We propose and validate a new method for the evaluation of seismic hazard. In particular, our aim is to model large earthquakes consistently with the underlying geophysics. Therefore we propose a non-Poisson model, which takes into account occurrence history, improved with some physical constraints. Among the prevalent non-Poisson models, we chose the Markov renewal process, which is expected to be sufficient to capture the main characteristics, maintaining simplicity in analysis. However, due to the introduction of some physical constraint, our process differs significantly from others already presented in literature. A mixture of exponential-Weibull distributions is proposed for the waiting times and their parameters are estimated following the likelihood method. We validated our model, using data of earthquakes of high severity occurred in Turkey during the 20th century. Our results exhibit a good accordance with the real events.

About Earthquake Forecasting by MarkovRenewal Processes

GARAVAGLIA, ELSA;PAVANI, RAFFAELLA
2011

Abstract

We propose and validate a new method for the evaluation of seismic hazard. In particular, our aim is to model large earthquakes consistently with the underlying geophysics. Therefore we propose a non-Poisson model, which takes into account occurrence history, improved with some physical constraints. Among the prevalent non-Poisson models, we chose the Markov renewal process, which is expected to be sufficient to capture the main characteristics, maintaining simplicity in analysis. However, due to the introduction of some physical constraint, our process differs significantly from others already presented in literature. A mixture of exponential-Weibull distributions is proposed for the waiting times and their parameters are estimated following the likelihood method. We validated our model, using data of earthquakes of high severity occurred in Turkey during the 20th century. Our results exhibit a good accordance with the real events.
Earthquake prediction; Semi Markov processes; Renewal processes; Mixture distribution
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/578322
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