Analysing the Interevent Time Distribution to Identify Seismicity Phases: A Bayesian Nonparametric Approach to the Multiple-Changepoint Problem
| Autor: | Renata Rotondi, Antonio Pievatolo |
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| Rok vydání: | 2000 |
| Předmět: |
Statistics and Probability
Markov chain Generalized gamma distribution Dirichlet distribution Bayesian nonparametric inference Distribution estimation Mixture of Dirichlet processes Reversible jump Markov chain Monte Carlo methods Seismicity phase Dirichlet process symbols.namesake Stochastic simulation symbols Gamma distribution Econometrics Probability distribution Phase-type distribution Statistical physics Statistics Probability and Uncertainty Mathematics |
| Zdroj: | Applied statistics 49 (2000): 543–562. doi:10.1111/1467-9876.00211 info:cnr-pdr/source/autori:A. Pievatolo and R. Rotondi/titolo:Analysing the interevent time distribution to identify seismicity phases: a Bayesian nonparametric approach to the multiple-changepoint problem/doi:10.1111%2F1467-9876.00211/rivista:Applied statistics (Print)/anno:2000/pagina_da:543/pagina_a:562/intervallo_pagine:543–562/volume:49 |
| ISSN: | 1467-9876 0035-9254 |
| DOI: | 10.1111/1467-9876.00211 |
| Popis: | SUMMARY In the study of earthquakes, several aspects of the underlying physical process, such as the time non-stationarity of the process, are not yet well understood, because we lack clear indications about its evolution in time. Taking as our point of departure the theory that the seismic process evolves in phases with different activity patterns, we have attempted to identify these phases through the variations in the interevent time probability distribution within the framework of the multiple-changepoint problem. In a nonparametric Bayesian setting, the distribution under examination has been considered a random realization from a mixture of Dirichlet processes, the parameter of which is proportional to a generalized gamma distribution. In this way we could avoid making precise assumptions about the functional form of the distribution. The number and location in time of the phases are unknown and are estimated at the same time as the interevent time distributions. We have analysed the sequence of main shocks that occurred in Irpinia, a particularly active area in southern Italy: the method consistently identifies changepoints at times when strong stress releases were recorded. The estimation problem can be solved by stochastic simulation methods based on Markov chains, the implementation of which is improved, in this case, by the good analytical properties of the Dirichlet process. |
| Databáze: | OpenAIRE |
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