Abstract
The stationary Epidemic-Type Aftershock Sequence (ETAS) model is applied to seismicity in Central Italy, in order to study the temporal changes of the corresponding earthquakes time series. However, the residual analysis reveals that some features of the observed seismicity cannot be captured by the stationary ETAS model in its standard formulation. In this case, a decision-tree algorithm is developed to deal with inference problems linked to the estimation of specific time points where stationarity may be potentially broken. Specifically, this algorithm considers the subdivision of the whole time period into two or more subintervals that join in specific time points called change points, where significant time variation in the ETAS parameters is observed. As a result, a three-stage ETAS model with two change points is selected as the best model describing seismicity of the Central Apennines region during the time period 2005–2017, compared to the standard ETAS model. The variation of the estimated ETAS parameters is statistically significant from one stage to another. In particular, the three-stage ETAS estimates of background seismicity rates are found to be increasing from one stage to another over time.
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Acknowledgements
Many thanks are due to the Institute of Statistical Mathematics (ISM) in Tokyo, Japan, for providing the codes destinated to the estimation of the ETAS parameters, freely available via the link https://www.ism.ac.jp/~ogata/Ssg/ssg_softwaresE.html. The financial support from Research Organization of Information and Systems (ROIS) is kindly acknowledged, because of its full coverage of the expenditures of the first author visit to ISM. This research benefitted also from the support of the Centre de Recherche en Astronomie, Astrophysique et Géophysique (CRAAG) in Algiers, Algeria. The authors thank two anonymous reviewers for their constructive comments that significantly improved this article.
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Edited by Prof. Ramón Zúñiga (CO-EDITOR-IN-CHIEF).
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Benali, A., Zhuang, J. & Talbi, A. An updated version of the ETAS model based on multiple change points detection. Acta Geophys. 70, 2013–2031 (2022). https://doi.org/10.1007/s11600-022-00863-y
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DOI: https://doi.org/10.1007/s11600-022-00863-y