Publication Date:
2021-02-26
Description:
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.
Description:
Published
Description:
1674-1678
Description:
5T. Modelli di pericolosità sismica e da maremoto
Description:
6T. Variazioni delle caratteristiche crostali e precursori
Description:
JCR Journal
Keywords:
Earthquake Forecasting
;
Seismic Risk
;
Risk assessment
;
04.06. Seismology
;
05.08. Risk
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
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