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Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

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Abstract

Determining P and S wave arrival times while minimizing noise is a major problem in seismic signal analysis. Precise determination of earthquake onset arrival timing, determination of earthquake magnitude, and calculation of other parameters that can be used to make more accurate seismic maps are possible with the detection of these waves. Experts try to determine these waves by manual analysis. But this process is time-consuming and painful. In this study, a new method that enables the determination of P and S wave arrival times in noisy recordings is recommended. This method is based on the hybrid usage of empirical mode decomposition and Teager–Kaiser energy operator algorithms. The results show that the proposed system gives effective results in the automatic detection of P and S wave arrival times. Promisingly, the recommended system might serve as a novel and powerful candidate for the effective detection of P and S wave arrival time.

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References

  1. Anant KS, Dowla FU (1997) Wavelet transform methods for phase identification in three-component seismograms. Bull Seismol Soc Am 87:1598–1612

    MathSciNet  Google Scholar 

  2. Colak OH, Destici TC, Arman H, Cerezci O (2009) Detection of P-and S-waves arrival times using the discrete wavelet transform in real seismograms. Arab J Sci Eng 34:79–89

    MATH  Google Scholar 

  3. Erturk A, Gullu MK, Erturk S (2013) Hyperspectral image classification using empirical mode decomposition with spectral gradient enhancement. IEEE Trans Geosci Remote Sens 51:2787–2798

    Article  Google Scholar 

  4. Ross ZE, Ben-Zion Y (2014) Automatic picking of direct P, S seismic phases and fault zone head waves. Geophys J Int 199:368–381

    Article  Google Scholar 

  5. Ross ZE, Ben-Zion Y (2014) An earthquake detection algorithm with pseudo-probabilities of multiple indicators. Geophys J Int 197:458–463

    Article  Google Scholar 

  6. Borcherdt RD (1992) Anatomy of Seismograms. Earthq Spectra 8:495–496

    Article  Google Scholar 

  7. Gelchinsky B, Shtivelman V (1983) Automatic picking of first arrivals and parameterization of traveltime curves. Geophys Prospect 31:915–928

    Article  Google Scholar 

  8. McCormack MD, Zaucha DE, Dushek DW (1993) First-break refraction event picking and seismic data trace editing using neural networks. Geophysics 58:67–78

    Article  Google Scholar 

  9. Boschetti F, Dentith MD, List RD (1996) A fractal-based algorithm for detecting first arrivals on seismic traces. Geophysics 61:1095–1102

    Article  Google Scholar 

  10. Tong C, Kennett BLN (1996) Automatic seismic event recognition and later phase identification for broadband seismograms. Bull Seismol Soc Am 86:1896–1909

    Google Scholar 

  11. Wagner GS, Owens TJ (1996) Signal detection using multi-channel seismic data. Bull Seismol Soc Am 86:221–231

    Google Scholar 

  12. Withers M, Aster R, Young C, Beiriger J, Harris M, Moore S, Trujillo J (1998) A comparison of select trigger algorithms for automated global seismic phase and event detection. Bull Seismol Soc Am 88:95–106

    Google Scholar 

  13. Hidani A, Yamanaka H (2003) Automatic picking of seismic arrivals in strong motion data using an artificial neural network. Environmental Science and Technology, Tokyo Institute of Technology

  14. Zhang H, Thurber C, Rowe C (2003) Automatic P-wave arrival detection and picking with multiscale wavelet analysis for single-component recordings. Bull Seismol Soc Am 93:1904–1912

    Article  Google Scholar 

  15. Takanami T, Kitagawa G (2003) Methods and applications of signal processing in seismic network operations. In: Bhattacharji S, Friedman GM, Neugebauer HJ, Seilacher A (eds) Lecture Notes in Earth Sciences, vol 98. Springer, Berlin, p 2003

    Google Scholar 

  16. Hafez AG, Khan MTA, Kohda T (2010) Clear P-wave arrival of weak events and automatic onset determination using wavelet filter banks. Digit Signal Process 20:715–723

    Article  Google Scholar 

  17. Liu X (2013) Time-arrival location of seismic P-wave based on wavelet transform modulus maxima. J Multimed 8:32–39

    Google Scholar 

  18. Hafez AG, Rabie M, Kohda T (2013) Seismic noise study for accurate P-wave arrival detection via MODWT. Comput Geosci 54:148–159

    Article  Google Scholar 

  19. Wong J, Han L, Bancroft JC, Stewart RR (2009) Automatic time-picking of first arrivals on noisy microseismic data. STA 2:1

    Google Scholar 

  20. Allen RV (1978) Automatic earthquake recognition and timing from single traces. Bull Seismol Soc Am 68:1521–1532

    Google Scholar 

  21. Magotra N, Ahmed N, Chael E (1987) Seismic event detection and source location using single-station (three-component) data. Bull Seismol Soc Am 77:958–971

    Google Scholar 

  22. Magotra N, Ahmed N, Chael E (1989) Single-station seismic event detection and location. IEEE Trans Geosci Remote Sens 27:15–23

    Article  Google Scholar 

  23. Wang J, Teng T-L (1995) Artificial neural network-based seismic detector. Bull Seismol Soc Am 85:308–319

    Google Scholar 

  24. Zhao Y, Takano K (1999) An artificial neural network approach for broadband seismic phase picking. Bull Seismol Soc Am 89:670–680

    Google Scholar 

  25. Gentili S, Michelini A (2006) Automatic picking of P and S phases using a neural tree. J Seismol 10:39–63

    Article  Google Scholar 

  26. Ahmed A, Sharma ML, Sharma A (2014) Wavelet based automatic phase picking algorithm for 3-component broadband seismological data. J Seismol Earthq Eng 9:15–24

    Google Scholar 

  27. Hafez AG, Khan TA, Kohda T (2009) Earthquake onset detection using spectro-ratio on multi-threshold time–frequency sub-band. Digit Signal Process 19:118–126

    Article  Google Scholar 

  28. Xiantai G, Zhimin L, Na Q, Weidong J (2011) Adaptive picking of microseismic event arrival using a power spectrum envelope. Comput Geosci 37:158–164

    Article  Google Scholar 

  29. Sleeman R, van Eck T (1999) Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings. Phys Earth Planet Inter 113:265–275

    Article  Google Scholar 

  30. Panagiotakis C, Kokinou E, Vallianatos F (2008) Automatic P-phase picking based on local-maxima distribution. IEEE Trans Geosci Remote Sens 46:2280–2287

    Article  Google Scholar 

  31. Saragiotis CD, Hadjileontiadis LJ, Panas SM (2002) PAI–S/K: a robust automatic seismic P phase arrival identification scheme. IEEE Trans Geosci Remote Sens 40:1395–1404

    Article  Google Scholar 

  32. Küperkoch L, Meier T, Lee J, Friederich W, Working Group, E (2010) Automated determination of P-phase arrival times at regional and local distances using higher order statistics. Geophys J Int 181:1159–1170

    Google Scholar 

  33. Taylor KM, Procopio MJ, Young CJ, Meyer FG (2011) Estimation of arrival times from seismic waves: a manifold-based approach. Geophys J Int 185:435–452

    Article  Google Scholar 

  34. Incorporated Research Institutions For Seismology (IRIS) http://ds.iris.edu/wilber3/. Accessed 2 November 2014

  35. An X, Yang J (2015) Denoising of hydropower unit vibration signal based on variational mode decomposition and approximate entropy. Trans Inst Meas. Control. 0142331215592064

  36. Wang Z, Lu C, Wang Z, Liu H, Fan H (2013) Fault diagnosis and health assessment for bearings using the Mahalanobis–Taguchi system based on EMD-SVD. Trans Inst Meas Control. 0142331212472929

  37. Jabloun F, Cetin AE, Erzin E (1999) Teager energy based feature parameters for speech recognition in car noise. IEEE Signal Process Lett 6:259–261

    Article  Google Scholar 

  38. Bahoura M, Rouat J (2001) Wavelet speech enhancement based on the Teager energy operator. IEEE Signal Process Lett 8:10–12

    Article  Google Scholar 

  39. Kaiser JF (1990) On a simple algorithm to calculate the ‘energy’ of a signal. In: 1990 international conference on acoustics, speech, and signal processing. ICASSP-90. vol 1, pp 381–384

  40. Laasri EHA, Akhouayri E-S, Agliz D, Atmani A (2014) Automatic detection and picking of P-wave arrival in locally stationary noise using cross-correlation. Digit Signal Process 26:87–100

    Article  Google Scholar 

  41. Cichowicz A (1993) An automatic S-phase picker. Bull Seismol Soc Am 83:180–189

    Google Scholar 

  42. Wang J, Teng T (1997) Identification and picking of S phase using an artificial neural network. Bull Seismol Soc Am 87:1140–1149

    Google Scholar 

  43. Tasic I, Grabec I (2000) Characterization of seismic waves arrival with simulated neural networks;. I. Ljubljana, Tasic

    Google Scholar 

  44. Saragiotis CD, Hadjileontiadis LJ, Savvaidis AS, Papazachos CB, Panas SM (2000) Automatic S-phase arrival determination of seismic signals using nonlinear filtering and higher-order statistics. In Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International. vol 1, pp 292–294

  45. Tasic I, Runovc F (2009) Automatic S-phase arrival identification for local earthquakes. Acta Geotech Slov 6:46–55

    Google Scholar 

  46. Aboamer MM, Azar AT, Wahba K, Mohamed ASA (2014) Linear model-based estimation of blood pressure and cardiac output for Normal and Paranoid cases. Neural Comput Appl 25(6):1223–1240. doi:10.1007/s00521-014-1566-4

    Article  Google Scholar 

  47. Aboamer MM, Azar AT, Mohamed ASA, Bär KJ, Berger BS, Wahba K (2014) Nonlinear features of heart rate variability in paranoid schizophrenic. Neural Comput Appl 25(7–8):1535–1555. doi:10.1007/s00521-014-1621-1

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering and Architecture, Mehmet Akif Ersoy University, Burdur, Turkey

    Ismail Kirbas

  2. Department of Information Systems Engineering, Mugla Sitki Kocman University, Muğla, Turkey

    Musa Peker

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  1. Ismail Kirbas
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  2. Musa Peker
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Correspondence to Ismail Kirbas.

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Kirbas, I., Peker, M. Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis. Neural Comput & Applic 28, 3035–3045 (2017). https://doi.org/10.1007/s00521-016-2333-5

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  • DOI: https://doi.org/10.1007/s00521-016-2333-5

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