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

Denis M. Filatov and Alexey A. Lyubushin

Physica A: Statistical Mechanics and its Applications, 2017, vol. 469, issue C, 718-730

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.

Keywords: Fractal analysis of stochastic dynamical systems; Critical states; Precursors of catastrophes; Measure of chaoticity; Disastrous seismic events (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:469:y:2017:i:c:p:718-730

DOI: 10.1016/j.physa.2016.11.046

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