Di Benedetto, Andrea

Urban seismic networks are considered very useful tools for the management of seismic emergencies. In this work, a study of the first urban seismic network in central Italy is presented. The urban seismic network, built using MEMS sensors, was implemented in the urban district of Camerino, one of the cities in central Italy with the greatest seismic vulnerability. The technological choices adopted in developing this system as well as the implemented algorithms are shown in the context of their application to the first seismic event recorded by this innovative monitoring infrastructure. This monitoring network is innovative because it implements a distributed computing and statistical earthquake detection algorithm. As such, it is not based on the traces received by the stations from the central server; rather, each station carries out the necessary checks on the signal in real time, sending brief reports to the server in case of anomalies. This approach attempts to shorten the time between event detection and alert, effectively removing the dead times in the systems currently used in the Italian national network. The only limit for an instant alarm is the latency in the tcp/ip packages used to send the short reports to the server. The presented work shows the infrastructure created; however, there is not enough data to draw conclusions on this new early warning approach in the field, as it is currently in the data collection phase.

In this paper, we propose a novel picking algorithm for the automatic P- and S-waves onset time determination. Our algorithm is based on the variance piecewise constant models of the earthquake waveforms. The effectiveness and robustness of our picking algorithm are tested both on synthetic seismograms and real data. We simulate seismic events with different magnitudes (between 2 and 5) recorded at different epicentral distances (between 10 and 250 km). For the application to real data, we analyse waveforms from the seismic sequence of L’Aquila (Italy), in 2009. The obtained results are compared with those obtained by the application of the classic STA/LTA picking algorithm. Although the two algorithms lead to similar results in the simulated scenarios, the proposed algorithm results in greater flexibility and automation capacity, as shown in the real data analysis. Indeed, our proposed algorithm does not require testing and optimization phases, resulting potentially very useful in earthquakes routine analysis for novel seismic networks or in regions whose earthquakes characteristics are unknown.

In the last years, monitoring systems based on low-cost and miniaturized sensors (MEMS) revealed as a very successful compromise between the availability of data and their quality. Also applications in the field of seismic and structural monitoring have been constantly increasing in term of number and variety of functions. Among these applications, the implementation of systems for earthquake early warning is a cuttingedge topic, mainly for its relevance for the society as millions of peoples in various regions of the world are exposed to high seismic hazard. This paper introduces the optimization of an already established seismic (and structural) monitoring system, that would make it suitable for the implementation of the earthquake early warning. In particular, the sampling code has been improved and a new triggering algorithm able to automatically detect the ground shaking due to the propagation of the seismic waves has been developed. The preliminary results indicate that the system is very flexible and easy to implement, and encourage to perform further developing steps.