Giorgio, Massimiliano

Earthquake early warning systems (EEWS), based on real-time prediction of ground motion or structural response measures, may play a role in re- ducing vulnerability and/or exposure of buildings and lifelines. Indeed, seismologists have recently developed efficient methods for real-time es- timation of an event’s magnitude and location based on limited informa- tion of the P-waves. Therefore, when an event occurs, estimates of magni- tude and source-to-site distance are available, and the prediction of the structural demand at the site may be performed by Probabilistic Seismic Hazard Analysis (PSHA) and then by Probabilistic Seismic Demand Analysis (PSDA) depending upon EEWS measures. Such an approach contains a higher level of information with respect to traditional seismic risk analysis and may be used for real-time risk management. However, this kind of prediction is performed in very uncertain conditions which may affect the effectiveness of the system and therefore have to be taken into due account. In the present study the performance of the EWWS under development in the Campania region (southern Italy) is assessed by simu- lation. The earthquake localization is formulated in a Voronoi cells ap- proach, while a Bayesian method is used for magnitude estimation. Simu- lation has an empirical basis but requires no recorded signals. Our results, in terms of hazard analysis and false/missed alarm probabilities, lead us to conclude that the PSHA depending upon the EEWS significantly improves seismic risk prediction at the site and is close to what could be produced if magnitude and distance were deterministically known.

The seismological community is currently developing operational earthquake forecasting (OEF) systems that aim to estimate, based on continuous ground motion recording by seismic networks, the rates of events exceeding a certain magnitude threshold in an area of interest and in a short-period of time (days to weeks); i.e., the seismicity. OEF may be possibly used for short-term seismic risk management in regions affected by seismic swarms only if its results may be the input to compute, in a probabilistically sound manner, consequence-based risk metrics. The present paper reports the investigation about feasibility of short-term risk assessment, or operational earthquake loss forecasting (OELF), in Italy. The approach is that of performance-based earthquake engineering, where the loss rates are computed by means of hazard, vulnerability, and exposure. The risk is expressed in terms of individual and regional measures, which are based on short-term macroseismic intensity, or ground motion intensity, hazard. The vulnerability of the built environment relies on damage probability matrices empirically calibrated for Italian structural classes, and exposure data in terms of buildings per vulnerability class and occupants per building typology. All vulnerability and exposure data are at the municipality scale. The procedure set-up, which is virtually independent on the seismological model used, is implemented in an experimental OELF system, which continuously process OEF information to produce weekly nationwide risk maps. This is illustrated by a retrospective application to the 2012 Pollino (southern Italy) seismic sequence, which provides insights on the capabilities of the system and on the impact, on short-term risk assessment, of the methodology currently used for OEF in Italy.

Earthquake Early Warning Systems (EEWS), based on real-time prediction of ground motion or structural response measures, may play a role in reducing vulnerability and/or exposition of buildings and lifelines. In fact, recently seismologists developed efficient methods for rapid estimation of event features by means of limited information of the P-waves. Then, when an event is occurring, probabilistic distributions of magnitude and source-to-site distance are available and the prediction of the ground motion at the site, conditioned to the seismic network measures, may be performed in analogy with the Probabilistic Seismic Hazard Analysis (PSHA). Consequently the structural performance may be obtained by the Probabilistic Seismic Demand Analysis (PSDA), and used for real-time risk management purposes. However, such prediction is performed in very uncertain conditions which have to be taken into proper account to limit false and missed alarms. In the present study, real-time risk analysis for early warning purposes is discussed. The magnitude estimation is performed via the Bayesian approach, while the earthquake localization is based on the Voronoi cells. To test the procedure it was applied, by simulation, to the EEWS under development in the Campanian region (southern Italy). The results lead to the conclusion that the PSHA, conditioned to the EEWS, correctly predicts the hazard at the site and that the false/missed alarm probabilities may be controlled by set up of an appropriate decisional rule and alarm threshold.

Earthquake Early Warning Systems (EEWS), based on real-time prediction of ground motion or structural response measures, may play a role in reducing vulnerability and/or exposition of buildings and lifelines. In fact, recently seismologists developed efficient methods for rapid estimation of event features by means of limited information of the P-waves. Then, when an event is occurring, probabilistic distributions of magnitude and source-to-site distance are available and the prediction of the ground motion at the site, conditioned to the seismic network measures, may be performed in analogy with the Probabilistic Seismic Hazard Analysis (PSHA). Consequently the structural performance may be obtained by the Probabilistic Seismic Demand Analysis (PSDA), and used for real-time risk management purposes. However, such prediction is performed in very uncertain conditions which have to be taken into proper account to limit false and missed alarms. In the present study, real-time risk analysis for early warning purposes is discussed. The magnitude estimation is performed via the Bayesian approach, while the earthquake localization is based on the Voronoi cells. To test the procedure it was applied, by simulation, to the EEWS under development in the Campanian region (southern Italy). The results lead to the conclusion that the PSHA, conditioned to the EEWS, correctly predicts the hazard at the site and that the false/missed alarm probabilities may be controlled by set up of an appropriate decisional rule and alarm threshold.

Classical probabilistic seismic-hazard models (Cornell, 1968), which typically refer to the homogeneous Poisson process for earthquake occurrence, are not able to model explicitly the space-time clustering of earthquakes. Clustering may be particularly evident in time windows of days and weeks (e.g., Kagan and Knopoff, 1987; Ogata, 1988), but it may be still appreciable in the medium term, because the time sequences to large earthquakes may last long (Kagan and Jackson, 1991; Parsons, 2002; Faenza et al., 2003; Marzocchi and Lombardi, 2008). The modeling of such a space–time clustering is an important subject of seismological research (Jordan et al., 2011). In fact, accounting for time–space clustering of earthquakes may provide additional information, not only to seismic-hazard assessment aimed at structural design (e.g., Iervolino et al., 2014; Marzocchi and Taroni, 2014), but also to short-term seismic risk management. The latter issue has been explored by the International Commission for Earthquake Forecasting, established after the L’Aquila earthquake in 2009, which paves the way to the so-called operational earthquake forecasting (OEF). As defined by Jordan et al. (2011), OEF comprises procedures for gathering and disseminating authoritative information about the time dependence of seismic hazards to help communities prepare for potentially destructive earthquakes. Notwithstanding some recent earthquake sequences showing the importance of tracking the time evolution of seismic hazard (e.g., as for the recent Canterbury sequence in New Zealand; Wein and Becker, 2013), currently OEF represents a controversial issue in seismology. Most critics are not focused on debating the scientific credibility of the models used to describe short-term earthquake clustering, but they dispute the usefulness (if not the potential danger) of the information they provide, in particular, the probability of a damaging event in a short time frame. According to OEF models available in the literature, the weekly probability of a large earthquake (e.g., of magnitude six or larger) is above a few percent only after another large event. During a seismic sequence of moderate events (e.g., of maximum magnitude less than five), the weekly probability of a large event may increase also by two to three orders of magnitude with respect to the background value, but almost always this probability remains below a few percent (Jordan et al., 2011). These figures sparked a debate among seismologists about the usefulness and danger of releasing information on the time evolution of short-term earthquake probability. A comprehensive discussion of all these issues can be found in Jordan et al. (2014) and Wang and Rogers (2014). In this article, we focus our attention on one particular aspect of this discussion. In particular, we put forward a different perspective that should replace the common practice of discussing when the probability of a large earthquake can be considered small. As a matter of fact, in a risk-informed decision framework, the variable of interest should be a probabilistically assessed loss (consequence) metric, for instance, the expected loss. A comparison of such a risk metric with some risk thresholds for individuals and/or for communities may help in understanding whether the risk is tolerable or not, and in choosing the optimal risk management decision. A step in this direction has been recently made by Iervolino et al. (2015), which introduces the operational earthquake loss forecasting (OELF) concept. Specifically, OELF translates short-term seismic hazard (OEF) into risk assessment (i.e., the weekly expected loss), using some specific metric, such as the expected number of collapsed buildings, displaced residents, injuries, and fatalities (see also van Stiphout et al., 2010; Zechar et al., 2014). Along these lines, in this article we analyze the evolutions of seismicity forecasts and consequent seismic risk for a seismic sequence that occurred in southern Italy in 2012 and featuring an ML 5.0 largest shock (the Pollino sequence hereafter). This sequence lasted for more than one year, and it was not associated with any destructive earthquake. In particular, the OEF seismicity rates and consequent OELF weekly estimates are evaluated as a function of time for a period spanning 2010–2013 to capture the full evolution of the sequence. Seismic risk metrics are compared with some reference risk values referring to other events from the literature.