Advertisement

Izvestiya, Atmospheric and Oceanic Physics

, Volume 54, Issue 10, pp 1460–1469 | Cite as

Cyclic Properties of Seismic Noise and the Problem of Predictability of the Strongest Earthquakes in Japanese Islands

  • A. A. LyubushinEmail author
Article

Abstract

The results of an analysis of the properties of low-frequency seismic noise in the Japanese Islands from early 1997 to March 2018 are presented. The time interval under consideration includes one of the largest seismic disasters of recent times, the Tohoku earthquake of March 11, 2011. The presence of a dense network of seismic observations provides a unique opportunity to investigate how the preparation of a strong earthquake is reflected in changes in the properties of seismic noise in time and space. An analysis of the clustering of the daily multifractal and entropic properties of seismic noise in a 1-year moving time window averaged over all stations of the network made it possible to find a 2.5-year periodicity established since the beginning of 2003. This periodicity correlates with the occurrence of strong earthquakes in Japan. Studying features of the spatial distribution of seismic-noise properties allows us to put forward a hypothesis about the increased danger of the next megaearthquake in Japan in the area of the contact between the northern boundary of the Philippine Sea plate and Honshu Island, in the Nankai deepwater trough area, not far from Tokyo. In the Japanese Islands, in addition to the network of seismic observations, there is a dense network of fixed GPS points for which observations are available from the beginning of March 2015 with a time step of 5 min. The availability of such measurements makes it possible to complement the analysis of seismic-noise properties and to calculate the degree of correlation of GPS data at any point from measurements at neighboring stations. An analysis and processing of measurement results show that the most intense spot of increased correlation of the Earth’s surface tremor, measured using GPS, is located in the Nankai Trough, with the center at the point of 34° N and 138° E.

Keywords:

seismic noise Earth’s tremor multifractals entropy earthquake precursors 

Notes

ACKNOWLEDGMENTS

This work was supported by the Russian Foundation for Basic Research, project no. 18-05-00133.

REFERENCES

  1. 1.
    Aivazyan, S.A., Bukhshtaber, V.M., Enyukov, I.S., and Meshalkin, L.D., Prikladnaya statistika: Klassifikatsiya i snizhenie razmernosti (Applied Statistics: Classification and Dimension Reduction), Moscow: Finansy i statistika, 1989.Google Scholar
  2. 2.
    Duda, R.O. and Hart, P.E., Pattern Classification and Scene Analysis, New York: Wiley and Sons, 1973; Moscow: Mir, 1976.Google Scholar
  3. 3.
    Feder, J., Fractals, New York: Plenum, 1988; Moscow: Mir, 1991.Google Scholar
  4. 4.
    Filatov, D.M. and Lyubushin, A.A., Assessment of seismic hazard of the Japanese islands based on fractal analysis of GPS time series, Izv., Phys. Solid Earth, 2017a, vol. 53, no. 4, pp. 545–555.CrossRefGoogle Scholar
  5. 5.
    Filatov, D.M. and Lyubushin, A.A., Fractal analysis of GPS time series for early detection of disastrous seismic events, Phys. A (Amsterdam, Neth.), 2017b, vol. 469, no. 1, pp. 718–730. http://dx.doi.org/. doi 10.1016/ j.physa.2016.11.046Google Scholar
  6. 6.
    Hardle, W., Applied Nonparametric Regression, Cambridge: Cambridge Univ. Press, 1989; Moscow: Mir, 1993.Google Scholar
  7. 7.
    Huber, P.J., Robust Statistics, New York: Wiley, 1981; Moscow: Mir, 1984.Google Scholar
  8. 8.
    Kantelhardt, J.W., Zschiegner, S.A., Konscienly-Bunde, E., Havlin, S., Bunde, A., and Stanley, H.E., Multifractal detrended fluctuation analysis of nonstationary time series, Phys. A (Amsterdam, Neth.), 2002, vol. 316, pp. 87–114.Google Scholar
  9. 9.
    Ketchen, D.J., Jr. and Shook, C.L., The application of cluster analysis in strategic management research: An analysis and critique, Strategic Manage. J., 1996, vol. 17, no. 6, pp. 441–458.CrossRefGoogle Scholar
  10. 10.
    Lyubushin, A.A., Analiz dannykh sistem geofizicheskogo i ekologicheskogo monitoringa (Analysis of Geophysical and Environmental Monitoring Data), Moscow: Nauka, 2007.Google Scholar
  11. 11.
    Lyubushin, A.A., Microseismic noise in the low frequency range (periods of 1–300 min): Properties and possible prognostic features, Izv., Phys. Solid Earth, 2008, vol. 44, no. 4, pp. 275–290.CrossRefGoogle Scholar
  12. 12.
    Lyubushin, A.A., Synchronization trends and rhythms of multifractal parameters of the field of low-frequency microseisms, Izv., Phys. Solid Earth, 2009, vol. 45, no. 5, pp. 381–394.CrossRefGoogle Scholar
  13. 13.
    Lyubushin, A.A., The statistics of the time segments of low-frequency microseisms: trends and synchronization, Izv., Phys. Solid Earth, 2010, vol. 46, no. 6, pp. 544–553.CrossRefGoogle Scholar
  14. 14.
    Lyubushin, A.A., Cluster analysis of low-frequency microseismic noise, Izv., Phys. Solid Earth, 2011a, vol. 47, no.  6, pp. 488–495.CrossRefGoogle Scholar
  15. 15.
    Lyubushin, A.A., The seismic catastrophe of March 11, 2011, in Japan: Long-term forecast by low-frequency microseisms, Geofiz. Protsessy Biosfera, 2011b, vol. 10, no. 1, pp. 9–35.Google Scholar
  16. 16.
    Lyubushin, A.A., Forecast of the Great Japanese earthquake, Priroda, 2012a, no. 8, pp. 23–33.Google Scholar
  17. 17.
    Lyubushin, A., Prognostic properties of low-frequency seismic noise, Nat. Sci., 2012b, vol. 4, pp. 659–666. doi 10.4236/ns.2012.428087Google Scholar
  18. 18.
    Lyubushin, A.A., Mapping the properties of low-frequency microseisms for seismic hazard assessment, Izv., Phys. Solid Earth, 2013a, vol. 49, no. 1, pp. 9–18.CrossRefGoogle Scholar
  19. 19.
    Lyubushin, A., How soon would the next mega-earthquake occur in Japan?, Nat. Sci., 2013b, vol. 5, no. 8A1, pp. 1–7. doi 10.4236/ns.2013.58A1001Google Scholar
  20. 20.
    Lyubushin, A.A., Analysis of coherence in global seismic noise for 1997–2012, Izv., Phys. Solid Earth, 2014a, vol. 50, no. 3, pp. 325–333.CrossRefGoogle Scholar
  21. 21.
    Lyubushin, A.A., Prognostic properties of random fluctuations in geophysical characteristics, Biosfera, 2014b, no. 4, pp. 319–338.Google Scholar
  22. 22.
    Lyubushin, A.A., Dynamic estimate of seismic danger based on multifractal properties of low-frequency seismic noise, Nat. Hazards, 2014c, vol. 70, no. 1, pp. 471–483. doi 10.1007/s11069-013-0823-7CrossRefGoogle Scholar
  23. 23.
    Lyubushin, A.A., Coherence between the fields of low-frequency seismic noise in Japan and California, Izv., Phys. Solid Earth, 2016, vol. 52, no. 6, pp. 810–820.CrossRefGoogle Scholar
  24. 24.
    Lyubushin, A.A., Long-range coherence between seismic noise properties in Japan and California before and after Tohoku mega-earthquake, Acta Geod. Geophys., 2017, vol. 52, pp. 467–478. doi 10.1007/s40328-016-0181-5CrossRefGoogle Scholar
  25. 25.
    Lyubushin, A.A. and Sobolev, G.A., Multifractal measures of synchronization of microseismic oscillations in a minute range of periods, Izv., Phys. Solid Earth, 2006, vol. 42, no. 9, pp. 734–744.CrossRefGoogle Scholar
  26. 26.
    Lyubushin, A.A., Kopylova, G.N., Kasimova, V.A., and Taranova, L.N., On properties of the field of low-frequency noises recorded on the Kamchatka network of wideband seismic stations, Vestn. Kamchatskoi Reg. Assots. Uchebno-Nauchnyi Tsentr, Nauki Zemle, 2015, vol. 2, no. 26, pp. 20–36.Google Scholar
  27. 27.
    Mallat, S., A Wavelet Tour of Signal Processing, San Diego: Academic, 1998; Moscow: Mir, 2005.Google Scholar
  28. 28.
    Mogi, K., Two grave issues concerning the expected Tokai earthquake, Earth Planets Space, 2004, vol. 56, no. 8, pp. li–lxvi.CrossRefGoogle Scholar
  29. 29.
    Rikitake, T., Probability of a great earthquake to recur in the Tokai district, Japan: Reevaluation based on newly-developed paleoseismology, plate tectonics, tsunami study, micro-seismicity and geodetic measurements, Earth Planets Space, 1999, vol. 51, pp. 147–157.CrossRefGoogle Scholar
  30. 30.
    Sobolev, G.A., Kontseptsiya predskazuemosti zemletryasenii na osnove dinamiki seismichnosti pri triggernom vozdeistvii (The Concept of Earthquake Predictability on the Basis of Seismicity Dynamics in Trigger Influence), Moscow: IFZ RAN, 2011.Google Scholar
  31. 31.
    Sobolev, G.A., Seismicheskii shum (Seismic Noise), Moscow: Nauka i obrazovanie, 2014.Google Scholar
  32. 32.
    Vogel, M.A. and Wong, A.K.C., PFS clustering method, IEEE Trans. Pattern Anal. Machine Intell., 1979, vol. 1, no. 3, pp. 237–245. doi 10.1109/TPAMI.1979.4766919CrossRefGoogle Scholar
  33. 33.
    Zoller, G., Holschneider, M., Hainzl, S., and Zhuang, J., The largest expected earthquake magnitudes in Japan: The statistical perspective, Bull. Seismol. Soc. Am., 2014, vol. 104, no. 2, pp. 769–779. doi https://doi.org/. doi 10.1785/0120130103CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Schmidt Institute of Physics of the Earth, Russian Academy of SciencesMoscowRussia

Personalised recommendations