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Characterizing the aperiodic behavior of seismic waves using the scale index analysis

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Abstract

The scale index analysis determines the degree of aperiodicity and chaos in nonlinear dynamical systems. The scale index parameters provide quantitative information on the aperiodicity and chaos in a system in the interval between 0 and 1. Aperiodicity and chaotic behavior of the seismic waves can be studied by this wavelet-based method. In this work, the method is applied firstly to the Duffing oscillator for calibration and secondly to the time series of selected seismic waves to determine and classify their aperiodic and chaotic characteristics. The results indicate that the method is efficient in distinguishing aftershocks from independent earthquakes. The scale index parameters computed from the time series of 23 October 2011, 10:41:21 Van-Tabanlı (ML:6.6) earthquake are in the range of 0.67–0.98, indicating relatively strong aperiodicity hence strong chaotic behavior. On the other hand, the parameters computed from the time series of 09 November 2011, 19:23:33 Van-Edremit (ML:5.6) earthquake are in the range of 0.27–0.62, showing relatively weak aperiodicity hence weak chaotic behavior. The scale index analysis is presented as a unique quantitative approach in seismic wave comparison, complementary to the established seismological analysis techniques.

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Abbreviations

ψ u, s(t):

wavelet function

Wf(u,s):

wavelet transform

S(s):

scalogram function

\( {\overline{S}}^{inner}(s) \) :

normalized inner scalogram function

S :

scalogram scale

i scale :

scalogram scale index

J(s):

time interval

c(s):

initial time boundary

d(s):

final time boundary

γ :

Duffing oscillator magnitude

γ c :

bifurcation value of magnitude

δ :

Duffing oscillator slowing rate

M L :

Richter magnitude

References

  • Akhshani A, Akhavan A, Mobaraki A, Lim SC, Hassan Z (2014) Pseudo random number generator based on quantum chaotic map. Commun Nonlinear Sci Numer Simulat 19:101–111

    Article  Google Scholar 

  • Akilli M (2016) Detecting weak periodic signals in EEG time series. Chin J Phys 54:77–85

    Article  Google Scholar 

  • Akilli M, Yilmaz N (2018) Study of weak periodic signals in the EEG signals and their relationship with postsynaptic potentials. IEEE Trans Neural Syst Rehabil Eng 26(10):1918–1925

    Article  Google Scholar 

  • Akıllı M, Yılmaz N, Akdeniz KG (2019) Study of the q-Gaussian distribution with the scale index and calculating entropy by normalized inner scalogram. Phys Lett A 383(11):1099–1104

    Article  Google Scholar 

  • Akıllı M, Yılmaz N, Akdeniz KG (2021) The ‘wavelet’ entropic index q of non-extensive statistical mechanics and superstatistics. Chaos Soliton Fract Vol 150:111094 ISSN 0960-0779

    Article  Google Scholar 

  • Behnia S, Ziaei J, Ghiassi M, Akhsani A (2015) Nonlinear dynamic approach of heartbeats based on the Grudzinski-Zebrowski’s model. Chin J Phys 53(7)

  • Benitez R, Bolo’s VJ, Ramirez ME (2010) A wavelet-based tool for studying non-periodicity. Comput Math Appl 60:634–641

    Article  Google Scholar 

  • Bogazici University Kandilli Observatory and Earthquake Monitoring Center (KOERI) (2012) Regional Earthquake-Tsunami monitoring Center (RETMC), Earthquake Data Archive, http://www.koeri.boun.edu.tr/ (Viewing date: September 2012)

  • Bolós VJ, Benítez R, Ferrer R, Jammazi R (2017) The windowed scalogram difference: a novel wavelet tool for comparing time series. Appl Math Comput 312:49–65

    Google Scholar 

  • Brittany A, Erickson B, Birnir B, Lavallée D (2011) Periodicity, chaos and localization in a Burridge–Knopoff model of an earthquake with rate-and-state friction. Geophys J Int 187:1,178–1,198

    Google Scholar 

  • Eckmann JP, Kamphorst SO, Ruelle D, Ciliberto S (1986) Liapunov exponents from time series. Phys Rev A 34:4971–4979

    Article  Google Scholar 

  • Erickson B, Birnir B, Lavallée D (2008) A model for aperiodicity in earthquakes. Nonlin Processes Geophys 15:1–12

    Article  Google Scholar 

  • Jhoi B, Takada T, Itoi T (2013) Probabilistic hazard analysis based on 2011 Tohuku erthquake. Data Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures ISBN: 978-1-138-00086-5, 4123–4128.

  • Mallat S (1999) A wavelet tour of signal processing. Academic Press London

    Google Scholar 

  • McCloskey J, Bean CJ, Jacob AWB (1991) Evidence for chaotic behaviour in seismic wave scattering. Geophys Res Lett 18(10):1901–1904

    Article  Google Scholar 

  • Piccirillo V, Baltazar JM, Tusset A, Bernardini D, Rega G (2016) Characterizing the nonlinear behavior of a pseudoelastic oscillator via the wavelet transform. J. Mech Eng Sci 230(1):120–132

    Article  Google Scholar 

  • Rosenstein M, Collins J, De Luca C (1993) A practical method for calculating largest Lyapunov exponents from small data sets. Phys D 65:117–134

    Article  Google Scholar 

  • Rouet-Leduc B, Hulbert C, Lubbers N, Barros K, Colin J, Humphreys CJ, Johnson PA (2017) Machine learning predicts laboratory earthquakes. 44:9276–9282. https://doi.org/10.1002/2017GL074677

  • Srivastava HN, Bhattacharya SN, Sinha RKC (1996) Strange attractor characteristics of earthquakes in Shillong Plateau and adjoining regions. Geophys Res Lett 23(24):3519–3522. https://doi.org/10.1029/96GL03232

    Article  Google Scholar 

  • Strogatz SG (1994) Nonlinear dynamics and chaos: with applications to physics, biology, chemistry and engineering Addison-Wesley, Singapore

  • Wolf A, Swift JB, Swinney HL, Vastano JA (1985) Determining Lyapunov exponents from a time series. Physica D 16:285–317

    Article  Google Scholar 

  • Yang YG, Zhao Q (2016) Novel pseudo-random number generator based on quantum random walks. Sci Rep 6:20362. https://doi.org/10.1038/srep20362

    Article  Google Scholar 

  • Yang D, Yang P, Zhang C (2012) Chaotic characteristic analysis of strong earthquake ground motions. International Journal of Bifurcation and Chaos 22(3)

  • Yılmaz N (2016) Dalgacık Skalogram, Skalogram Ölçek Endeksi ve Güç Spektrumu Yöntemleri ile Bölgesel Depremlerin İncelenmesi. [Analysis of Regional Earthquakes with Wavelet Scalogram, Scalogram Scale Index and Power Spectrum Methods.] PhD thesis, Istanbul University, Istanbul

  • Yilmaz N, Canbaz B, Akilli M, Onem C (2018) Study of the stability of the fermionic instanton solutions by the scale index method. Phys Lett A 382, Issue 32.

  • Yılmaz N, Akıllı M, Özbek M, Zeren T, Akdeniz KG (2020) Application of the nonlinear methods in pneumocardiogram signals. J Biol Phys. 46(2):209–222

    Article  Google Scholar 

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Correspondence to Nazmi Yılmaz.

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Responsible editor: Longjun Dong

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Yılmaz, N. Characterizing the aperiodic behavior of seismic waves using the scale index analysis. Arab J Geosci 14, 2063 (2021). https://doi.org/10.1007/s12517-021-08408-1

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