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

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) [Gentili and Di Giovambattista 2017, 2020, 2022, Gentili et al. 2023, Anyfadi et al. 2023] 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 to improve SSE forecasting for research purpose and on-line sharing (Gentili et al. 2023). In our analysis, we considered both a nationwide and a regional approach for Italy, by studying the seismicity recorded over the last 40 years in two areas covering most of the Italian territory and northeastern Italy, respectively. In both approaches, using two dedicated catalogs, 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 catalogs, we obtained good results in terms of near-real-time class forecasting for clusters recorded from 2021 onward.