Turbulencelike Behavior of Seismic Time Series

Department of Physics, Sharif University of Technology, Tehran 11155-9161, Iran.
Physical Review Letters (Impact Factor: 7.51). 02/2009; 102(1):014101. DOI: 10.1103/PhysRevLett.102.014101
Source: arXiv


We report on a stochastic analysis of Earth's vertical velocity time series by using methods originally developed for complex hierarchical systems and, in particular, for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced transition in their probability density function from Gaussian to non-Gaussian. The transition occurs 5-10 hours prior to a moderate or large earthquake, hence representing a new and reliable precursor for detecting such earthquakes.

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Available from: Joachim Peinke, Oct 07, 2015
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    • "All these transitions should ultimately result in operational time and corresponding time scale modulations. For example, plausible testable hypotheses are that cognition may modulate the Markov time τM, i.e., the minimum length interval over which the data can be considered as a Markov process, even when the process itself is not Markovian, or the time scale of the transition from microscopic to macroscopic dynamics (Aquino et al., 2007), or the time scales at which fluctuations start converging to a Gaussian distribution (Mantegna and Stanley, 1994; Manshour et al., 2009). "
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    ABSTRACT: Cognitive neuroscience boils down to describing the ways in which cognitive function results from brain activity. In turn, brain activity shows complex fluctuations, with structure at many spatio-temporal scales. Exactly how cognitive function inherits the physical dimensions of neural activity, though, is highly non trivial, and so are generally the corresponding dimensions of cognitive phenomena. As for any physical phenomenon, when studying cognitive function, the first conceptual step should be that of establishing its dimensions. Here, we provide a systematic presentation of the temporal aspects of task-related brain activity, from the smallest scale of the brain imaging technique's resolution, to the observation time of a given experiment, through the characteristic time scales of the process under study. We first review some standard assumptions on the temporal scales of cognitive function. In spite of their general use, these assumptions hold true to a high degree of approximation for many cognitive (viz. fast perceptual) processes, but have their limitations for other ones (e.g. thinking or reasoning). We define in a quantitative way the temporal quantifiers of cognition at all scales, and illustrate how these quantifiers qualitatively vary as a function of the properties of the cognitive process under study. We propose that each phenomenon should be approached with its own set of theoretical, methodological and analytical tools. In particular, we show that when treating cognitive processes such as thinking or reasoning, complex properties of ongoing brain activity, which can be drastically simplified when considering fast (e.g. perceptual) processes, start playing a major role, and not only characterize the temporal properties of task-related brain activity, but also determine the conditions for proper observation of the phenomena. Finally, some implications on the design of experiments, data analyses, and the choice of recording parameters are discussed.
    Frontiers in Physiology 04/2013; 4:86. DOI:10.3389/fphys.2013.00086 · 3.53 Impact Factor
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    • "Since direct measurement or observation of the detailed dynamics in the friction interface is difficult to achieve without perturbing the overall system, the present study attempts to use techniques known from data analysis of nonlinear dynamical systems to extract properties of the system directly from measured time series data. Although such analysis techniques have been known and applied successfully in a number of scientific and engineering disciplines [19] [20], it seems that their application to friction affected and friction-induced vibration has been rather limited. Interestingly, one of the few exceptions seems to be the recent work by Oberst and Lai [21] [22], which have convincingly shown that brake squeal may also appear in the form of chaotic dynamics, and not only in the form of limit-cycles. "
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    ABSTRACT: Irregular friction brake vibration data have been collected with sampling rates of up to 200 kHz. The measured time series have been subjected to recurrence analysis and phase space reconstruction. The recurrence analysis indicates that irregular vibration states of friction brakes are strongly dominated by intermittency phenomena. Phase space reconstruction suggests that this intermittency is dominated by low-dimensional irregular deterministic dynamics rather than by high-dimensional stochastic processes.
    Journal of Sound and Vibration 07/2012; 331(16):3887–3896. DOI:10.1016/j.jsv.2012.04.003 · 1.81 Impact Factor
    • "Note that, in addition to well logs of hydrocarbon reservoirs, there are many other phenomena whose various properties are simultaneously recorded and exhibit cross correlations. Examples include stochastic signals recorded by detector arrays used in seismic studies and detection of earthquakes (Manshour et al. 2009, 2010), stream flow of rivers, and sun activity represented by sunspot numbers in climate changes (Sadegh Movahed et al. 2006), and asset and investment time series for risk estimation of the economical activities (Podobnik and Stanley 2008; Podobnik et al. 2009). In all such problems, the MF-DXA method may be used with high precision. "
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    Transport in Porous Media 11/2011; 90(2):445-464. DOI:10.1007/s11242-011-9794-x · 1.43 Impact Factor
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