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Study of the seismic activity in central Ionian Islands via semi-Markov modelling

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Języki publikacji
EN
Abstrakty
EN
The seismicity of the central Ionian Islands (M ≥ 5.2, 1911–2014) is studied via a semi-Markov chain which is investigated in terms of the destination probabilities (occurrence probabilities). The interevent times are considered to follow geometric (in which case the semi-Markov model reduces to a Markov model) or Pareto distributions. The study of the destination probabilities is useful for forecasting purposes because they can provide the more probable earthquake magnitude and occurrence time. Using the first half of the data sample for the estimation procedure and the other half for forecasting purposes it is found that the time windows obtained by the destination probabilities include 72.9% of the observed earthquake occurrence times (for all magnitudes) and 71.4% for the larger (M ≥ 6.0) ones.
Czasopismo
Rocznik
Strony
533--541
Opis fizyczny
Bibliogr. 26 poz.
Twórcy
  • Department of Mathematics, Aristotle University of Thessaloniki, Thessaloníki, Greece; Université de Technologie de Compiègne, Sorbonne Universités, LMAC Laboratoire de Mathématiques Appliquées de Compiègne, Compiègne, France
autor
  • Department of Mathematics, Aristotle University of Thessaloniki, Thessaloníki, Greece
  • Department of Geophysics, Aristotle University of Thessaloniki, Thessaloníki, Greece
Bibliografia
  • 1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723. doi:10.1109/TAC.1974.1100705
  • 2. Alhajj R, Rokne J (2014) Encyclopedia of social network analysis and mining, probability distributions. Springer, New York, pp 1374–1375. doi:10.1007/978-1-4614-6170-8_100157
  • 3. Al-Hajjar J, Blanpain O (1997) Semi-Markovian approach for modelling seismic aftershocks. Eng Struct 19(12):969–976. doi:10.1016/S0141-0296(97)00006-0
  • 4. Altinok Y, Kolcak D (1999) An application of the semi-Markov model for earthquake occurrences in North Anatolia, Turkey. J Balkan Geophys Soc 2(4):90–99
  • 5. Anagnos T, Kiremidjian AS (1984) Stochastic time-predictable model for earthquake occurrences. Bull Seism Soc Am 74(6):2593–2611
  • 6. Anderson TW, Darling DA (1952) Asymptotic theory of certain “goodness of fit” criteria based on stochastic processes. Ann Math Stat 23(2):193–212
  • 7. Barbu VS, Limnios N (2008) Semi-Markov chains and hidden semi-Markov models toward applications. Their use in reliability and DNA analysis. Springer, New York, p 209
  • 8. Belosta CG (2015) Anderson-Darling GoF test. R statistical package. doi:https://cran.r-project.org/web/packages/ADGofTest/index.html
  • 9. Cluff LS, Patwardhan AS, Coppersmith KJ (1980) Estimating the probability of occurrences of surface faulting earthquakes on the Wasatch fault zone, Utah. Bull Seism Soc Am 70(5):1463–1478
  • 10. Howard R (1971) Dynamic probabilistic systems: semi-Markov and decision processes, vol 2. Wiley, New York
  • 11. Kagan Y (2002) Seismic moment distribution revisited: I. Statistical results. Geophys J Int 148(3):520–541. doi:10.1046/j.1365-246x.2002.01594.x
  • 12. Karakostas V, Papadimitriou E (2010) Fault complexity associated with the 14 August 2003 M w 6.2 Lefkada, Greece, aftershock sequence. Acta Geophys 58(5):838–854. doi:10.2478/s11600-010-0009-6
  • 13. Kokinou E, Papadimitriou E, Karakostas V, Kamberis E, Vallianatos F (2006) The Kefalonia Transform Zone (offshore Western Greece) with special emphasis to its prolongation towards the Ionian Abyssal Plain. Marine Geophys Res 27(4):241–252. doi:10.1007/s11001-006-9005-2
  • 14. Lutz KA, Kiremidjian AS (1995) A stochastic model for spatially and temporally dependent earthquakes. Bull Seism Soc Am 85(4):1177–1189
  • 15. Malik HJ (1970) Estimation of the parameters of the Pareto distribution. Metrika 15(1):126–132. doi:10.1007/BF02613565
  • 16. Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18(1):50–60
  • 17. Masala G (2012) Earthquakes occurrences estimation through a parametric semi-Markov approach. J Appl Stat 39(1):81–96. doi:10.1080/02664763.2011.578617
  • 18. Miller J, Adamchik VS (1998) Derivatives of the Hurwitz Zeta function for rational arguments. J Comput Appl Math 100(2):201–206. doi:10.1016/S0377-0427(98)00193-9
  • 19. Papadimitriou E (2002) Mode of strong earthquake recurrence in the Central Ionian Islands (Greece): possible triggering due to Coulomb stress changes generated by the occurrence of previous strong shocks. Bull Seismol Soc Am 92(8):3293–3308. doi:10.1785/0120000290CrossRefGoogle Scholar
  • 20. Patwardhan AS, Kulkarni RB, Tocher D (1980) A semi-Markov model for characterizing recurrence of great earthquakes. Bull Seismol Soc Am 70(1):323–347Google Scholar
  • 21. Pertsinidou CE, Tsaklidis G, Papadimitriou E (2013) Seismic hazard assessment in the northern Aegean Sea (Greece) through discrete semi-Markov modeling. In: Proceedings 13th int. Congr. Geol. Soc. Greece, 5–8 September 2013, vol 3. Chania, Greece, pp 1417–1428Google Scholar
  • 22. Pertsinidou CE, Tsaklidis G, Papadimitriou E (2016) Modeling the seismicity of central Ionian Islands with semi-Markov models. In: Proc. 14th int. Congr. Geol. Soc. Greece, 25–27 May 2016, vol 3, Thessaloniki, Greece, pp 1399–1411. doi:http://www.geosociety.gr/index.php/en/publications-en/2000/255-l-14th-25-5-2016-en
  • 23. Sadeghian R, Jalali-Naini GR, Sadjadi J, Fard NH (2008) Applying semi-Markov models for forecasting the triple dimensions of next earthquake occurrences: with case study in Iran Area. Int J Ind Eng Prod Res 19(4):57–67
  • 24. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464
  • 25. Scordilis EM, Karakaisis GF, Karacostas BG, Panagiotopoulos DG, Comninakis PE, Papazachos BC (1985) Evidence for transform faulting in the Ionian Sea: the cephalonia island earthquake sequence of 1983. Pure Appl Geoph 123(3):388–397. doi:10.1007/BF00880738
  • 26. Votsi I, Limnios N, Tsaklidis G, Papadimitriou E (2012) Estimation of the expected number of earthquake occurrences based on semi-Markov models. Methodol Comput Appl Probab 14(3):685–703. doi:10.1007/s11009-011-9257-4
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-861e35d3-e98f-4f56-be42-00d4ebd4b144
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