Nonparametric clustering of seismic events

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Abstract

In this paper we propose a clustering technique, based on the maximization of the likelihood function defined from the generalization of a model for seismic activity (ETAS model, (Ogata (1988))), iteratively changing the partitioning of the events. In this context it is useful to apply models requiring the distinction between independent events (i.e. the background seismicity) and strongly correlated ones. This technique develops nonparametric estimation methods of the point process intensity function. To evaluate the goodness of fit of the model, from which the clustering method is implemented, residuals process analysis is used.
Lingua originaleEnglish
Titolo della pubblicazione ospiteData Analysis, Classification and the Forward Search
Pagine397-404
Numero di pagine8
Stato di pubblicazionePublished - 2006

Serie di pubblicazioni

NomeSTUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION

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Chiodi, M., Adelfio, G., Luzio, D., & De Luca, L. (2006). Nonparametric clustering of seismic events. In Data Analysis, Classification and the Forward Search (pagg. 397-404). (STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION).