Abstract
An adequate characterization of the temporal features of background seismicity, namely after removal of temporally and spatially clustered events (e.g. aftershocks), is a key element in several studies aimed at earthquake forecasting and seismic hazard assessment. In order to investigate the features of background seismicity component, we analyse the rate of background events, that is the rate of main/independent earthquakes as identified by Nearest Neighbour (NN) and Stochastic Declustering (SD) methods. The use of two different declustering methods, which are based on diverse statistical and physical assumptions, allows us to assess whether the identified features depend on the specific definition of background events. In this study, we carry out an in depth analysis of the time changes of background seismicity rate in Northeastern Italy, by means of continuous-time Hidden Markov Models, a stochastic tool that can be used to assess heterogeneity in the temporal pattern of seismicity rates. Specifically, we aim at understanding if the analysed time series can be better described by a homogeneous Poisson model, with unique constant rate, or by a switched Poisson model (i.e. linked to some systematic changes in earthquakes occurrence rates) or whether a basically different model is required. The analysis performed based on Markov modulated Poisson process, and according to Bayesian Information Criterion, shows that a switched Poisson process with three states is the best model describing the background rate identified by SD and NN approaches. The capability of adopted methodology in identifying seismicity rate changes, as well as the sensitivity of the method against the minimum magnitude threshold of analysis, have been verified by applying the method to synthetic catalogues with known properties, namely Poissonian time series with different rates prescribed in specific time intervals. The obtained results suggest that a Poisson model with multiple rates can be used to properly describe background seismicity in Northeastern Italy.
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Acknowledgements
We are grateful to Ilya Zaliapin and Jiancang Zhuang for providing the codes for catalogue declustering. This study was possible thanks to Ph.D. training financial support from University of Sciences and Technology Houari Boumediene (USTHB), Algiers, Algeria. The research also benefited from financial support by Protezione Civile della Regione Autonoma Friuli-Venezia Giulia and Regione Veneto, and by the National grant MIUR, PRIN-2015 program, Prot. 20157PRZC4: "Complex space–time modeling and functional analysis for probabilistic forecast of seismic events".
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Benali, A., Peresan, A., Varini, E. et al. Modelling background seismicity components identified by nearest neighbour and stochastic declustering approaches: the case of Northeastern Italy. Stoch Environ Res Risk Assess 34, 775–791 (2020). https://doi.org/10.1007/s00477-020-01798-w
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DOI: https://doi.org/10.1007/s00477-020-01798-w