal affected by strong interference. First, the approximate entropy, fuzzy entropy, sample entropy, and Lempel-Ziv (LZ) complexity are extracted from the magnetotelluric data. Then, four robust parameters are used as the inputs to the support vector machine (SVM) to train the sample library and build a model based on the different complexity of signals. Based on this model, we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition (CEEMD) and wavelet threshold (WT) for noise suppression. Simulation results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise suppression. Compared with the CEEMDWT, the proposed SVM-CEEMDWT method retains more low-frequency low-variability information, and the apparent resistivity curve is smoother and more continuous. Moreover, the results better reflect the deep electrical structure in the field.

Keywords

SVM-CEEMDWT magnetotelluric signal-noise separation MT data processing 

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References

  1. Chen, W., Wang, Z. Z., Xie, H. B., et al., 2007, Characterization of surface EMG signal based on fuzzy entropy: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(2), 266–272.Google Scholar
  2. Di, Q.Y., Wang, M.Y., Wang, R., et al., 2008, Study of long bipole and large power electromagnetic field: Chinese Journal of Geophysics, 51(6): 1917–1928.Google Scholar
  3. Filipo, W. A. S., and Hohmann, G. W., 1983, Computer simulation of low-frequency electromagnetic data acquisition: Geophysics, 42(6), 1265–1276.Google Scholar
  4. Gangi, A. F., and Shapiro, J. N., 1979, A propagating algorithm for determining nth-order polynomial, leastsquares fits: Geophysics, 48(9), 1219–1232.Google Scholar
  5. Han, J., and Mirko, V. D. B., 2013, Empirical mode decomposition for seismic time-frequency analysis: Geophysics, 78(2), O9–O19.Google Scholar
  6. Huang, N. E., Shen, Z., Long, S. R., et al., 1998, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-station time series analysis: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 454, 903–995.Google Scholar
  7. Lake, D. E., Richman, J. S., Griffin, M. P., et al., 2002, Sample entropy analysis of neonatal heart rate variability: Am J Physiol Regul Integr Comp Physiol, 283(3), R789–R797.Google Scholar
  8. Lempel, A., and Ziv, J., 1976, On the complexity of finite sequences: IEEE Transactions on Information Theory, 22(1), 75–81.Google Scholar
  9. Li, G., Xiao, X., Tang, J. T., et al., 2017, Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering: Applied Geophysics, 14(4), 581–589.Google Scholar
  10. Li, J., Zhang, X., Gong, J. Z., et al., 2018a, Signal-noise identification of magnetotelluric signals using fractalentropy and clustering algorithm for targeted denoising: Fractals, 26(2), 1840011.Google Scholar
  11. Li, J., Zhang, X., Tang, J. T., et al., 2019, Audio magnetotelluric signal-noise identification and separation based on multifractal spectrum and matching pursuit: Fractals, 27(1), 1940007.Google Scholar
  12. Li, T., Wang, R., Wang, Z., et al., 2018b, Prediction of fracture density using genetic algorithm support vector machine based on acoustic logging data: Geophysics, 83(2), D49–D60.Google Scholar
  13. Liu, C., Chen, C. L., Wang, D., et al., 2015, Seismic dip estimation based on the two-dimensional Hilbert transform and its application in random noise attenuation: Applied Geophysics, 12(1), 55–63.Google Scholar
  14. Liu, L., and Wang, T., 2008, Comparison of TOPS strings based on LZ complexity: Journal of Theoretical Biology, 251(1), 159–166.Google Scholar
  15. Liu, W. Q., Lü, Q. T., Chen, R. J., et al., 2019, A modified empirical mode decomposition method for multiperiod time-series detrending and the application in full-waveform induced polarization data: Geophysical Journal International, 217(2), 1058–1079.Google Scholar
  16. Lu, W., and Zhang, C. K., 2013, Robust estimation of instantaneous phase using a time-frequency adaptive filter: Geophysics, 78(1), O1–O7.Google Scholar
  17. Pal, M., and Foody, G. M., 2010, Feature selection for classification of hyperspectral data by SVM: IEEE Transactions on Geoscience & Remote Sensing, 48(5), 2297–2307.Google Scholar
  18. Pincus, S. M., 1991, Approximate entropy as a measure of system complexity: Proceedings of the National Academy of Sciences of the United States of America, 88, 2297–2301.Google Scholar
  19. Ren, Z. Y., Kalscheuer, T., Greenhalgh, S., et al., 2013, A goal-oriented adaptive finite-element approach for plane wave 3-D electromagnetic modelling: Geophysical Journal International, 194(2), 700–718.Google Scholar
  20. Richman, J. S., and Moorman, J. R., 2000, Physiological time-series analysis using approximate entropy and sample entropy: Am J Physiol Heart Circ Physiol, 278(6), H2039–H2049.Google Scholar
  21. Samui, P., and Kim, D., 2013, Determination of reservoir induced earthquake using support vector machine and gaussian process regression: Applied Geophysics, 10(2), 229–234.Google Scholar
  22. Trad, D. O., and Travassos, J. M., 2000, Wavelet filtering of magnetotelluric data: Geophysics, 65(2), 482–491.Google Scholar
  23. Weckmann, U., Magunia, A., and Wang, D. X., 2005, Effective noise separation for magnetotelluric single site data processing using a frequency domain selection scheme: Geophysical Journal International, 161(3), 635–652.Google Scholar
  24. Wei, W., Cai, J. C., Hu, X. Y., et al., 2015, An electrical conductivity model for fractal porous media: Geophysical Research Letters, 42(12), 4833–4840.Google Scholar
  25. Wen, X. T., He, Z. H., and Huang, D. J., 2009, Reservoir detection based on EMD and correlation dimension: Applied Geophysics, 6(1), 70–76.Google Scholar
  26. Yeh, J. R., Shieh, J. S., and Huang, N. E., 2010, Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method: Advances in Adaptive Data Analysis, 2(2), 135–156.Google Scholar
  27. Zhang, J., Yan, R. Q., Gao, R. X., et al., 2010, Performance enhancement of ensemble empirical mode decomposition: Mechanical Systems and Signal Processing, 24(7), 2104–2123.Google Scholar

Copyright information

© The Editorial Department of APPLIED GEOPHYSICS 2019

Authors and Affiliations

  • Jin Li
    • 1
    • 2
    Email author
  • Jin Cai
    • 1
  • Jing-Tian Tang
    • 2
  • Guang Li
    • 2
    • 3
  • Xian Zhang
    • 1
  • Zhi-Min Xu
    • 2
  1. 1.Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and EngineeringHunan Normal UniversityChangshaChina
  2. 2.School of Geosciences and Info-Physics, Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor, Ministry of EducationCentral South UniversityChangshaChina
  3. 3.State Key Laboratory Breeding Base of Nuclear Resources and EnvironmentEast China University of TechnologyNanchangChina

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