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The goal… We provide a spatial-temporal distribution of large earthquakes (M 5.5+) occurred in Italy in the last 4 centuries We provide a NONPARAMETRIC.

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Presentation on theme: "The goal… We provide a spatial-temporal distribution of large earthquakes (M 5.5+) occurred in Italy in the last 4 centuries We provide a NONPARAMETRIC."— Presentation transcript:

1 The goal… We provide a spatial-temporal distribution of large earthquakes (M 5.5+) occurred in Italy in the last 4 centuries We provide a NONPARAMETRIC model of the Hazard Function no assumptions regarding the temporal behaviour of the events Physical/tectonic/geological parameters are taken into account to describe earthquake spatial distribution

2 1.Why an appropriate modelling? - it can provide important information about the physics of earthquakes occurrence process - it allows reliable earthquake forecasting. 2.So far, shared conclusions on statistical distributions could not be reached because of Magnitude Threshold Spatial Domain Very often the distribution of large earthquakes is studied only in the temporal domain selecting small seismic homogeneous areas with not enough data to verify the hypothses. For this reason quite different distributions (e.g. Poisson, Weibull, BPT, Gap, etc…) are commonly used in hazard studies. The state of the art…

3 Parametric seismic catalog (CPTI) plus CSTI M 5.5 Events since 1600 Spatial Grid: Regular Grid The data…

4 Proportional Hazard Model ASSUMPTION : earthquakes generation mechanism is the same for different areas; only the parameters can vary Vector of Coefficients (Max Likelihood Estimation) Vector of Covariates: it contains any spatial/tectonic information characterizing the Inter-Event Times (IETs) or the region where these are sampled Base-Line Hazard Function, bearing information about the physics of the process The method…

5 Results… La variabile riportata nell’asse delle ordinate ha lo stesso andamento della Hazard Function. ClusterPoisson

6 Results… =10 anni

7 Results… Evaluation of the “forecasting ability” of the model ( )


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