Izvestiya, Physics of the Solid Earth

, Volume 48, Issue 2, pp 155–161

Characteristic variation of local scaling property before Puer M6.4 earthquake in China: The presence of a new pattern of nonlinear behavior of seismicity

Article

Abstract

We observed a new pattern of nonlinear behavior of seismicity. We studied the scaling property of the interevent time series of the seismic sequence for Puer area in China by using the methods of the local scaling property, the generalized dimension spectrum, and the correlation dimension. It is indicated that there are clear characteristic variations of local scaling property and generalized dimension spectrum prior to Puer M6.4 earthquake while there is no characteristic variation of the correlation dimension. This result suggests that the choice of suitable methods is needed for the purpose of getting valuable information about the scale invariance in application of the three methodologies. The major difference between the characteristic variation of local scaling property and the other patterns of nonlinear behavior of seismicity such as the variations of the generalized dimension spectrum and fractal dimension of seismicity before large earthquakes is in the description of scaling property: the generalized dimension spectrum and fractal dimension focus on the global description of the scaling properties, while the local scaling property emphasis the local features. Therefore, the characteristic variation of local scaling property before Puer M6.4 earthquake presents a new pattern of nonlinear behavior of seismicity.

Keywords

local scaling property multifractal patterns of seismicity wavelet transform 

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References

  1. Arneodo, A., Grasseau, G., and Holschneider, M., Wavelet Transform of Multifractals, Physical Review Letters, 1988, vol. 61, pp. 2881–2884.CrossRefGoogle Scholar
  2. Auyang, S.Y., Foundations of Complex-System Theories, Cambridge University Press, 1999.Google Scholar
  3. Carpinteri, A., Lacidogna, G., and Puzzi, S., From Criticality to Final Collapse: Evolution of the “b-value” from 1.5 to 1.0, Chaos, Solitons and Fractals, 2009, vol. 41, pp. 843–853.CrossRefGoogle Scholar
  4. Caruso, F., Vinciguerra, S., Latora, V., Rapisarda, A., and Malone, S., Multifractal Analysis of Mount St. Helens Seismicity as a Tool for Identifying Eruptive Activity, Fractals-Complex Geometry Patterns and Scaling in Nature and Society, 2006, vol. 14, pp. 179–186.CrossRefGoogle Scholar
  5. Chelidze, T. and Matcharashvili, T., Complexity of Seismic Process; Measuring and Applications—A Review, Tectonophysics, 2007, vol. 431, pp. 49–60.CrossRefGoogle Scholar
  6. Chhabra, A. and Jensen, R.V., Direct Determination of the f(a) Singularity Spectrum, Physical Review Letters, 1989, vol. 62, pp. 1327–1330.CrossRefGoogle Scholar
  7. Dimitriu, P.P., Scordilis, E.M., and Karacostas, V.G., Multifractal Analysis of the Arnea, Greece Seismicity with Potential Implications for Earthquake Prediction, Natural Hazards, 2000, vol. 21, pp. 277–295.CrossRefGoogle Scholar
  8. Enescu, B., Itol, K., and Struzik, Z.R., Wavelet-Based Multiscale Resolution Analysis of Real and Simulated Time-Series of Earthquakes, Geophys. J. Int., 2006, vol. 164, pp. 63–74.CrossRefGoogle Scholar
  9. Goltz, C., Fractal and Chaotic Properties of Earthquakes, Springer-Verlag, 1997.Google Scholar
  10. Grassberger, P., Generalized Dimensions of Strange Attractors, Phys. Lett. A, 1983, vol. 97, pp. 227–320.CrossRefGoogle Scholar
  11. Grassberger, P. and Procaccia, J., Dimensions and Entropies of Strange Attractors from a Fluctuating Dynamics Approach, Physica D, 1984, vol. 13, pp. 34–54.CrossRefGoogle Scholar
  12. Hirabayashi, T., Ito, K., and Yoshii, T., Multifractal Analysis of Earthquakes, Pure and Applied Geophysics, 1992, vol. 138, pp. 592–610.CrossRefGoogle Scholar
  13. Jiang, H.K., Zheng, J.C., Wu, Q., Qu, Y.J., and Li, Y.L., Earlier Statistical Features of ETAS Model Parameters and Their Seismological Meanings, Chinese J. Geophys., 2007, vol. 50, pp. 1778–1786.Google Scholar
  14. Kagan, Y.Y., Observational Evidence for Earthquakes as a Nonlinear Dynamic Process, Physica D, 1994, vol. 77, pp. 160–192.CrossRefGoogle Scholar
  15. Keilis-Borok, V.I., The Lithosphere of the Earth as a Nonlinear System with Implications for Earthquake Prediction, Rev. Geophys., 1990, vol. 28, pp. 9–34.CrossRefGoogle Scholar
  16. Kentz, H. and Schreiber, T., Nonlinear Time Series Analysis, Cambridge University press, 1997.Google Scholar
  17. Kiyashchenko, D., Smirnova, N., Troyan, V., Saenger, E., and Vallianatos, F., Seismic Hazard Precursory, Evolution: Fractal and Multifractal Aspects, Physics and Chemistry of the Earth, 2004, vol. 29, pp. 367–378.CrossRefGoogle Scholar
  18. Lee, Y.T., Chen, C.C., Chang, Y.F., and Chiao, L.Y., Precursory Phenomena Associated with Large Avalanches in the Long-Range Connective Sandpile (LRCS) Model, Physica A, 2008, vol. 387, pp. 5263–5270.CrossRefGoogle Scholar
  19. Lei, X.L. and Satoh, T., Indicators of Critical Point, Behavior Prior to Rock Failure Inferred from Pre-Failure Damage, Tectonophysics, 2007, vol. 431, pp. 97–111.CrossRefGoogle Scholar
  20. Liu, F. and Cheng, J.Z., Local Fractal Scale Wavelet Analysis, Journal of Xi’an Jiaotong University, 1999, vol. 33, pp. 14–34.Google Scholar
  21. Martin, M.T., Plastino, A.R., and Plastino, A., Tsallis-Like Information Measures and the Analysis of Complex Signals, Physica A, 2000, vol. 275, pp. 262–271.CrossRefGoogle Scholar
  22. Matcharashvili, T., Chelidze, T., and Javakhishvili, Z., Nonlinear Analysis of Magnitude and Interevent Time Interval Sequences for Earthquakes of the Caucasian Region, Nonlin. Processes in Geophys., 2000, vol. 7, pp. 9–19.CrossRefGoogle Scholar
  23. Murase, K., A Characteristic Change in Fractal Dimension Prior to the 2003 Tokachi-oki Earthquake (MJ = 8.0), Hokkaido, Northern Japan, Earth, Planets and Space, 2004, vol. 56, pp. 401–405.Google Scholar
  24. Nakaya, S., Fractal Properties of Seismicity in Regions Affected by Large, Shallow Earthquakes in Western Japan: Implications for Fault Formation Processes Based on a Binary Fractal Fracture Network Model, J. Geophys. Res., 2005, vol. 110, p. B01310.CrossRefGoogle Scholar
  25. Ogata, Y., Statistical Model for Earthquake Occurrence and Residual Analysis for Point Processes, Journal of the American Statistical Association, 1988, vol. 17, pp. 97–105.Google Scholar
  26. Ogata, Y., Statistical Model for Standard Seismicity and Detection of Anomalies by Residual Analysis for Point Processes, Tectonophysics, 1989, vol. 83, pp. 9–27.Google Scholar
  27. Radulian, M. and Trifu, C.-I., Would It Have Been Possible to Predict the 30 August 1986 Vrancea Earthquake, Bull. Seismol. Soc. Am., 1991, vol. 81, pp. 2498–2503.Google Scholar
  28. Roy, P.N.S. and Nath, S.K., Precursory Correlation Dimensions for Three Great Earthquakes, Current Science, 2007, vol. 93, pp. 1522–1529.Google Scholar
  29. Roy, P.N.S. and Padhi, A., Multifractal Analysis of Earthquakes in the Southeastern Iran-Bam Region, Pure and Applied Geophysics, 2007, vol. 164, pp. 2271–2290.CrossRefGoogle Scholar
  30. Telesca, L., Lapenna, V., and Macchiato, M., Mono- and Multi-Fractal Investigation of Scaling Properties in Temporal Patterns of Seismic Sequences, Chaos, Solitons and Fractals, 2004, vol. 19, pp. 1–15.CrossRefGoogle Scholar
  31. Telesca, L., Lapenna, V., and Macchiato, M., Multifractal Fluctuations in Seismic Interspike Series, Physica A, 2005, vol. 354, pp. 629–640.CrossRefGoogle Scholar
  32. Telesca, L. and Lapenna, V., Measuring Multifractality in Seismic Sequences, Tectonophysics, 2006, vol. 423, pp. 115–123.CrossRefGoogle Scholar
  33. Xu, Z.G., Systems Science, Shanghai Scientific and Technological Education Publishing House, 2000.Google Scholar
  34. Yang, F.S., Application of Wavelet Transform on Engineering Analysis, Science Press, 2003.Google Scholar
  35. Zhu, L.R. and Chen, R., Fractal in Earthquakes, Seismological Press, 2000.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2012

Authors and Affiliations

  1. 1.Research Center for Earthquake PredictionEarthquake Administration of Jiangsu ProvinceNanjingPR China

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