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 work, we perform an evaluation of the coverage of the earthquake monitoring network of Taiwan. The capability of a general network is a function of an adequate number of optimally distributed nodes. For this case study, the evaluation is performed with a statistical approach which includes descriptive spatial statistics in combination with point pattern techniques. The spatial distribution of the nodes of the earthquake monitoring network is analyzed in comparison with the distribution of seismicity, completeness magnitude, active seismogenic sources, seismic hazard, and population distribution. All these data can be put in relationship with the objectives of an earthquake monitoring network; therefore, they can be used, in turn, to retrieve information about the consistency of the network itself. In particular, we investigate the “Real-time Seismic Monitoring Network” and the “Strong-Motion Earthquake Observation Network,” each one characterized by its own objectives, and therefore respectively compared with external information related to their purposes such as seismicity, seismogenic sources, seismic hazard, and population distribution. This simple and reliable approach reveals the high quality of the networks established in Taiwan. In general, it is able to provide quantitative information on the coverage of any type of network, identifying possible critical areas and addressing their future development.

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