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| Titel |
Natural Time Analysis and Complex Networks |
| VerfasserIn |
Nicholas Sarlis, Efthimios Skordas, Mary Lazaridou, Panayiotis Varotsos |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250084449
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| Zusammenfassung |
| Here, we review the analysis of complex time series in a new time domain, termed natural time, introduced by our group [1,2]. This analysis conforms to the desire to reduce uncertainty and extract signal information as much as possible [3]. It enables [4] the distinction between the two origins of self-similarity when analyzing data from complex systems, i.e., whether self-similarity solely results from long-range temporal correlations (the process’s memory only) or solely from the process’s increments infinite variance (heavy tails in their distribution). Natural time analysis captures the dynamical evolution of a complex system and identifies [5] when the system enters a critical stage. Hence, this analysis plays a key role in predicting forthcoming catastrophic events in general. Relevant examples, compiled in a recent monograph [6], have been presented in diverse fields, including Solid State Physics [7], Statistical Physics (for example systems exhibiting self-organized criticality [8]), Cardiology [9,10], Earth Sciences [11] (Geophysics, Seismology), Environmental Sciences (e.g. see Ref. [12]), etc.
Other groups have proposed and developed a network approach to earthquake events with encouraging results. A recent study [13] reveals that this approach is strengthened if we combine it with natural time analysis. In particular, we find [13,14] that the study of the spatial distribution of the variability [15] of the order parameter fluctuations, defined in natural time, provides important information on the dynamical evolution of the system.
1. P. Varotsos, N. Sarlis, and E. Skordas, Practica of Athens Academy, 76, 294-321, 2001.
2. P.A. Varotsos, N.V. Sarlis, and E.S. Skordas, Phys. Rev. E, 66, 011902 , 2002.
3. S. Abe, N.V. Sarlis, E.S. Skordas, H.K. Tanaka and P.A. Varotsos, Phys. Rev. Lett. 94, 170601, 2005.
4. P.A. Varotsos, N.V. Sarlis, E.S. Skordas, H.K. Tanaka and M.S. Lazaridou, Phys. Rev. E, 74, 021123, 2006.
5. P.Varotsos, N. V. Sarlis, E. S. Skordas, S. Uyeda, and M. Kamogawa, Proc Natl Acad Sci USA 108, 11361-11364, 2011.
6. P.A.Varotsos, N.V.Sarlis and E.S.Skordas, NATURAL TIME ANALYSIS: THE NEW VIEW OF TIME. Precursory Seismic Electric Signals, Earthquakes and other Complex Time-Series, Springer-Verlag, Berlin, Heidelberg, 2011.
7. N.V. Sarlis, P.A. Varotsos, and E.S. Skordas, Phys. Rev. B 73, 054504, 2006.
8. N. V. Sarlis, E. S. Skordas, and P. A. Varotsos, EPL 96, 28006, 2011.
9. P.A. Varotsos, N.V. Sarlis, E.S. Skordas, and M.S. Lazaridou, Appl. Phys. Lett. 91, 064106, 2007.
10. N.V. Sarlis, E.S. Skordas and P.A. Varotsos, EuroPhysics Letters EPL, 87, 18003, (2009).
11. P.A. Varotsos, N. V. Sarlis and E. S. Skordas, EPL 96 59002, 2011; 99, 59001 2012; 100 39002, 2012.
12. C.A. Varotsos and C. Tzanis, Atmospheric Environment 47, 428-434, 2012.
13. P. Varotsos, N. Sarlis, E. Skordas and M. Lazaridou, Tectonophysics (DOI 10.1016/j.tecto.2012.12.020).
14. P. Varotsos, N. Sarlis and E. Skordas, EPL to be published.
15. N. V. Sarlis, E. S. Skordas and P. A. Varotsos, EPL 91, 59001, 2010. |
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