Forecasting strong subsequent events in the Italian territory: a national and regional application for NESTOREv1.0

P. Brondi (), S. Gentili and R. Di Giovambattista
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P. Brondi: National Institute of Oceanography and Applied Geophysics - OGS
S. Gentili: National Institute of Oceanography and Applied Geophysics - OGS
R. Di Giovambattista: Istituto Nazionale Di Geofisica E Vulcanologia

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 3, No 43, 3499-3531

Abstract: Abstract The Italian territory is one of the most seismically active areas in Europe, where Strong Subsequent Events (SSEs), in combination with the strong mainshock effects, can lead to the collapse of already weakened buildings and to further loss of lives. In the last few years, the machine learning-based algorithm NESTORE (Next STrOng Related Earthquake) was proposed and used to forecast clusters in which the mainshock is followed by a SSE of similar magnitude. Recently, a first new version of a MATLAB package based on this algorithm (NESTOREv1.0) has been developed and the code has been further improved. In our analysis, we considered a nationwide and a regional catalogue for Italy to study the seismicity recorded over the last 40 years in two areas covering most of the Italian territory and northeastern Italy, respectively. For both applications, we obtained statistical information about the clusters in terms of duration, productivity and release of seismic moment. We trained NESTOREv1.0 on the clusters occurring approximately in the first 30 years of catalogues and we evaluated its performance on the last 10 years. The results showed that 1 day after the mainshock occurrence the rate of correct SSE forecasting is larger than 85% in both areas, supporting the application of NESTOREv1.0 in the Italian territory. Furthermore, by training the software on the entire period available for the two catalogues, we obtained good results in terms of near-real-time class forecasting for clusters recorded from 2021 onward.

Keywords: Machine learning; Aftershock forecasting; Statistical seismology; Seismic risk mitigation; Strong subsequent earthquake; Italian seismicity (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-024-06913-6

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