Chaos, Self Organized Criticality, Intermittent Turbulence and Nonextensivity Revealed From Seismogenesis in North Aegean Area

Iliopoulos, A. C.; Pavlos, G. P.; Papadimitriou, Eleftheria; Sfiris, Dimitris S.; Athanasiou, M. A.; Tsoutsouras, Vasileios

doi:10.1142/s0218127412502240

Cited by 23 publications

(13 citation statements)

References 54 publications

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“…In a series of papers [5,[60][61][62] results were presented concerning seismogenesis in different Hellenic regions (land and sea of Greece), applying nonlinear analysis to various earthquake time series, such as magnitude, inter-event times, longitude and latitude time series. For the analysis, the model of the dripping faucet was used as a physical interpretation of the seismic process.…”

Section: Seismogenesismentioning

confidence: 99%

Complex Systems: Phenomenology, Modeling, Analysis

Iliopoulos¹

2016

Int J Appl Exp Math

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In this paper a short overview on complex systems and their basic features, as well as the models and mathematical tools developed for their analysis, is given. This review is formed according to the related experience, activity and scientific interests of the author, namely focused on mainly in nonlinear time series analysis and statistics and their applications on different physical systems. In particular, rough outlines are given concerning the phenomenology of complex systems, e.g. far from equilibrium thermodynamics and Tsallis statistics, power law scaling, multi-fractality, low dimensional chaos, SOC, strange kinetics and anomalous diffusion and turbulent intermittency. In addition, a non-complete list of models, based on equations or agent based, is briefly described such as Kuramoto -Sivashinsky equation, cubic complex Ginzburg-Landau Equation, reaction-diffusion Equation, fractional equations, cellular automata, complex networks and artificial neural networks. A more extended review is provided concerning the nonlinear time series analysis complex systems, describing tools like mutual information, flatness coefficient, structure functions, Tsallis q-triplet, correlation dimension and Lyapunov exponents in the reconstructed phase space, which can provide valuable information for the complex system's dynamics. Finally, applications of nonlinear time series analysis on various physical systems, such as earthquakes, Earth's magnetosphere, solar plasma and solar wind, plastic deformation of materials, epilepsy, economical indices and DNA structure, are provided. Mini ReviewOpen Access Complex SystemsIn general, even though there is no precise definition of complex systems, a complex system can be thought of as a collection of nonlinearly interacting elements summing up as a whole which is characterized by novel large scale effects. These effects arise as emergent properties related with nonlinear-complex behavior, which is the most distinguishing feature of complex systems [1]. A nonexhaustive list of complex systems contains among others (in fact most systems can be considered as complex systems): geophysical processes (earthquakes), biophysics (brain dynamics, DNA), space plasmas (solar wind, solar flares, Earth's magnetosphere), plastic deformation of materials, economy (stock indices), socio-technical systems and many others. The aforementioned complex systems, even though they are completely different in many aspects (e.g. different elements), share common characteristics such as:1. They consist of a large number of interacting elements. This fact allows the description of these systems in two different hierarchical levels, namely microscopic and macroscopic. 2. Their subsystems and their interactions are nonlinear. 3. They exhibit emergent behavior, namely a self-organizing collective behavior, which is not predictable from the knowledge of the element's behavior. 4. These systems are open, namely they interact with their environment. This allows these systems to be driven to metastable state...

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Section: Seismogenesismentioning

confidence: 99%

Complex Systems: Phenomenology, Modeling, Analysis

Iliopoulos¹

2016

Int J Appl Exp Math

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“…More specifically, it has been shown that the seismic process in the temporal and spatial domains may reveal features that are close to so-called low-dimensional dynamical structures, although the features of the behaviour in the energy domain appear close to randomness, i.e. representing high-dimensional dynamical processes (Goltz, 1998;Matcharashvili et al, 2000;Iliopoulos et al, 2012). This has been shown for whole catalogues as well as for their parts and for different time periods.…”

Section: Introductionmentioning

confidence: 97%

“…Moreover, the results of analyses carried out to assess the dynamical features of the seismic process in terms of its separate domains (time, space, and energy) also indicate differences in behaviour (see e.g. Goltz, 1998;Matcharashvili et al, 2000Matcharashvili et al, , 2002Abe and Suzuki, 2004;Chelidze and Matcharashvili, 2007;Iliopoulos et al, 2012). More specifically, it has been shown that the seismic process in the temporal and spatial domains may reveal features that are close to so-called low-dimensional dynamical structures, although the features of the behaviour in the energy domain appear close to randomness, i.e.…”

Section: Introductionmentioning

confidence: 99%

Mahalanobis distance-based recognition of changes in the dynamics of a seismic process

Matcharashvili

Czechowski

Zhukova

2019

Nonlin. Processes Geophys.

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Abstract. In the present work, we aim to analyse the regularity of a seismic process based on its spatial, temporal, and energetic characteristics. Increments of cumulative times, increments of cumulative distances, and increments of cumulative seismic energies are calculated from an earthquake catalogue for southern California from 1975 to 2017. As the method of analysis, we use the multivariate Mahalanobis distance calculation, combined with a surrogate data testing procedure that is often used for the testing of non-linear structures in complex data sets. Before analysing the dynamical features of the seismic process, we tested the used approach for two different 3-D models in which the dynamical features were changed from more regular to more randomised conditions by adding a certain degree of noise. An analysis of the variability in the extent of regularity of the seismic process was carried out for different completeness magnitude thresholds. The results of our analysis show that in about a third of all the 50-data windows the original seismic process was indistinguishable from a random process based on its features of temporal, spatial, and energetic variability. It was shown that prior to the occurrence of strong earthquakes, mostly in periods of generation of relatively small earthquakes, the percentage of windows in which the seismic process is indistinguishable from a random process increases (to 60 %–80 %). During periods of aftershock activity, the process of small earthquake generation became regular in all of the windows considered, and thus was markedly different from the randomised catalogues. In some periods within the catalogue, the seismic process appeared to be closer to randomness, while in other cases it became closer to a regular behaviour. More specifically, in periods of relatively decreased earthquake generation activity (with low energy release), the seismic process appears to be random, while during periods of occurrence of strong events, followed by series of aftershocks, significant deviation from randomness is shown, i.e. the extent of regularity markedly increases. The period for which such deviation from random behaviour lasts depends on the amount of seismic energy released by the strong earthquake.

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“…Moreover, it was shown that in the temporal and spatial domains earthquakes' distribution may reveal some features of a low-dimensional, nonlinear structure, while in the energy domain (magnitude distribution) it is close to a random-like high dimensional process (Goltz, 1998;Matcharashvili et al, 2000). Moreover, according to present views, the extent of regularity of the seismic process should vary in time and space (Goltz, 1998;Matcharashvili et al, 2000Matcharashvili et al, , 2002Abe and Suzuki, 2004;Chelidze and Matcharashvili, 2007;Iliopoulos et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Simple statistics for complex Earthquakes’ time distribution

Matcharashvili¹,

Hatano²,

Chélidzé³

et al. 2018

Preprint

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Abstract. Here we investigated a statistical feature of earthquakes time distribution in southern Californian earthquake catalogue. As a main data analysis tool, we used simple statistical approach based on the calculation of integral deviation times (IDT) from the time distribution of regular markers. The research objective is to define whether the process of earthquakes time distribution approaches to randomness. Effectiveness of the IDT calculation method was tested on the set of simulated color noise data sets with the different extent of regularity. Standard methods of complex data analysis have also been used, 5 such as power spectrum regression, Lempel and Ziv complexity and recurrence quantification analysis as well as multi-scale entropy calculation. After testing the IDT calculation method for simulated model data sets, we have analyzed the variation of the extent of regularity in southern Californian earthquake catalogue. Analysis was carried out for different time periods and at different magnitude thresholds. It was found that the extent of the order in earthquakes time distribution is fluctuating over the catalogue. Particularly, we show that the process of earthquakes' time distribution becomes most random-like in periods of 10 decreased local seismic activity.

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Chaos, Self Organized Criticality, Intermittent Turbulence and Nonextensivity Revealed From Seismogenesis in North Aegean Area

Cited by 23 publications

References 54 publications

Complex Systems: Phenomenology, Modeling, Analysis

Complex Systems: Phenomenology, Modeling, Analysis

Mahalanobis distance-based recognition of changes in the dynamics of a seismic process

Simple statistics for complex Earthquakes’ time distribution

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