GNGTS 2023 - Atti del 41° Convegno Nazionale

Session 2.2 GNGTS 2023 observed data and seismological knowledge. A shake map depicts the spatial distribution of peak ground motion usually taking into account the peak ground acceleration (PGA) or the peak-ground velocity (PGV). The distribution of ground-motion shaking intensity is a very useful parameter for the evaluation of the potential of damage (Faenza and Michelini, 2010). Successively, the shake maps enable the possibility to perform a rapid (tens of minutes to hours) disaster assessment of the area affected by the strongest values of ground motion. For instance, automatic visual inspection systems and survivors research relying on remotely operated vehicles can be performed (Cannioto et al., 2017; Zhu et al., 2019). The recorded signals represent also a notable source of information for the evaluation of seismic site effects within the urban area. All the earthquakes recorded, even the low magnitude ones, will be used to identify site effects by direct comparison between the waveforms at the recorded station and at the reference site. These investigations represent the base for seismological studies and also for the seismic hazard assessment. Fig. 2 – General scheme and time progress bar of the potential actions of an urban seismic network consequently to a strong earthquake (response phase). Figure modified from D’Alessandro et al., (2019a). Urban seismic networks: technical aspects The selection of appropriate sites to establish a seismic network usually comes from consideration about the background seismic noise, since it affects the detection capability. Sites should be as quietest as possible in order to maximize the signal-to-noise ratio. Great part of the noise power, especially at high frequency, is related to urban and cultural noise (i.e., related to human activities). Inevitably, urban areas suffer strong levels of seismic noise which makes these noisy areas unsuitable to host networks only conceived and designed to locate earthquakes. These conditions are a relevant limitation for urban seismic networks and represent also the reasons why the sensors employed are usually less performing (especially in terms of self-noise and bandwidth). Often even low-cost, compact devices like MEMS sensors are employed. In particular, this type of sensors has been largely the most employed in recent times for local networks in urban areas (D’Alessandro et al., 2019b).