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Precursory Analysis of GPS Time Series for Seismic Hazard Assessment

  • Denis M. FilatovEmail author
  • Alexey A. Lyubushin
Article
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Abstract

We discuss the use of fractal analysis of geophysical big data as a precursory method for the estimation of current seismic hazard. We begin by analyzing GPS time series of land surface displacements recorded in various regions of the planet exhibiting different degrees of seismic activity. We show how the quantitative measure of chaoticity based on a modified detrended fluctuation analysis (DFA) method can be used to distinguish critical and noncritical regimes of evolution of the Earth’s crust. Having established the reference values for the measure of chaoticity, we then choose California as the case study region and apply modified DFA to over 1200 time series for determining local areas currently exposed to higher seismic hazard. We comprehensively analyze the results obtained and compare them with those produced by conventional risk assessment methods. We demonstrate consistency of our results and yet discover that the suggested approach allows us to quantify two qualitatively different mechanisms involved in the growth of seismic hazards. We propose a hypothesis which relates these mechanisms to the known mechanisms of earthquake generation that evince a growth of collective behavior in the Earth’s crust on the eve of an earthquake. In order to verify the hypothesis, we analyze the same GPS data by an independent statistical method, canonical coherence analysis (CCA), which permits us to quantify the degree of synchronized behavior of constituents of the Earth’s crust. The CCA method confirms our previously obtained estimates, thus supporting fractal analysis of GPS time series as a suitable precursory method for current seismic hazard assessment.

Keywords

Short- and intermediate-term seismic hazard assessment GPS time series collective behavior detrended fluctuation analysis coherence analysis transition to criticality 

Notes

Acknowledgements

We are grateful to scientists of the Nevada Geodetic Laboratory (University of Nevada, the USA) for preprocessing the raw GPS data and making them publicly available. The software implementing the modified DFA method used for processing the time series is freely available at the website of Sceptica Scientific Ltd (www.sceptica.co.uk/?catastrophes) under a copyleft license. We acknowledge valuable comments of the anonymous referees that have contributed to the improvement of the final version of the paper. The research was partially supported by the Russian Foundation for Basic Research, Grant No. 18-05-00133, project ”Estimation of fluctuations of seismic hazard on the basis of complex analysis of the Earth’s ambient noise.”

References

  1. Aggarwal, Y. P., Sykes, L. R., Armbruster, J., & Sbar, M. L. (1973). Premonitory changes in seismic velocities and prediction of earthquakes. Nature, 241, 101–104.CrossRefGoogle Scholar
  2. Blewitt, G. (1990). An automatic editing algorithm for GPS data. Geophysical Research Letters, 17, 199–202.CrossRefGoogle Scholar
  3. Blewitt, G., Kreemer, C., Hammond, W. C., & Goldfarb, J. M. (2013). Terrestrial reference frame NA12 for crustal deformation studies in North America. Journal of Geodynamics, 72, 11–24.CrossRefGoogle Scholar
  4. Boehm, J., Heinkelmann, R., & Schuh, H. (2013). Short note: A global model of pressure and temperature for geodetic applications. Journal of Geodesy, 81, 679–683.CrossRefGoogle Scholar
  5. Braunmiller, J. (2015). Earthquake mechanisms and tectonics. In M. Beer, I. A. Kougioumtzoglou, E. Patelli, & S.-K. Au (Eds.), Encyclopedia of earthquake engineering. Berlin: Springer.Google Scholar
  6. Brillinger, D. R. (1975). Time series: Data analysis and theory. Holt: Rinehart and Winston Inc.Google Scholar
  7. Cady, J. W. (1975). Magnetic and gravity anomalies in the Great Valley and Western Sierra Nevada metamorphic belt, California. GSA Special Papers, 168, 1–56.Google Scholar
  8. Cox, S. F. (2017). Rupture nucleation and fault slip: Fracture versus friction. Geology, 45, 861–862.CrossRefGoogle Scholar
  9. Crampin, S., Volti, T., & Stefánsson, R. (1999). A successfully stress-forecast earthquake. Geophysical Journal International, 138, F1–F5.CrossRefGoogle Scholar
  10. Feder, J. (1988). Fractals. New York: Plenum Press.CrossRefGoogle Scholar
  11. Field, E. H., and the 2014 Working Group on California Earthquake Probabilities. (2015). UCERF3: A new earthquake forecast for California’s complex fault system. U.S. Geological Survey, Fact Sheet 2015-3009.Google Scholar
  12. Field, E. H., Milner, K. R., and the 2007 Working Group on California Earthquake Probabilities. (2008), Forecasting California’s earthquakes—What can we expect in the next 30 years?. U.S. Geological Survey, Fact Sheet 2008-3027.Google Scholar
  13. Filatov, D. M. (2016). A method for identification of critical states of open stochastic dynamical systems based on the analysis of acceleration. Journal of Statistical Physics, 165, 681–692.CrossRefGoogle Scholar
  14. Filatov, D. M., & Lyubushin, A. A. (2017). Fractal analysis of GPS time series for early detection of disastrous seismic events. Physica A, 469, 718–730.CrossRefGoogle Scholar
  15. Fraser-Smith, A. C., Bemardi, A., McGill, P. R., Ladd, M. E., Heliwell, R. A., & Villard, O. G. (1990). Low-frequency magnetic field measurements near the epicenter of the Ms 7.1 Loma Prieta earthquake. Geophysical Research Letters, 17, 1465–1468.CrossRefGoogle Scholar
  16. Geller, R. J. (1996). Debate on evaluation of the VAN method: Editor’s introduction. Geophysical Research Letters, 23, 1291–1293.CrossRefGoogle Scholar
  17. Geller, R. J. (1997). Earthquake prediction: A critical review. Geophysical Journal International, 131, 425–450.CrossRefGoogle Scholar
  18. Goldberger, A. L., Amaral, L. A. N., Hausdorff, J. M., Ivanov, P. C., Peng, C.-K., & Stanley, H. E. (2002). Fractal dynamics in physiology: Alterations with disease and aging. Proceedings of the National Academy of Sciences of the United States of America, 99, 2466–2472.CrossRefGoogle Scholar
  19. Gratier, J.-P., Richard, J., Renard, F., Mittempergher, S., Doan, M.-L., Di Toro, G., et al. (2011). Aseismic sliding of active faults by pressure solution creep: Evidence from the San Andreas fault observatory at depth. Geology, 39, 1131–1134.CrossRefGoogle Scholar
  20. Gubbins, D. (1990). Seismology and plate tectonics. Cambridge: Cambridge University Press.Google Scholar
  21. Haken, H. (2006). Information and self-organization: A macroscopic approach to complex systems. Berlin: Springer.Google Scholar
  22. Hong, K. T. (2009). Earthquake mechanics. In J. Lastovicka (Ed.), Geophysics and geochemistry (Vol. II, pp. 128–149). Oxford: EOLSS Publishers.Google Scholar
  23. Hotelling, H. (1936). Relations between two sets of variants. Biometrika, 28, 321–377.CrossRefGoogle Scholar
  24. Igarashi, G., Saeki, S., Takahata, N., Sumikawa, K., Tasaka, S., Sasaki, Y., et al. (1995). Ground-water radon anomaly before the Kobe earthquake in Japan. Science, 269, 60–61.CrossRefGoogle Scholar
  25. Johnston, M. J. S., Linde, A. T., & Agnew, D. C. (1994). Continuous borehole strain in the San Andreas fault zone before, during, and after the 28 June 1992, Mw 7.3 Landers, California, earthquake. Bulletin of the Seismological Society of America, 84, 799–805.Google Scholar
  26. Johnston, M. J. S., Linde, A. T., & Gladwin, M. T. (1990). Nearfield high resolution strain measurements prior to the October 18, 1989, Loma Prieta Ms 7.1 earthquake. Geophysical Research Letters, 17, 1777–1780.CrossRefGoogle Scholar
  27. Kanamori, H. (1994). Mechanics of earthquake. Annual Review of Earth and Planetary Sciences, 22, 207–237.CrossRefGoogle Scholar
  28. Kantelhardt, J. W. (2008), Fractal and multifractal time series. arXiv:0804.0747.Google Scholar
  29. Kasahara, K. (1981). Earthquake mechanics. USA: Cambridge University Press.Google Scholar
  30. Kavasseri, R. G., & Nagarajan, R. (2005). A multifractal description of wind speed records. Chaos Solitons Fractals, 24, 165–173.CrossRefGoogle Scholar
  31. Kay, S. M. (1988). Modern spectral estimation: Theory and application. Upper Saddle River: Prentice-Hall Inc.Google Scholar
  32. Klimontovich, Y. L. (1995). Statistical theory of open systems: A unified approach to kinetic description of processes in active systems. Amsterdam: Springer.CrossRefGoogle Scholar
  33. Koscielny-Bunde, E., Kantelhardt, J. W., Braun, P., Bunde, A., & Havlin, S. (2006). Long-term persistence and multifractality of river runoff records: Detrended fluctuation studies. Journal of Hydrology, 322, 120–137.CrossRefGoogle Scholar
  34. Li, Q., Fu, Z., Yuan, N., & Xie, F. (2014). Effects of non-stationarity on the magnitude and sign scaling in the multi-scale vertical velocity increment. Physica A, 410, 9–16.CrossRefGoogle Scholar
  35. Lighthill, J. (1996). A critical review of VAN: Earthquake prediction from seismic electrical signals. Singapore: World Scientific.CrossRefGoogle Scholar
  36. Lipsitz, L. A., & Goldberger, A. L. (1992). Loss of ‘complexity’ and aging: Potential applications of fractals and chaos theory to senescence. The Journal of the American Medical Association, 287, 1806–1809.CrossRefGoogle Scholar
  37. Liu, B., King, M., & Dai, W. (2018). Common mode error in Antarctic GPS coordinate time-series on its effect on bedrock-uplift estimates. Geophysical Journal International, 214, 1652–1664.CrossRefGoogle Scholar
  38. Lognonné, P., & Clévédé, E. (2002). Normal modes of the Earth and planets. In W. H. K. Lee, H. Kanamori, P. C. Jennings, & C. Kisslinger (Eds.), International handbook of earthquake and engineering seismology: Part A (pp. 125–147). London: International Association of Seismology and Physics of the Earth’s Interior (IASPEI).CrossRefGoogle Scholar
  39. Lyubushin, A. A. (1998). Analysis of canonical coherences in the problems of geophysical monitoring. Izvestiya, Physics of the Solid Earth, 34, 52–58.Google Scholar
  40. Lyubushin, A. A. (1999). Analysis of multidimensional geophysical monitoring time series for earthquake prediction. Annals of Geophysics, 42, 927–937.Google Scholar
  41. Lyubushin, A. A. (2010). Multifractal parameters of low-frequency microseisms. In V. de Rubeis, Z. Czechowski, & R. Teisseyre (Eds.), Geoplanet: Earth and planetary sciences. Synchronization and triggering: From fracture to earthquake processes (pp. 253–272). Berlin: Springer.CrossRefGoogle Scholar
  42. Lyubushin, A. A. (2011). Seismic catastrophe in Japan on March 11, 2011: Long-term prediction on the basis of low-frequency microseisms. Izvestiya, Atmospheric and Oceanic Physics, 46, 904–921.CrossRefGoogle Scholar
  43. Lyubushin, A. A. (2012). Prognostic properties of low-frequency seismic noise. Natural Science, 4, 659–666.CrossRefGoogle Scholar
  44. Lyubushin, A. A. (2014a). Dynamic estimate of seismic danger based on multifractal properties of low-frequency seismic noise. Natural Hazards, 70, 471–483.CrossRefGoogle Scholar
  45. Lyubushin, A. A. (2014b). Analysis of coherence in global seismic noise for 1997–2012. Izvestiya, Physics of the Solid Earth, 50, 325–333.CrossRefGoogle Scholar
  46. Lyubushin, A. A., & Sobolev, G. A. (2006). Multifractal measures of synchronization of microseismic oscillations in a minute range of periods. Izvestiya, Physics of the Solid Earth, 42, 734–744.CrossRefGoogle Scholar
  47. McEvilly, T. V., & Johnson, L. R. (1973). Earthquakes of strike-slip in central California: Evidence on the question of dilatancy. Science, 182, 581–584.CrossRefGoogle Scholar
  48. McEvilly, T. V., & Johnson, L. R. (1974). Stability of P and S velocities from central California quarry blasts. Bulletin of the Seismological Society of America, 64, 343–353.Google Scholar
  49. Montagner, J.-P., & Roult, G. (2008). Normal modes of the Earth. Journal of Physics: Conference Series, 118, 012004.Google Scholar
  50. Nevada Geodetic Laboratory, University of Nevada, USA. (2018). http://geodesy.unr.edu/index.php.
  51. Peng, C.-K., Hausdorff, J. M., & Goldberger, A. L. (2000). Fractal mechanisms in neuronal control: Human heartbeat and gait dynamics in health and disease. In J. Walleczek (Ed.), Self-organized biological dynamics and nonlinear control: Toward understanding complexity, chaos and emergent function in living systems (pp. 66–96). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  52. Peng, C.-K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos, 5, 82–87.CrossRefGoogle Scholar
  53. Potirakis, S. M., Hayakawa, M., & Schekotov, A. (2017). Fractal analysis of the ground-recorded ULF magnetic fields prior to the 11 March 2011 Tohoku earthquake (Mw = 9): Discriminating possible earthquake precursors from space-sourced disturbances. Natural Hazards, 85, 59–86.CrossRefGoogle Scholar
  54. Raleigh, C. B., Bennett, G., Craig, H., Hanks, T., Molnar, P., Nur, A., et al. (1977). Prediction of the Haicheng earthquake. Eos Transactions American Geophysical Union, 58, 236–272.CrossRefGoogle Scholar
  55. Rikitake, T. (1976). Earthquake prediction, developments in solid earth geophysics series (Vol. 9). Amsterdam: Elsevier.Google Scholar
  56. Rosen, P., Werner, C., Fielding, E., Hensley, S., Buckley, S., & Vincent, P. (1998). Aseismic creep along the San Andreas Fault northwest of Parkfield, CA measured by radar interferometry. Geophysical Research Letters, 25, 825–828.CrossRefGoogle Scholar
  57. Scholz, C. H. (1997). What ever happened to earthquake prediction? Geotimes, 42, 16–19.Google Scholar
  58. Scholz, C. H. (2002). The mechanics of earthquakes and faulting. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  59. Scholz, C. H., Sykes, L. R., & Aggarwal, Y. P. (1973). Earthquake prediction: A physical basis. Science, 181, 803–810.CrossRefGoogle Scholar
  60. Semenov, A. M. (1969). Variations in the travel-time of transverse and longitudinal waves before violent earthquakes. Izvestiya, Physics of the Solid Earth, 4, 245–248.Google Scholar
  61. Silver, P. G., & Wakita, H. (1996). A search for earthquake precursors. Science, 273, 77–78.CrossRefGoogle Scholar
  62. Sornette, D. (2006). Critical phenomena in natural sciences. Chaos, fractals, selforganization and disorder: Concepts and tools. Berlin: Springer.Google Scholar
  63. Stover, C. W., & Coffman, J. L. (1993). Seismicity of the United States, 1568–1989: U.S. Geological Survey Professional Paper 1527. Washington: United States Government Printing Office.CrossRefGoogle Scholar
  64. Tsunogai, U., & Wakita, H. (1995). Precursory chemical changes in ground water: Kobe earthquake, Japan. Science, 269, 61–63.CrossRefGoogle Scholar
  65. U.S. Geological Survey. (2017a). Are earthquake probabilities or forecasts the same as prediction? https://www.usgs.gov/faqs/are-earthquake-probabilities-or-forecasts-same-prediction.
  66. U.S. Geological Survey (2017b), The San Andreas Fault, https://pubs.usgs.gov/gip/earthq3/where.html.
  67. Varotsos, P. A., Sarlis, N. V., & Skordas, E. S. (2011). Natural time analysis: The new view of time. Precursory seismic electric signals, earthquakes and other complex time series. Berlin: Springer.CrossRefGoogle Scholar
  68. Wakita, H., Nakamura, Y., Notsu, K., Noguchi, M., & Asada, T. (1980). Radon anomaly: A possible precursor of the 1978 Izu-Oshima-Kinkai earthquake. Science, 207, 882–883.CrossRefGoogle Scholar
  69. Whitcomb, J. H., Garmany, J. D., & Anderson, D. L. (1973). Earthquake prediction: Variation of seismic velocities before the San Fernando earthquake. Science, 180, 632–635.CrossRefGoogle Scholar
  70. Williams, S. D. P., Bock, Y., Fang, P., Jamason, P., Nikolaidis, R. M., Prawirodirdjo, L., et al. (2004). Error analysis of continuous GPS position time series. Journal of Geophysical Research, 109, B03412.Google Scholar
  71. Wyss, M. (1975). A search for precursors to the Sitka, 1972, earthquake: Sea level, magnetic field, and p-residuals. In M. Wyss (Ed.), Earthquake prediction and rock mechanics (pp. 297–309). Berlin: Birkhauser Basel.CrossRefGoogle Scholar
  72. Wyss, M. (1991). Evaluation of proposed earthquake precursors. Washington, DC: American Geophysical Union.CrossRefGoogle Scholar
  73. Yamazaki, Y. (1975). Precursory and coseismic resistivity changes. In M. Wyss (Ed.), Earthquake prediction and rock mechanics (pp. 219–227). Berlin: Birkhauser Basel.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sceptica Scientific LtdStockportUK
  2. 2.Institute of Physics of the EarthRussian Academy of SciencesMoscowRussia

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