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Power line interference noise elimination method based on independent component analysis in wavelet domain for magnetotelluric signal

  • Cao, Xiaoling (Key Laboratory of Exploration Technologies for Oil and Gas Resources in Ministry of Education, Yangtze University) ;
  • Yan, Liangjun (Key Laboratory of Exploration Technologies for Oil and Gas Resources in Ministry of Education, Yangtze University)
  • Received : 2017.04.27
  • Accepted : 2017.10.15
  • Published : 2018.11.30

Abstract

With the urbanization in recent years, the power line interference noise in electromagnetic signal is increasing day by day, and has gradually become an unavoidable component of noises in magnetotelluric signal detection. Therefore, a kind of power line interference noise elimination method based on independent component analysis in wavelet domain for magnetotelluric signal is put forward in this paper. The method first uses wavelet decomposition to change single-channel signal into multi-channel signal, and then takes advantage of blind source separation principle of independent component analysis to eliminate power line interference noise. There is no need to choose the layer number of wavelet decomposition and the wavelet base of wavelet decomposition according to the observed signal. On the treatment effect, it is better than the previous power line interference removal method based on independent component analysis. Through the de-noising processing to actual magnetotelluric measuring data, it is shown that this method makes both the apparent resistivity curve near 50 Hz and the phase curve near 50 Hz become smoother and steadier than before processing, i.e., it effectively eliminates the power line interference noise.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, Yangtze University

References

  1. Berdichevsky, M. N., & Dmitriev, V. I. (1976). Basic principles of interpretation of magnetotelluric sounding curves. In A. Adam (Ed.), Geoelectric & Geothermal Studies (pp. 165-221). KAPG Geophysical Monograph. Akademiai Kiado.
  2. Cai, J. H. (2016). A combinatorial filtering method for magnetotelluric data series with strong interference[J]. Arabian Journal of Geosciences, 9(13), 628. https://doi.org/10.1007/s12517-016-2658-5
  3. Chen, R., He, Z., He, L., & Liu, X. (2008). Random noise and coherent interference estimation of MT instrument[C]//2008 International Conference on Computer and Electrical Engineering. IEEE, 2009, 368-372.
  4. He, L. F., Wang, X. B., He, Z. X., & Li, C. F. (2001). Waveletbased denoising of MT time series. Journal of earthquake geology, 23(2), 222-226.
  5. Hyvärinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3), 626-634. https://doi.org/10.1109/72.761722
  6. Hyvärinen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications [J]. Neural Networks, 13(4-5), 411-430. https://doi.org/10.1016/S0893-6080(00)00026-5
  7. Kaufman, A. A., & Keller, G. V. (1981). The magnetotelluric sounding method[M]. Elsevier/North-Holland: Elsevier Scientific Pub. Co., Distributors for the U.S. and Canada.
  8. Kaushal, G., Jain, V. K., & Singh, A. (2015). Removal of power line interference from EEG using wavelet-ICA[J]. IJCA Proceedings on International Conference on Advancements in Engineering and Technology ICAET, 6, 29-31.
  9. Kou, Y. M., Xia, H. W., Liu, R., & Wang, C. H. (2011). Method for separation of low-frequency electromagnetic interference in geomagnetic navigation systems[J]. Journal of Harbin Institute of Technology, 43(7), 32-37.
  10. Li, J., Tang, J. T., & Xiao, X. (2011). De-noising algorithm for magnetotelluric signal based on mathematical morphology filtering[J]. Noise & Vibration Worldwide, 42(11), 65-72. https://doi.org/10.1260/0957-4565.42.11.65
  11. Liang, S. X., Zhang, S. Y., Huang, L. S., & Sun, S. C. (2012). The powerline interference in the magneto-electrotelluric exploration[J]. Geophysical & Geochemical Exploration, 36(5), 813-816.
  12. Ling, Z. B., Wang, P. Y., Wan, Y. X., Wang, Y. Z., Cheng, D. F., & Li, T. L. (2016). A combined wavelet transform algorithm used for de-noising magnetotellurics data in the strong human noise[J]. Chinese Journal of Geophysics, 59(9), 3436-3447.
  13. Rokityansky, I. I. (1982). Magnetotelluric sounding[M]//Geoelectromagnetic investigation of the earth's crust and mantle (pp. 186-246). Berlin Heidelberg: Springer.
  14. Szarka, L. (1988). Geophysical aspects of man-made electromagnetic noise in the earth-A review[J]. Surveys in Geophysics, 9(3-4), 287-318. https://doi.org/10.1007/BF01901627
  15. Tang, J. T., Li, H., Li, J., Qiang, J., & Xiao, X. (2014). Tophat transformation and magnetotelluric sounding data strong interference separation of Lujiang-Zongyang ore concentration area[J]. Journal of Jilin University, 44(1), 336-343.
  16. Tang, J. T., Hua, X. R., Cao, Z. M., & Ren, Z. Y. (2008). Hilbert-Huang transformation and noise suppression of magnetotelluric sounding data[J]. Chinese Journal of Geophysics, 51(2), 603-610.
  17. Tang, J. T., Li, J., Xiao, X., Xu, Z. M., Li, H., & Zhang, C. (2012). Magnetotelluric sounding data strong interference separation method based on mathematical morphology filtering[J]. Journal of Central South University, 43(6), 2215-2221.
  18. Thirionmoreau, N., & Amin, M. (2010). Handbook of blind source separation[M]. Oxford: Academic Press.
  19. Trad, D. O., & Travassos, J. M. (2000). Wavelet filtering of magnetotelluric data[J]. Geophysics, 65(2), 482-491. https://doi.org/10.1190/1.1444742
  20. Wang, H., Zhang, L., & Han, Y. (1997). Power frequency interference rejection in power line carrier communication[C]// International Symposium on Electromagnetic Compatibility Proceedings. IEEE, 312-315.
  21. Wang, S. M., & Wang, J. Y. (2004). Analysis on statistic characteristics of magnetotelluric signal. Journal of earthquake, 26(6), 669-674.
  22. Wu, X. P., Zhan, C. A., Zhou, H. Q., & Feng, H. Q. (2000). Removal of power interference from digital signals by using independent component analysis[J]. Journal of China university of science and technology, 30(6), 671-676.
  23. Xiao, X., Li, J., & Tang, J. T. (2012). Strong interference separation method based on morphology-median filtering for magnetotelluric sounding data in ore concentration area[J]. International Journal of Advancements in Computing Technology, 4(16), 396-403.
  24. Zhang, X., Hu, W. B., Yan, L. J., & Zhang, S. Z. (2002). Application of wavelet transformation of static correction in magnetotelluric depth measurement[J]. Journal of Jianghan Petroleum Institute, 24(2), 40-41.
  25. Zhang, Y., & Paulson, K. V. (1997). Enhancement of signalto-noise ratio in natural-source transient magnetotelluric data with wavelet transform[J]. Pure & Applied Geophysics, 149(2), 405-419. https://doi.org/10.1007/s000240050033

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