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Journal of Seismology

, Volume 23, Issue 5, pp 1031–1046 | Cite as

Automatic P-wave picking using undecimated wavelet transform

  • Mohammad Shokri Kaveh
  • Reza MansouriEmail author
  • Ahmad Keshavarz
Original Article
  • 92 Downloads

Abstract

From the seismologists’ point of view, it is extremely important to accurately detect the first P wave arrival time. The P wave arrivals have considerable information about events such as location, magnitude, mechanism, and source parameters. In the classic methods, P wave pickings have been accomplished manually in a visual way. But in the era of information and communication technology, it can be done by computer programs. Seismologists have developed many methods for the picking of the first arrival time of P wave. The wavelet transform is one of the methods to analyze the arrival times and useful for picking up the singularities of any function. Decomposing signals by wavelet transform is a master key to the study of time-frequency varying signals such as earthquake seismograms. This paper presents P phase picking without any prior information using undecimated wavelet transform. For undertaking this study, a simple envelope characteristic function is used for P phase picking. The proposed method is tested on 5 earthquakes recorded by the Fnet network in Japan that have varying signal-to-noise ratio levels for calibrating. Then the method is applied on 50 earthquakes. The observed results are compared with manual phase picking and standard STA/LTA method. The wavelet base method shows the higher accuracy of phase picking in event detection and time picking, respect to the standard STA/LTA method, when compared to manual picking.

Keywords

Wavelet transform Phase picking Japan Arrival time Signal Earthquake 

Notes

Acknowledgements

We are grateful to Dr. Shobeyr Ashkpour for his precious information on the phase picking information and assistance in the research. The authors would like to thank Dr. Scafidi and anonymous reviewer for insightful comments which led us to improvement of the works. We also thank the Fnet network for providing the waveforms.

Funding information

The research did partially benefit of funds by Persian Gulf University of Iran in the framework of the MSc Project.

References

  1. Addison PS (2005) Wavelet transforms and the ECG: a review. Physiol Meas 26:R155–R199CrossRefGoogle Scholar
  2. Ahmed AM, Sharma ML, Sharma A (2007) Wavelet based automatic phase picking algorithm for 3-component broadband seismological data. JSEE 9:15Google Scholar
  3. Allen RV (1978) Automatic earthquake recognition and timing from signal traces. Bull Seism Soc Amer 68:1521–1532Google Scholar
  4. Anant KS, Dowla FU (1997) Wavelet transform methods for phase identification in three-component seismograms. Bull Seismol Soc Amer 87:1598–1612Google Scholar
  5. Akazawa, Takashi (2004) A technique for automatic detection of onset time of P-and S-phases in strong motion records. In: Proceedings of the 13th world conference on earthquake engineeringGoogle Scholar
  6. Akram J, Eaton EW (2016) A review and appraisal of arrival-time picking methods for downhole microseismic data. Geophysics 81:KS71–KS91CrossRefGoogle Scholar
  7. Akram J, Eaton EW, Onge ASt (2013) Automatic event-detection and time-picking algorithms for downhole microseismic data processing. 4th EAGE Passive Seismic WorkshopGoogle Scholar
  8. Boggess A, Narcowich F, Donoh DL, Donoh PL (2002) A first course in wavelets with fourier analysis. Physics Today 55:63Google Scholar
  9. Bogiatzis P, Ishii M (2015) Continuous wavelet decomposition algorithms for automatic detection of compressional-and shear-wave arrival times. Bull Seism Soc Amer 105:1628–1641CrossRefGoogle Scholar
  10. Baer M, Kradolfer U (1978) An automatic phase picker for local and teleseismic event. Bull Seismol Soc Amer 77:437–1445Google Scholar
  11. Choudhary MM (2015) P-wave onset point detection for seismic signal using bhattacharyya distance. SPIJ 9:38Google Scholar
  12. Daubechies I (1992) Ten lectures on wavelets. SIAM 61:289–312Google Scholar
  13. Elmansouri K, Rachid L, Maoulainine F (2014) FPGA based on electrocardiogram noise cancellingGoogle Scholar
  14. Gendron P, Ebel J, Manolakis D (2000) Rapid joint detection and classification with wavelet bases via Bayes theorem. Bull Seismolo l Soc Amer 90:764–774CrossRefGoogle Scholar
  15. Gibbons SJ, Ringdal F, Kværna T (2008) Detection and characterization of seismic phases using continuous spectral estimation on incoherent and partially coherent arrays. Geophys J Int 172:405–421CrossRefGoogle Scholar
  16. Gyaourova A, Kamath C, Fodor I K (2002) Undecimated wavelet transforms for image de-noising. No. UCRL-ID-150931. Lawrence Livermore National LabGoogle Scholar
  17. Han L (2010) Microseismic monitoring and hypocenter location. (Doctoral dissertation, University of Calgary)Google Scholar
  18. Karamzadeh N, Javan-Doloei G, Voss P, Reza AM (2012) Automatic detection and picking of local and regional S-waves. JSEE 14:165–181Google Scholar
  19. Karamzadeh N, Javan-Doloei G, Voss P, Reza AM (2013) Automatic earthquake signal onset picking based on the continuous wavelet transform. IEEE Trans Geosci Remote Sens 51:2666–2674CrossRefGoogle Scholar
  20. Li X, Dong S, Yuan Z (1999) Discrete wavelet transform for tool breakage monitoring. Int J Mach Tools Manuf 39:1935–1944CrossRefGoogle Scholar
  21. Li X, Dong S, Yuan Z (2013) A comparative study between discrete wavelet transform and maximal overlap discrete wavelet transform for testing stationarity. Int J Math Comput Phys Electr Comput Eng 7:1677–1681Google Scholar
  22. Morita Y, Hamaguchi H (1984) Automatic detection of onset time of seismic waves and its confidence interval using the autoregressive model fitting. Zisin (J Seism Soc Japan) 37:281–293Google Scholar
  23. Maity D, Aminzadeh F, Karrenbach M (2014) Novel hybrid artificial neural network based autopicking workflow for passive seismic data. Geophysical Prospecting 62:834–847CrossRefGoogle Scholar
  24. Mallat S (1998) A wavelet tour of signal processing San Diego, CA: AcademicGoogle Scholar
  25. Mousavi SM, Langston CA, Horton SP (2016) Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform. Geophysics 81:V341–V355CrossRefGoogle Scholar
  26. Percival DB, Walden V (2000) Wavelet methods for time series analysis. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  27. Okada Y, Kasahara K, Hori S, Obara K, Sekiguchi S, Fujiwara H, Yamamoto A (2004) Recent progress of seismic observation networks in Japan -Hi-net, F-net, K-NET and KiK-net. Earth Planets and Space 56:xv–xxviiiCrossRefGoogle Scholar
  28. Percival DB, Mofjeld HO (1997) Analysis of subtidal coastal sea level fluctuations using wavelets. JASA 92:868–880CrossRefGoogle Scholar
  29. Ruud BO, Husebye ES (1992) A new three-component detector and automatic single-station bulletin production. Bull Seismol Soc Amer 82:221–237Google Scholar
  30. Sifuzzaman M, Islam MR, Ali MZ (2009) Application of wavelet transform and its advantages compared to fourier transform. J Phys Sci 13:121–134Google Scholar
  31. Struzik ZR (2001) Wavelet methods in (financial) time-series processing. Physica A: Statistical Mechanics and its Applications 296:307–319CrossRefGoogle Scholar
  32. Starck JL, Fadili J, Murtagh F (2007) The undecimated wavelet decomposition and its reconstruction. IEEE Trans Image Process 16:297–309CrossRefGoogle Scholar
  33. Shang X, Li X, Morales-Esteban A, Dong L (2017) Enhancing micro-seismic P-phase arrival picking: EMD-cosine function-based denoising with an application to the AIC picker. J Appl Geophys 150:325–337CrossRefGoogle Scholar
  34. Tarvainen, Matti (1992) Automatic seismogram analysis: statistical phase picking and locating methods using one-station three-component data. Bull Seismol Soc Amer 82:860– 869Google Scholar
  35. Tibuleac M, Herrin ET (1999) An automatic method for determination of Lg arrival times using wavelet transforms. Seismol Res Lett 70:577–595CrossRefGoogle Scholar
  36. Vetterli M (2013) Subband coding. Subband Image Coding 115:43Google Scholar
  37. Withers M, Aster R, Young C, Beiriger J, Harris M, Harris S, Trujillo J (2013) A comparison of select trigger algorithms for automated global seismic phase and event detection. Bull Seism Soc Amer 88:95–106Google Scholar
  38. Wickerhauser MV (1994) Adapted wavelet analysis from theory to software. AK Peters, BostonGoogle Scholar
  39. 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 Amer 93:1904–1912CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Persian Gulf University (PGU)BushehrIran

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