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

In most of the recent intense earthquakes in Italy, a strong subsequent event (SSE) of comparable or higher magnitude was observed. Its effects, in combination with the strong mainshock, may lead to the collapse of already weakened buildings and to a further increase in damage or even in the number of fatalities, with serious consequences for society. Therefore, the forecasting of an SSE is of strategic importance to reduce the seismic risk during the occurrence of a seismic sequence. To this end, we have recently developed the machine learning-based multi-parameter algorithm NESTORE (Next STrOng Related Earthquake). The first MATLAB version (NESTOREv1.0) was applied to Italian seismicity to forecast clusters where the difference between the magnitude of the mainshock Mm and that of the strongest aftershock is less than or equal to 1. These clusters are called type A by the NESTOREv1.0 software, while the other cases are called type B. NESTOREv1.0 is based on nine seismicity features that measure the number of events with M > Mm-2, their spatial distribution, magnitude, and energy trend over time in increasing time intervals following the occurrence of the mainshock. The software identifies seismic clusters above a threshold for mainshock magnitude Mth, finds appropriate thresholds for features to distinguish A and B cases in a training database, and uses them to provide an estimate of the probability that a cluster is of type A in a test set. For the application of NESTOREv1.0 to Italy, we considered both a national and a regional approach. In the first case, we analysed the seismicity recorded by the INGV network from 1980 to 2021, while in the second case we used the seismic catalogue of the dense OGS network in northeastern Italy for the period 1977-2021. In the nationwide application of NESTOREv1.0, we observed an area between Tuscany and Emilia-Romagna with anomalously high seismic activity concentrated in bursts of short duration. Since this area is almost exclusively populated by type B and therefore not suitable for a specific training procedure, we excluded it from the following analyses. In the remaining national area, we trained NESTOREv1.0 with clusters in the time period 1980-2009 (24 clusters) and tested it in the period 2010-2021 (14 clusters). For the regional case, we considered a rectangular area in northeastern Italy, where we could lower Mth due to the higher local density of seismic stations of the OGS seismic network compared to the mean density of the national network. In this area, 13 clusters from 1977 to 2009 were used as training set, and the performance of NESTOREv1.0 was evaluated using 18 clusters from 2010 to 2021. For both approaches, we obtained good results in terms of the rate of correct forecasting of cluster typology. In the 12 hours following the mainshock, the rate is 86% for the nationwide analysis and 89% for the regional analysis, respectively, which supports the application of NESTOREv1.0 on the Italian territory.

Funded by a grant from the Italian Ministry of Foreign Affairs and International Cooperation