C. Martino

Earthquake early warning system in southern Italy: Methodologies and performance evaluation

We investigate the effect of extended faulting processes and heterogeneous wave propagation on the early warning system capability to predict the peak ground velocity (PGV) from moderate to large earthquakes occurring in the southern Apennines (Italy). Simulated time histories at the early warning network have been used to retrieve early estimates of source parameters and to predict the PGV, following an evolutionary, probabilistic approach. The system performance is measured through the Effective Lead-Time (ELT), i.e., the time interval between the arrival of the first S-wave and the time at which the probability to observe the true PGV value within one standard deviation becomes stationary, and the Probability of Prediction Error (PPE), which provides a measure of PGV prediction error. The regional maps of ELT and PPE show a significant variability around the fault up to large distances, thus indicating that the systems capability to accurately predict the observed peak ground motion strongly depends on distance and azimuth from the fault.

Development and Testing of an Advanced Monitoring Infrastructure (ISNet) for Seismic Early-warning Applications in the Campania Region of Southern Italy

In the framework of an ongoing project financed by the Campania Region, a prototype system for seismic early and post-event warning is being developed and tested, based on a dense, wide dynamic seismic network (ISNet) and under installation in the Apennine belt region.

Exploring the feasibility of a nationwide earthquake early warning system in Italy

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.

An Integrated Regional and On-Site Earthquake Early Warning System for Southern Italy: Concepts, Methodologies and Performances

We present an approach to Earthquake Early Warning for Southern Italy that integrates regional and on-site systems. The regional approach is based on the PRobabilistic and Evolutionary early warning SysTem (PRESTo) software platform. PRESTo processes 3-components acceleration data streams and provides a peak ground-motion prediction at target sites based on earthquake location and magni- tude computed from P-wave analysis at few stations in the source vicinity. On the other hand, the on-site system is based on the real-time measurement of peak dis- placement and dominant period, on a 3s P-wave time-window. These values are compared to thresholds, set for a minimum magnitude 6 and instrumental intensity VII, derived from empirical regression analyses on strong-motion data. Here we present an overview of the system and describe the algorithms implemented in the PRESTo platform. We also show some case-studies and propose a robust methodol- ogy to evaluate the performance of this Early Warning System.