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Fractal analysis of GPS time series for early detection of disastrous seismic events

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  • Filatov, Denis M.
  • Lyubushin, Alexey A.

Abstract

A new method of fractal analysis of time series for estimating the chaoticity of behaviour of open stochastic dynamical systems is developed. The method is a modification of the conventional detrended fluctuation analysis (DFA) technique. We start from analysing both methods from the physical point of view and demonstrate the difference between them which results in a higher accuracy of the new method compared to the conventional DFA. Then, applying the developed method to estimate the measure of chaoticity of a real dynamical system — the Earth’s crust, we reveal that the latter exhibits two distinct mechanisms of transition to a critical state: while the first mechanism has already been known due to numerous studies of other dynamical systems, the second one is new and has not previously been described. Using GPS time series, we demonstrate efficiency of the developed method in identification of critical states of the Earth’s crust. Finally we employ the method to solve a practically important task: we show how the developed measure of chaoticity can be used for early detection of disastrous seismic events and provide a detailed discussion of the numerical results, which are shown to be consistent with outcomes of other researches on the topic.

Suggested Citation

  • Filatov, Denis M. & Lyubushin, Alexey A., 2017. "Fractal analysis of GPS time series for early detection of disastrous seismic events," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 718-730.
    • Handle: RePEc:eee:phsmap:v:469:y:2017:i:c:p:718-730
    DOI: 10.1016/j.physa.2016.11.046
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    References listed on IDEAS

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    1. A. Lyubushin, 2014. "Dynamic estimate of seismic danger based on multifractal properties of low-frequency seismic noise," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 70(1), pages 471-483, January.
    2. Carbone, A. & Castelli, G. & Stanley, H.E., 2004. "Time-dependent Hurst exponent in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 267-271.
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    1. Gupta, Sandeep K. & Roy, P.N.S. & Pal, S.K., 2021. "Scale invariance behavior for pre and post-2015 Nepal Gorkha earthquake GPS time series based on fractal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

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