Articles | Volume 23, issue 4
Nonlin. Processes Geophys., 23, 241–256, 2016
https://doi.org/10.5194/npg-23-241-2016
Nonlin. Processes Geophys., 23, 241–256, 2016
https://doi.org/10.5194/npg-23-241-2016

Research article 09 Aug 2016

Research article | 09 Aug 2016

Foreshocks and short-term hazard assessment of large earthquakes using complex networks: the case of the 2009 L'Aquila earthquake

Eleni Daskalaki1,2, Konstantinos Spiliotis2, Constantinos Siettos2, Georgios Minadakis1, and Gerassimos A. Papadopoulos1 Eleni Daskalaki et al.
  • 1Institute of Geodynamics, National Observatory of Athens, Athens, 11810, Greece
  • 2School of Applied Mathematics and Physical Sciences, National Technical University of Athens, 15780, Politechnioupoli, Zografos, Athens

Abstract. The monitoring of statistical network properties could be useful for the short-term hazard assessment of the occurrence of mainshocks in the presence of foreshocks. Using successive connections between events acquired from the earthquake catalog of the Istituto Nazionale di Geofisica e Vulcanologia (INGV) for the case of the L'Aquila (Italy) mainshock (Mw = 6.3) of 6 April 2009, we provide evidence that network measures, both global (average clustering coefficient, small-world index) and local (betweenness centrality) ones, could potentially be exploited for forecasting purposes both in time and space. Our results reveal statistically significant increases in the topological measures and a nucleation of the betweenness centrality around the location of the epicenter about 2 months before the mainshock. The results of the analysis are robust even when considering either large or off-centered the main event space windows.

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Short summary
The monitoring of statistical network properties could be useful for short-term hazard assessment of the occurrence of mainshocks in the presence of foreshocks. Using successive connections between events acquired from the earthquake catalog of INGV for the case of the L’Aquila (Italy) mainshock (Mw = 6.3) of 6 April 2009, we provide evidence that network measures, both global (average clustering coefficient, small-world index) and local (betweenness centrality) ones, could potentially be used.