Abstract

We apply here a forecasting model to the Italian region for the spatio-temporal distribution of seismicity based on a smoothing Kernel function, Coulomb stress variations, and a rate-and-state friction law. We tested the feasibility of this approach, and analyzed the importance of introducing time-dependency in forecasting future events. The change in seismicity rate as a function of time was estimated by calculating the Coulomb stress change imparted by large earthquakes. We applied our approach to the region of Italy, and used all of the cataloged earthquakes that occurred up to 2006 to generate the reference seismicity rate. For calculation of the time-dependent seismicity rate changes, we estimated the rate-and-state stress transfer imparted by all of the ML≥4.0 earthquakes that occurred during 2007 and 2008. To validate the results, we first compared the reference seismicity rate with the distribution of ML≥1.8 earthquakes since 2007, using both a non-declustered and a declustered catalog. A positive correlation was found, and all of the forecast earthquakes had locations within 82% and 87% of the study area with the highest seismicity rate, respectively. Furthermore, 95% of the forecast earthquakes had locations within 27% and 47% of the study area with the highest seismicity rate, respectively. For the time-dependent seismicity rate changes, the number of events with locations in the regions with a seismicity rate increase was 11% more than in the regions with a seismicity rate decrease.

Highlights

  • Statistical forecasting of earthquakes has proven to be of great importance over time, due to two critical aspects

  • There is some evidence for distance and directional dependencies of dynamic triggering earthquakes [e.g., Parsons 2002, Felzer and Brodsky 2006, Main 2006, among many others], several studies have suggested that the change in the static Coulomb failure stress (DCFS) due to an earthquake has a stronger influence on the distribution of both aftershocks and large earthquakes that might follow [e.g., Harris 1998, Stein 1999, King and Cocco 2000, Freed 2005, Steacy et al 2005]

  • According to the Coulomb model, the stress perturbation due to a large earthquake triggers the aftershocks, which are mainly located in the area of increased DCFS, and inhibits further earthquake occurrence in the stress-shadow areas [e.g. Ma et al 2005]

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Summary

Introduction

Statistical forecasting of earthquakes has proven to be of great importance over time, due to two critical aspects. To quantify the impact of these stress changes on the seismicity rate, previous studies have applied the rateand-state model of Dieterich [1994] to investigate the time-dependency of earthquake occurrence [Toda et al 2005, Catalli et al 2008, Console et al 2010]. In this model, a sudden increase in seismicity rate occurs where the DCFS is positive, due to the earthquake interactions; this is followed by a graduate recovery over time. The evolution of the seismicity rate due to stress changes imparted by earthquakes can be estimated

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