Skip to main content
Log in

Enhancing earthquake signal based on variational mode decomposition and S-G filter

  • Original Article
  • Published:
Journal of Seismology Aims and scope Submit manuscript

Abstract

The precise estimation of associated parameters for microseismic and earthquake signals is a challenging task due to the presence of background noise. Important parameters to analyze earthquake signals such as peak ground acceleration, velocity, displacement, and P, S-wave arrival time are affected by noise. In this study, we propose a seismic data denoising algorithm by combining variational mode decomposition (VMD) and Savitzky-Golay (SG) filter. The method first employs a VMD technique that disintegrates the original signal into band-limited intrinsic mode functions. The modes that are contaminated with high-frequency noise are selected and smoothed by the SG filter. An important advantage of SG filters is their ability to retain the shape of data with high frequency. To observe the effect of noise, PPHASEPICKER is applied to the signal provided by the proposed denoising method. As the fundamental constituent of the earthquake accelerogram is displacement, we performed an experiment to expose the effect of noise and different denoising techniques on the displacement component. The results of synthetic data and real data from the Nepal 2015 earthquake show the enhancement of signal-to-noise ratio while preserving the significant features of the displacement component and onset time arrival accuracy, in comparison with some existing techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Allen R (1978) Automatic earthquake recognition and timing from single traces. B Seismol Soc Am 68(5):1521–1532

    Google Scholar 

  • Alvarez I, Garcia L, Mota S, Cortes G, Benitez C, De la Torre A (2013) An automatic P-phase picking algorithm based on adaptive multiband processing. IEEE Geosci Remote Sens Lett 10(6):1488–1492

    Article  Google Scholar 

  • Ansari A, Noorzad A, Zafarani A, Hessam V (2010) Correction of highly noisy strong motion records using a modified wavelet de-noising method. Soil Dyn Earthq Eng 30(11):1168–1181

    Article  Google Scholar 

  • Baijal S, Singh S, Rani A, Agarwal S (2015) Performance evaluation of S-Golay and MA filter on the basis of white and flicker noise. In: Advances in signal processing and intelligent recognition systems. advances in intelligent systems and computing, vol 425, pp 245–255

  • Banjade TP, Yu S, Ma J (2019) Earthquake accelerogram denoising by wavelet based variational mode decomposition. J Seismol 175(6):1–15

    Google Scholar 

  • Beenamol M, Mohanalin J, Prabavathy S, Jordina T (2016) A novel wavelet seismic denoising method using type II fuzzy. Appl Soft Comput 48:507–521

    Article  Google Scholar 

  • Beenamol M, Prabavathy S, Mohanalin J (2012) Wavelet based seismic signal de-noising using Shannon and Tsallis entropy. Comput Math Appl 64(11):3580–3593

    Article  Google Scholar 

  • Bekara M, Baan M (2009) Random and coherent noise attenuation empirical mode decomposition. Geophysics 74(5):89–98

    Article  Google Scholar 

  • Bonar D, Sacchi M (2010) Complex spectral decomposition via inversion strategies: 80th annual international meeting, SEG, expanded abstracts, 1408–1412

  • Botella F, Rosa-Herranz J, Giner JJ, Molina S, Galiana-Merino JJ (2003) A real-time earthquake detector with prefiltering by wavelets. Comput Geosci 29:911–919

    Article  Google Scholar 

  • Dai W, Selesnick I, Rizoo JR, Rucker J, Hudson T (2017) A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades. J Vis 17(9):1–15

    Article  Google Scholar 

  • Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

    Article  Google Scholar 

  • Galiana-Merino JJ, Herranz J, Sergio M, Giner J, Botella F (2003) De-noising of short-period seismograms by wavelet packet transform. Bull Seismol Soc Am 93(6):2554–2562

    Article  Google Scholar 

  • Galiana-Merino JJ, Rosa-Herranz J, Parolai S (2008) Seismic P-phase picking using a Kurtosis-based criterion in the stationary wavelet domain. IEEE Trans Geosci Remote Sens 46:3815–3826

    Article  Google Scholar 

  • Gibbons SJ, Schweitzer J, Kvaerna T, Roth M (2019) Enhanced detection and estimation of regional S-phases using the 3-component ARCES array. J Seismol 23(2):341–355

    Article  Google Scholar 

  • Gong J, Li Y, Wu N, Li M (2019) Automatic time picking of microseismic data based on shearlet - AIC algorithm. J Seismol 23(2):261–269

    Article  Google Scholar 

  • Guo Y, Kareem A (2016) Generation of artificial earthquake records with a non-stationary Kanai - Tamiji model. Eng Struct 23(7):827–837

    Google Scholar 

  • Hafez AG, Mostafa R, Kohda (2013) Seismic noise study for accurate P wave arrival detection via MODWT. Comput Geosci 54(2013):148–159

    Article  Google Scholar 

  • Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc London 454:903–995

    Article  Google Scholar 

  • Kalkan E (2016) An automatic P-phase arrival time picker. Bull Seismol Soc Am 106 (3):971–986

    Article  Google Scholar 

  • Karamzadel N, Doloei G, Reza A (2013) Automatic earthquake signal onset picking based on the continuous wavelet transform. IEEE Trans Geosci Remote Sens 51(5):2666–2674

    Article  Google Scholar 

  • Li Y, Wang Y, Lin H, Zhong T (2018) First arrival time picking for microseismic data based on DWSW algorithm. J Seismol 22(4):833–840

    Article  Google Scholar 

  • Liu W, Cao S, Wang Z (2017) Application of variational mode decomposition to seismic random noise reduction. J Geophys Eng 14(4):888–899

    Google Scholar 

  • Loh CH, Wu TC, Huang NE (2001) Application of the empirical mode decomposition Hilbert spectrum method to identify near fault ground motion characteristics. B Seismol Soc Am 91 (5):1339–1357

    Article  Google Scholar 

  • Mousavi SM, Langston CA, Horton SP (2016) Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform. Geophysics 81(4):341–355

    Article  Google Scholar 

  • Paao B, Steeghs P (2014) Calibration of a local magnitude relationship for microseismic events using earthquake data. Geophysics 81(2):61–70

    Google Scholar 

  • Press WH, Teukolsky SA (1990) Savitzky-Golay Smoothing filters. Comput Phys 4 (6):669

    Article  Google Scholar 

  • Rodriguez IV, Bonar D, Sacchi M (2011) Microseismic data denoising using a 3C group sparsity constrained time-frequency transform. Geophysics 77(2):21–29

    Article  Google Scholar 

  • Sang YF, Wang D, Wu JC, Zhu QP, Wang L (2009) Entropy-based wavelet de-noising method for time series analysis. Entropy 11(4):1123–1147

    Article  Google Scholar 

  • Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36(8):1627–1639

    Article  Google Scholar 

  • Schafer RW (2011) What is a Savitzky-Golay filter. IEEE Signal Proc Mag 28(4):111–117

    Article  Google Scholar 

  • Shang X, Li X, Weng L (2018) Enhancing seismic P phase arrival picking based on wavelet denoising and kurtosis picker. J Seismol 22(1):21–33

    Article  Google Scholar 

  • Sobolev G, Lyubushin A (2006) Microseismic impulses as earthquake precursors: Izvestiya. Phys Solid Earth 42(9):721–733

    Article  Google Scholar 

  • Wu Z, Huang N (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advin Adapt Data Anal 1(1):1–49

    Article  Google Scholar 

  • Yilmaz O (2001) Seismic data analysis. tulsa, OK, USA: SEG, 2001, 837–876

  • Yu S, Ma J (2017) Complex variational mode decomposition for slop preserving denoising. IEEE Trans Geosci Remote Sens 56(1):586–597

    Article  Google Scholar 

  • Yu S, Ma J, Osher S (2018) Geometric mode decomposition. Inverse Probl Imaging 12(4):831–852

    Article  Google Scholar 

  • 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(5):1904–1912

    Article  Google Scholar 

  • Zhang R, Zhang L (2015) Method for identifying micro-seismic P-arrival by time-frequency nalysis using intrinsic time-scale decomposition. Acta Geophysica 63(2):468–485

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Asst. Prof. Siwei Yu for the constructive suggestions and helpful discussions.

Funding

This study is financially supported by National Science and Technology Major Project (Grant No.:2017ZX05049002-005).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tara P. Banjade.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Banjade, T.P., Liu, J., Li, H. et al. Enhancing earthquake signal based on variational mode decomposition and S-G filter. J Seismol 25, 41–54 (2021). https://doi.org/10.1007/s10950-020-09948-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10950-020-09948-x

Keywords

Navigation