When accompanied by appropriate training and preparedness of a population, Earthquake Early Warning Systems (EEWS) are effective and viable tools for the real-time reduction of societal exposure to seismic events in metropolitan areas. The Italian Accelerometric Network, RAN, which consists of about 500 stations installed over all the active seismic zones, as well as many cities and strategic infrastructures in Italy, has the potential to serve as a nationwide early warning system. In this work, we present a feasibility study for a nationwide EEWS in Italy obtained by the integration of the RAN and the software platform PRobabilistic and Evolutionary early warning SysTem (PRESTo). The performance of the RAN-PRESTo EEWS is first assessed by testing it on real strong motion recordings of 40 of the largest earthquakes that have occurred during the last 10 years in Italy. Furthermore, we extend the analysis to regions that did not experience earthquakes by considering a nationwide grid of synthetic sources capable of generating Gutenberg-Richter sequences corresponding to the one adopted by the seismic hazard map of the Italian territory. Our results indicate that the RAN-PRESTo EEWS could theoretically provide for higher seismic hazard areas reliable alert messages within about 5 to 10 s and maximum lead times of about 25 s. In case of large events (M > 6.5), this amount of lead time would be sufficient for taking basic protective measures (e.g., duck and cover, move away from windows or equipment) in tens to hundreds of municipalities affected by large ground shaking.

We propose a P wave based procedure for the rapid estimation of the radiated seismic energy, and a novel relationship for obtaining an energy‐based local magnitude (MLe) measure of the earthquake size. We apply the new procedure to the seismic sequence that struck Central Italy in 2016. Scaling relationships involving seismic moment and radiated energy are discussed for the Mw 6.0 Amatrice, Mw 5.9 Ussita, and Mw 6.5 Norcia earthquakes, including 35 ML > 4 aftershocks. The Mw 6.0 Amatrice earthquake shows the highest apparent stress, and the observed differences among the three main events highlight the dynamic heterogeneity with which large earthquakes can occur in Central Italy. Differences between estimates of MLe and Mw allows identification of events which are characterized by a higher proportion of energy being transferred to seismic waves, providing important real‐time indications of earthquakes shaking potential.

Earthquake Early Warning Systems (EEWS) are potentially effective tools for risk mitigation in active seismic regions. The present study explores the possibility of predicting the macroseismic intensity within EEW timeframes using the squared velocity integral (IV2) measured on the early P wave signals, a proxy for the P wave radiated energy of earthquakes. This study shows that IV2 correlates better than the peak displacement measured on P waves with both the peak ground velocity and the Housner Intensity, with the latter being recognized by engineers as a reliable proxy for damage assessment. Therefore, using the strong motion recordings of the Italian Accelerometric Archive, a novel relationship between the parameter IV2 and the macroseismic intensity (IM) has been derived. The validity of this relationship has been assessed using the strong motion recordings of the Istituto Nazionale di Geofisica e Vulcanologia Strong Motion Data and Osservatorio Sismico delle Strutture databases, as well as, in the case of the MW 6, 29 May 2012 Emilia earthquake (Italy), comparing the predicted intensities with the ones observed after a macroseismic survey. Our results indicate that P wave IV2 can become a key parameter for the design of on‐site EEWS, capable of proving real‐time predictions of the IM at target sites.

We present the results of a feasibility study of an earthquake early warning system (EEWS) for the Campania region (southern Italy) using schools as specific targets. The study considered the seismogenic zones as sources of potential earthquakes for the area, the Italian accelerometric network as the recording network for seismic event occurrence, and the performances of the software platform PRESToPlus for data analysis and processing. We analyze the distribution of lead-times for all possible threatening seismic sources for each municipality in the region under study by extracting the lead-time value corresponding to the 5th, 10th and 25th percentiles of the distributions. We discuss the results for the 5th percentile in order to analyze the worst-case scenario: in the case of a single site, the lead-time is expected to be larger than this value in the 95 % of the cases. Since the population distribution in Campania is uneven and most of the people live nearby the coast, whilst the most destructive earthquakes occur along the Apennine chain, we can conclude that an efficient EEWS can allow most of the schools in the area to undertake some mitigating actions. The testing of the EEWS was carried out in the high school ITIS ‘E. Majorana’, located at Somma Vesuviana, about 80 km from the seismogenic Irpinia\ud region. For this purpose, the Sentinel, an actuator made up of low-cost hardware (i.e., Arduino), was developed in close cooperation with students and teachers of the school to receive alert messages from the PRESToPlus platform and warn the school users in case of a seismic event. The EEWS and the Sentinel were successfully tested during some blind drills performed during normal school activities

The Alto Tiberina normal fault (ATF) in central Italy is a 50‐km‐long crustal structure that dips at a low angle (15–20°). Events on the fault plane are about 10 times less frequent than those located in its shallower syn‐ and antithetic hanging‐wall splays. To enhance ATF catalog and achieve a better understanding of the degree of coupling in the fault system, we apply a template matching technique in the 2010–2014 time window. We augment by a factor 5 the detections and decrease the completeness magnitude to negative values. Contrary to what previously observed on ATF, we highlight intermittent seismic activity and long‐lasting clusters interacting with sequences on the shallower splays. One of these episodes of prolonged seismic activity, detected at the end of 2013 on a 30‐km‐long ATF segment, suggest the ATF active role during an aseismic transient unraveled by geodetic data.

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This article presents the first publicly available version of the NExt STrOng Related Earthquake (NESTORE) software (NESTOREv1.0) designed for the statistical analysis of earthquake clusters. NESTOREv1.0 is a MATLAB (www.mathworks.com/products/matlab, last accessed August 2022) package capable of forecasting strong aftershocks starting from the first hours after the mainshocks. It is based on the NESTORE algorithm, which has already been successfully applied retrospectively to Italian and California seismicity. The code evaluates a set of features and uses a supervised machine learning approach to provide probability estimates for a subsequent large earthquake during a seismic sequence. By analyzing an earthquake catalog, the software identifies clusters and trains the algorithm on them. It then uses the training results to obtain forecasting for a test set of independent data to estimate training performance. After appropriate testing, the software can be used as an Operational Earthquake Forecasting (OEF) method for the next stronger earthquake. For ongoing clusters, it provides near-real-time forecasting of a strong aftershock through a traffic light classification aimed at assessing the level of concern. This article provides information about the NESTOREv1.0 algorithm and a guide to the software, detailing its structure and main functions and showing the application to recent seismic sequences in California. By making the NESTOREv1.0 software available, we hope to extend the impact of the NESTORE algorithm and further advance research on forecasting the strongest earthquakes during seismicity clusters.

Aftershocks of earthquakes can destroy many urban infrastructures and exacerbate the damage already inflicted upon weak structures. Therefore, it is important to have a method to forecast the probability of occurrence of stronger earthquakes in order to mitigate their effects. In this work, we applied the NESTORE machine learning approach to Greek seismicity from 1995 to 2022 to forecast the probability of a strong aftershock. Depending on the magnitude difference between the mainshock and the strongest aftershock, NESTORE classifies clusters into two types, Type A and Type B. Type A clusters are the most dangerous clusters, characterized by a smaller difference. The algorithm requires region-dependent training as input and evaluates performance on an independent test set. In our tests, we obtained the best results 6 h after the mainshock, as we correctly forecasted 92% of clusters corresponding to 100% of Type A clusters and more than 90% of Type B clusters. These results were also obtained thanks to an accurate analysis of cluster detection in a large part of Greece. The successful overall results show that the algorithm can be applied in this area. The approach is particularly attractive for seismic risk mitigation due to the short time required for forecasting.