NHESSNatural Hazards and Earth System SciencesNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus PublicationsGöttingen, Germany10.5194/nhess-17-1981-2017When probabilistic seismic hazard climbs volcanoes: the Mt. Etna case, Italy – Part 1: Model components for sources parameterizationAzzaroRaffaeleraffaele.azzaro@ingv.ithttps://orcid.org/0000-0001-8294-8687BarberiGraziellahttps://orcid.org/0000-0002-8273-0458D'AmicoSalvatorePaceBrunohttps://orcid.org/0000-0002-9509-5019PeruzzaLaurahttps://orcid.org/0000-0001-7781-5775TuvèTizianaIstituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Catania – Osservatorio Etneo, 95123 Catania, ItalyDiSPUTer, University “G. d'Annunzio” Chieti-Pescara Via dei Vestini, Chieti, ItalyIstituto Nazionale di Oceanografia e di Geofisica Sperimentale – OGS, 34010 Sgonico (TS), ItalyRaffaele Azzaro (raffaele.azzaro@ingv.it)22November201717111981199831March20175April201720September201724September2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://nhess.copernicus.org/articles/17/1981/2017/nhess-17-1981-2017.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/17/1981/2017/nhess-17-1981-2017.pdf
The volcanic region of Mt. Etna (Sicily, Italy) represents a perfect lab for
testing innovative approaches to seismic hazard assessment. This is largely
due to the long record of historical and recent observations of seismic and
tectonic phenomena, the high quality of various geophysical monitoring and particularly the rapid geodynamics clearly demonstrate some
seismotectonic processes. We present here the model components and the
procedures adopted for defining seismic sources to be used in a new
generation of probabilistic seismic hazard assessment (PSHA), the first
results and maps of which are presented in a companion paper, Peruzza et al. (2017).
The sources include, with increasing complexity, seismic zones, individual
faults and gridded point sources that are obtained by integrating geological
field data with long and short earthquake datasets (the historical
macroseismic catalogue, which covers about 3 centuries, and a high-quality
instrumental location database for the last decades). The analysis of the
frequency–magnitude distribution identifies two main fault systems within the
volcanic complex featuring different seismic rates that are controlled
essentially by volcano-tectonic processes. We discuss the variability of the
mean occurrence times of major earthquakes along the main Etnean faults by
using an historical approach and a purely geologic method. We derive a
magnitude–size scaling relationship specifically for this volcanic area,
which has been implemented into a recently developed software tool – FiSH
(Pace et al., 2016) – that we use to calculate the
characteristic magnitudes and the related mean recurrence times expected for
each fault. Results suggest that for the Mt. Etna area, the traditional
assumptions of uniform and Poissonian seismicity can be relaxed; a
time-dependent fault-based modeling, joined with a 3-D imaging of
volcano-tectonic sources depicted by the recent instrumental seismicity, can
therefore be implemented in PSHA maps. They can be relevant for the
retrofitting of the existing building stock and for driving risk reduction
interventions. These analyses do not account for regional M> 6
seismogenic sources which dominate the hazard over long return times (≥ 500 years).
Introduction
Mt. Etna, the largest active volcano in Europe, is commonly known for
striking volcanic phenomena, featuring nearly constant summit activity and
frequent flank eruptions. Less evident but equally impressive are tectonic
phenomena occurring along the eastern and southern slopes of the volcano,
which are crossed by different systems of active faults (Azzaro et al.,
2012a). The most severe effect of this tectonic activity is the intense
seismicity shaking the urbanized areas of the volcano, with obvious
implications arising in terms of seismic hazard.
For this reason one of the goals the DPC-INGV V3 project on the
“multi-disciplinary analysis of the relationships between tectonic
structures and volcanic activity” (Azzaro and De Rosa, 2016a, b) was to
assess seismic hazard in the eastern flank of Etna due to local
volcano-tectonic earthquakes. Taking advantage of the huge amount of
geological, seismological and geodetic data, which derive from field
observations and multi-parametric monitoring, we had the opportunity to test
innovative methodological approaches and computation codes developed for the
whole of Italy in the framework of previous projects (Azzaro et al., 2012b;
Peruzza, 2013), adapting them to consider the features of the
volcano-tectonic seismicity. In practice, this meant analyzing large
seismological and geological datasets to parameterize the seismicity rates
of seismic sources and hence the earthquake occurrence probability, thus
improving the results of previous researches based solely on the use of
macroseismic intensity data (Azzaro et al., 2016, and references therein).
In this study, we present the application of the entire procedure to
characterize seismic sources at Mt. Etna region providing, for the first time
on this volcano, a comprehensive view of the seismotectonic features and an
analytical estimation of seismic hazard input parameters, with related
uncertainties. The modeling deals not only with the complexities of the source
processes in a volcanic environment but also with the very nature of the
forces controlling the seismicity, which may be by definition
non-Poissonian: we handle them with an increasing degree of detail, in the
framework of a logic tree approach. We integrated historical and
instrumental earthquake catalogues to define four seismic zones around the
main fault systems recognized in the area, identifying the seismogenic
layers where most of the seismic energy is released (effective depth) and
estimating seismic rates through the frequency–magnitude distribution (FMD).
We also used a distributed seismicity model to describe background
earthquakes in the crustal volume beneath Mt. Etna by adopting a
high-resolution three-dimensional grid (inter-nodal distance of 2 km). In
addition, we performed the characterization of the sources at the scale of
the individual faults by applying a purely geological approach (Pace et al.,
2016) that considers the geometric–kinematic parameters representing fault
activity (dimensions and slip rate). To this end, we first obtained a
magnitude–size scaling relationship (MSR), specifically for this volcanic region,
and then calculated the seismic rates expressed in terms of mean recurrence
time of the maximum magnitude expected on each fault.
The obtained dataset defines the input parameters used in a full
probabilistic seismic hazard assessment (PSHA) at a local scale of Mt. Etna,
discussed in a companion paper (Peruzza et al., 2017, hereinafter referred as
Part 2). Finally, we remark that the amount of multi-disciplinary data used
here for source parameterization, as well as the consistency of results, in
our opinion represent a unique condition compared to both other volcanic
districts and tectonic areas worldwide. In this sense, we hope that this
work may be regarded as a pilot study for improving methodological
approaches and conceptual procedures in fault-based, time-dependent hazard estimations.
Linking faults to earthquakes at Mt. Etna: a short overview
Mt. Etna volcano is an ideal lab for observing at a small-scale a full range
of faulting processes that are difficult to find taking place together in
other regions. Evidence of active tectonics is impressive and widespread,
particularly in the eastern flank where morphotectonic features (Azzaro et
al., 2012a), recurrent seismicity (examined extensively in Sects. 3 and 4)
and ground deformations (Bonforte et al., 2011; Bruno et al., 2012) provide
a real measure of the intense volcano-tectonic activity. Furthermore, more
than a century of documented history of surface faulting related to
coseismic displacements (Azzaro, 1999) and creeping phenomena (Rasà et
al., 1996) suggests a clear picture of the relationship between faults and
earthquakes; the long list of large and minor events rupturing different
segments of the same faults (Fig. 1a) has led to a detailed mapping of
active faults and characterization of their behavior in both the long and
short term (Azzaro et al., 2013a).
Fault systems in the study area. (a) Patterns of historical
surface faulting along the southeastern flank of Mt. Etna (Timpe tectonic system);
colors represent coseismic ruptures related to different earthquakes (modified
from Azzaro, 1999). (b) Seismotectonic model of Etna (from Azzaro et
al., 2012a). The rift zones, i.e., high frequency of opening of eruptive
fissures, are in beige; the sedimentary and metamorphic basement underlying the volcano is in gray.
All these features have allowed constraining a seismotectonic model (Azzaro,
2004) where faults slip in a strongly heterogeneous mode along strike, with
two end-member rupture mechanisms addressing fault segments ruled by
stick-slip behavior (earthquake-related slip) or by stable-sliding
behavior (aseismic creeping) (Fig. 1b). In this framework, the Timpe system
and the Pernicana fault (PF) are the most important tectonic elements at Mt. Etna,
dissecting the eastern flank between the coast and the volcano-tectonic
structures of the NE Rift and Valle del Bove depression (Azzaro et al.,
2012a, 2013a, and references therein). They are both very active from a
seismotectonic point of view in terms of the number of earthquakes and
maximum magnitudes. Whilst the long-term seismic history of the PF is limited to a few decades – the urbanization of the uphill sector
crossed by the fault dates back to the late 1970s – the Timpe system is
responsible for most of the strongest earthquakes known to have occurred at
Mt. Etna since the early 19th century: from a total of 12 largest
events – here we consider those having epicentral intensities, I0,
larger than VIII on the European Macroseismic Scale (EMS), i.e., producing at least severe damage according
to the EMS (see Grünthal, 1998) – 10 of them
are located here, thus making this densely inhabited zone of Mt. Etna the
most hazardous of the volcano (Azzaro et al., 2016).
Another debated issue in modeling seismic sources of volcanic regions for
seismic hazard applications is the question of whether fault behavior is
strictly speaking controlled by the volcanic activity. It is true that
historical destructive earthquakes in the Timpe area did occur both during
flank eruptions and during periods of volcanic quiescence;
correlation studies of major seismic events and volcanic activity have not
produced univocal results (Gasperini et al., 1990; Nercessian et al., 1991;
Gresta et al., 1994). The inter-event time (IET) statistical analysis
(Sicali et al., 2014) shows that the occurrence of low-magnitude (M< 3)
shallow earthquakes in the central sector of the volcano, beneath the
summit craters, depends mainly on the volcanic activity and produces a
seismicity clustered in space and time. In fact, seismic swarms located here
before the 2001, 2002, 2004 and 2008 eruptions are interpreted as a
consequence of stress field variations induced by the process of magma
rising and dyke emplacement (Bonaccorso et al., 2004; Gambino et al., 2004;
Alparone et al., 2012; Sicali et al., 2015). Conversely, for the flanks of
the volcano, especially the eastern one hosting the seismic sources relevant
to seismic hazard, the IET distribution shows a prevalence of uncorrelated
events, i.e., behavior more similar to a tectonic domain than a volcanic one
(see Traversa and Grasso, 2010; Bell and Kilburn, 2012). The role of a
different, wider stress field acting in the Timpe area – a structurally
homogeneous domain characterized by a general east–west extension (Bousquet
and Lanzafame, 2004) – is also proved by the analyses of long time series of
geodetic and seismic data (Bruno et al., 2012; Solaro et al., 2010; Bozzano
et al., 2013; Palano, 2016, and references therein), highlighting the
influence of large-scale instability processes where the strain is released
by a steady process on decennial timescale (Bonforte et al., 2011). In this
scenario we therefore assume that modeled faults are constantly (on
average) loaded in time as expected in a typical tectonic process. We also
consider the role of eruptive activity controlling the occurrence of low-magnitude earthquakes in the central-summit area of the volcano to be
negligible for seismic hazard purposes, since this is an uninhabited zone
and hence the risk is very low.
Distribution of the historical seismicity in the Etna region from 1600
to 2015 (data from CMTE Working Group, 2017). Major events considered for the
analysis are outlined by a white circle and listed in the enclosed table
(asterisk indicates an instrumental value); fault pattern and abbreviations as
in Fig. 1; C.C. indicates the central craters. Inset map shows the retrospective
test of the time-dependent model based on intertimes and b values of faults:
colored curves indicate the variation in time of the conditional probability
assigned to the faults of the SZ Timpe in the next 5 years. Before the first
event assigned to each fault, the probability is assumed as Poissonian; following
the earthquake, the probability curve collapses and progressively increases
until the next rupture (from Azzaro et al., 2013b).
Historical seismicity: some hints for long-term fault behavior
Information provided from macroseismic data is representative of the
long-term seismicity since the effects of past major earthquakes affecting
the urbanized areas of the volcano are well documented (Azzaro et al., 2000;
Azzaro and Castelli, 2015). The historical earthquake dataset used for the
analysis is the CMTE catalogue (CMTE Working Group, 2017), covering the
period 1600–2013 and also including fore- and aftershocks of low intensity;
overall, nearly 1800 events are listed in the catalogue. The magnitude of
completeness of this macroseismic catalogue has been estimated as Mc= 3.7;
this corresponds to I0 VII on the EMS (i.e., moderate damage),
according to the relationship derived by Azzaro et al. (2011).
For our analysis, we selected the historical earthquakes located along the
Timpe fault system (Fig. 2), limiting our attention to the strongest events
with I0 ranging from VIII to IX–X on the EMS (i.e., from severe damage up to
destruction) and with a moment magnitude Mw from 4.6 to 5.2. It should
be noted that moderate values of magnitude for heavily damaging events are a
feature of seismicity in active volcanic areas such as Mt. Etna (Azzaro et
al., 2011), whereas in tectonic domains crustal earthquakes producing the
same effects are generally associated with M≥ 6 (Rovida et al., 2016).
The main reasons for this behavior are (i) the extremely shallow focal
depths of Etna earthquakes (0–4 km, see Sect. 4.1.1) compared with those
of regional events (typically in the range 10–15 km) and (ii) an anomalously
strong low-frequency (0.1 <f <1 Hz) radiation deviating
from the conventional Brune (1970) spectral scaling, which causes large
ground displacements and long (≈ 20 s) durations of shaking
(Milana et al., 2008). The final dataset therefore covers the time span 1805–2015
and consists of nine earthquakes, the causative faults of which are
clearly recognized through extensive evidence of coseismic surface faulting
(Azzaro, 1999). Earthquakes and associated faults used in the analysis are
indicated in Figs. 1 and 2. Thus, the long-term mean recurrence time of
historical major events in the Timpe area, reconstructed over a period of
210 years by the fault seismic histories (see Azzaro et al., 2013b), is just 23 years.
Comparison with estimations based on historical and instrumental
earthquake datasets; the explanation of the geological–kinematic approach is
given in Sect. 5. Abbreviations: Tmean is the mean recurrence time;
α is the aperiodicity factor; Mmax is the maximum magnitude obtained by
the FiSH code and related standard deviation (σMmax); M0 is the
moment rate; μ is the shear modulus; Mmin is the minimum magnitude of the
instrumental earthquake dataset. Asterisk indicates the value obtained by the
bootstrap analysis.
Supported by the observation that major earthquakes have produced surface
faulting ruptures along strike for the entire or most of the length of their
causative faults (Azzaro, 1999), we assume that seismogenic Timpe faults
behave according to the characteristic earthquake model (sensu Schwartz and
Coppersmith, 1984). The earthquake size beyond which the phenomenon becomes
evident corresponds to events having I0≥ VIII on the EMS, equivalent to
Mw≥ 4.6. These characteristic earthquakes therefore represent the
maximum or quasi-maximum historically observed events: in Sect. 5.3 we
will deal with the problem of maximum potential earthquake on faults by
means of magnitude–size vs. fault dimension relationships.
In previous studies we have calculated the mean recurrence time (Tmean)
of a characteristic earthquake by simple intertimes statistics, given by the
sum of all the IETs of major events divided by the number of
the intertimes (see details in Azzaro et al., 2012b). Since the main goal is
to bring the process of earthquake occurrence back to the scale of the
individual fault, we calculated intertimes of earthquakes occurring on the
same fault (in all, six intertimes), and then we applied statistics to obtain a
Tmean of 71.3 years and an aperiodicity factor α=σ/Tmean= 0.42,
a typical value for semi-periodic processes. Of course,
in this way we assume that all the considered faults are characterized by
the same values of Tmean and α (Table 1). Given that the intertimes
dataset is not robust from a statistical point of view, we also applied a
bootstrap analysis by sampling the initial IET dataset with replacement
1000 times to verify the confidence intervals of the results, similarly to the
procedure adopted for paleoseismic datasets (Parsons, 2008), which typically
are as “poor” as our sample. As a result, Tmean remains stable while
α is 15 % lower than the value reported above.
The inset of Fig. 2 represents the retrospective analysis obtained by Azzaro
et al. (2012b); the probability of having a characteristic earthquake on an
individual fault in the next 5 years, a period chosen as representative of
short-term earthquake rupture forecast in a high seismic rate region like
Etna, is plotted vs. time; the time-invariant probability according to a
Poisson distribution is represented by the horizontal pink line (at about
7 % in 5 years), whilst the waves represent fault time-dependent
probabilities calculated according to a Brownian passage time (BPT) distribution
(Matthews et al., 2002). The renewal process causes a sharp drop of the
conditional probability function at the occurrence time of earthquakes
assigned to each fault; note that all the historical events have occurred
when the time-dependent probability of having an earthquake in the next
5 years is higher than the one derived with stationary assumptions, thus
supporting the choice of time dependency in our analysis. By doing this, we
of course consider that fault behavior inside seismogenic
zone (SZ) Timpe is somehow uniform,
being affected by the same seismotectonic regime (Alparone et al., 2011).
Recent earthquake dataset: from the instrumental catalogue to the characterization of seismic sources
Regarding short-term seismicity, we used data recorded by the seismic
network of eastern Sicily that is operated by the Istituto Nazionale di
Geofisica and Vulcanologia, Osservatorio Etneo, in Catania. Although the
instrumental data at Etna have been collected since the early 1990s, a
revised and complete earthquake catalogue has been compiled from 2000 by
using a one-dimensional VP velocity model (Alparone et al., 2015;
Gruppo Analisi Dati Sismici, 2016). For this study, we considered only the
portion of the catalogue from 2005 to 2015. In this time window, the seismic
release is generally regular in terms of both energy and numbers of events,
not altered by the significant steps typically related with the seismic
swarms accompanying eruptions at Etna, as occurred in 2001 and 2002–2003
(Fig. 3). Moreover, since 2005 the seismic network has undergone a major upgrade,
in both the number of stations and technology, with three-component broadband
seismometers and digital acquisition. This technological development has
allowed the detection of very low energy events (Mw≤ 1), the
calculation of homogeneous and well-calibrated local magnitudes (Tuvè et
al., 2015) and the application of advanced techniques for locating hypocenters
(Mostaccio et al., 2013).
General trend of seismicity at Mt. Etna from 2000 to 2015: the black
line indicates the cumulative seismic moment (calculated according to Kanamori,
1977), and the blue line shows the number of earthquakes. The periods marked in red
indicate the main flank eruptions; in gray, the time span selected for the
analysis. Note that the step in the seismic release at the end of 2009 is related
to a seismic sequence in the NW sector of the volcano at a depth of 24–28 km,
not affecting the characterization of the shallow sources.
(a) Historical and instrumental seismicity used for characterizing
seismic sources at Etna. Areas in light blue indicate the seismic zones: PF
is the Pernicana fault (295 earthquakes); MF-SLF are the Moscarello and S. Leonardello faults
(354 eqs.); STF-SVF are the S. Tecla and S. Venerina faults (313 eqs.); FF is the Fiandaca
fault (69 eqs.); Timpe (919 eqs.). Solid black lines represent the simplified
pattern of active faults. (b) Distributions of seismic strain release
vs. focal depth for the 2005–2015 instrumental earthquake dataset referring to
the entire Etna region. (c) Cross sections of the 2005–2015 instrumental
earthquakes beneath the volcano.
In order to better define seismic clusters or hypocentral alignments, thus
contributing to the seismic source identification needed by the V3 project,
the 2005–2015 earthquake dataset was re-processed (Cocina et al., 2016) by
using a three-dimensional VP velocity model (Alparone et al., 2012) and
the tomoDDPS algorithm (Zhang et al., 2009). Compared to more simple
methods, this code uses a combination of both absolute and differential
arrival time readings between events of an earthquake cluster, so that for
earthquakes with foci lying close to each other, travel time errors due to
incorrect velocity models in the volume outside the cluster are essentially ruled out.
As a result, we obtained a revised dataset consisting of 4286 seismic
events with Mw up to 4.8; the magnitude of completeness of the
catalogue Mc is 1.1. Regarding the magnitude scale, the Mw values
of major recent earthquakes are taken from the literature or MedNet bulletin
(http://mednet.rm.ingv.it/earthquakes.php), whereas we adopted
the ML–Mw relationship calibrated on moment tensor analysis
(Saraò et al., 2016) to convert the ML values reported in the
catalogue. In general, most of shallowest earthquakes occurring at Etna in
the 2005–2015 period are located in the eastern sector of the volcano within
7 km of depth (orange in Fig. 4a), clustering around the tectonic features
of the Timpe and Pernicana fault systems. It should be noted that this
seismicity is strictly related with the continuous fault activity and
volcano-tectonic dynamics as a whole (Patanè et al., 2004; Solaro et
al., 2010). Conversely, seismicity occurring at deeper crustal levels mainly
represents purely tectonic regional dynamics due to the current compressive
regime at the front of the Sicilian Chain-Foreland (Lavecchia et al., 2007;
De Guidi et al., 2015; Scarfì et al., 2016). The most significant
seismogenic volume in the deep crust beneath Etna is the one in the
northwestern sector of the volcano, with focal depths in the range of 22–30 km.
Area seismic sources
The area sources represent the most simplified representation of the fault
systems that are relevant for seismic hazard. Area sources, or SZs, are polygons including one or more faults where the
earthquake occurrence rate is uniformly distributed and seismicity occurs at
a defined (i.e., fixed) level of depth. This conceptual approach has been
used in the past for the Italian seismic hazard map MPS04 (Meletti et al.,
2008; Stucchi et al., 2011) and, more recently, for the European hazard map
in the SHARE project too (Woessner et al., 2015).
Despite the detailed knowledge of the geometries of the active faults at
Etna (Azzaro et al., 2013a), defining a SZ is not an easy task since the
individual tectonic elements considered here are very close to each other,
just 1 km apart in the case of the Timpe fault system (Azzaro et al.,
2012a). The borders of the SZs are then defined as buffer zones around the
fault lines containing only the shallowest events occurring within 7 km
depth (orange in Fig. 4a) of the relocated instrumental earthquake dataset.
This is in agreement with the superficial nature of the volcano-tectonic
structures, not rooted in the crust. In addition, we also grouped adjacent
structures. In this way, we obtained four areal seismic sources – three for
the Timpe system and one for the Pernicana system (blue polygons in Fig. 4a) – respecting
the homogeneity in terms of other seismological and geological features (Mmax,
length and width, kinematics, slip rate; see also De Guidi et al., 2012).
These SZs represent the recent seismotectonic activity of the shallowest
crust (≤ 7 km) at Mt. Etna as well as all the strongest historical
earthquakes (Mw≥ 4.6) associated with faults discussed before.
About 1000 earthquakes were used for the detailed characterization of the
areal sources. For an additional exploration on the epistemic uncertainties
in defining source geometry, we also considered an extended SZ embracing the
whole Timpe system, shown as a red polygon in Fig. 4a. In the following, we
reported some graphs for the whole Timpe area; even if they are not used in
the hazard computation (see Peruzza et al., 2017), we believe they provide
the reader with an insight on the uncertainties associated with the source
geometry when a less detailed characterization mediating nonhomogeneous
behavior inside the zone is used.
Distributions of seismic strain release (top panels) and number of
earthquakes (bottom panels) vs. focal depth for the SZs considered in the model.
Dark blue histograms indicate the number of earthquakes with Mw≥ 3.0.
The effective depth is marked in orange. Abbreviations as in Fig. 4.
Effective depth
The characterization of the area sources includes the estimation of the
effective depth, i.e., the seismogenic layer where most of the seismic energy
is released. To this end, we calculated, by using the events included in
each SZ, the distribution of the number of earthquakes above the
completeness threshold and the related strain release vs. the focal depth,
with steps of 1 km. Results in Fig. 5 indicate that the seismogenic
thickness is mainly confined to the first 5 km of crust, a value in
agreement with the focal depth distribution of overall seismicity in the
Mt. Etna region (Fig. 4b). Note that, due to the cone-shaped topography of the
volcano rising up 3000 m, hypocenters can be located above sea level (depth
in these cases assumes negative values). In more detail, a first seismogenic
layer can be observed at 0–2 km b.s.l. (below sea level) in all SZs, but a
second layer is also evident at 4–5 km b.s.l., defining the bottom of S. Tecla–S. Venerina (STF-SVF) and
Moscarello–S. Leonardello faults (MF-SLF) area sources. It should be noted that major seismicity
(M≥ 3.0 eqs.) occurs within both layers (dark blue in Fig. 5).
A similar pattern also emerges for the Timpe SZ, which includes the
aforementioned individual SZs (except PF), confirming the main contribution
to seismogenesis of the deeper focal depth level. In conclusion, SZs at
Mt. Etna are characterized by shallow effective depths, with PF and Fiandaca fault (FF) in the
range of 0 to 2 km and other sources between 0 and 5 km b.s.l. (marked by orange
stripes in Fig. 5). These intervals are used as reference depths in the
hazard computation (see details in Peruzza et al., 2017).
Seismic rate
Seismic rates have been determined by analyzing the frequency–magnitude
distribution from the instrumental earthquake catalogue by using the ZMap
tools (Wiemer, 2001). The FMD of each SZ is estimated by maximum likelihood
method (Wiemer and Wyss, 2002) using only the shallowest events (those
occurring within a depth of 7 km b.s.l.), so that a and b coefficients of the
Gutenberg–Richter (GR) relationship are representative of the seismic
activity of shallow sources. The magnitude of completeness, Mc, of this
subset of data is 1.3–1.4. The obtained FMDs (red in Fig. 6) indicate that
the Timpe faults (FF, STF-SVF, MF-SLF) have b values varying from 0.84 to 1.13
(Table 2), while PF is characterized by a lower b value (0.64).
Effective depth, b and a coefficients of the GR relationship for
each SZ, obtained from the instrumental earthquake dataset (2005–2015).
PFMF-SLFSTF-SVFFFEffective depth (km)0 to 2.00 to 5.00 to 5.00 to 2.0b value0.64 ± 0.060.91 ± 0.081.13 ± 0.160.84 ± 0.15Annual a value2.082.512.731.72
Frequency magnitude distribution for each SZ. Red dots refer to the
instrumental dataset, blue dots to the macroseismic one; dotted lines indicate
uncertainties concerning the GR relationship (black line). b and a values
are obtained from the instrumental earthquake dataset. Years indicate the actual
time window (Tlast-Tfirst) of the events in each
sub-catalogue of the historical dataset. Data are normalized to 1 year.
Abbreviations as in Fig. 4.
To check whether the FMDs obtained from an instrumental earthquake dataset during
an interseismic period of just 11 years represent the deformation
processes driving the volcano-tectonic activity on the Mt. Etna's flanks, and
thus are adequate to describe the long-term seismogenic behavior, we calculated
FMDs from the historical macroseismic catalogue (blue symbols in Fig. 6).
The historical catalogue covers a time span of ca. 150 years for all the SZs
except for PF, whose anthropization (and thus the seismic history) is
limited to the last decades at most. Since the time extension of the
instrumental and historical sub-catalogues is different, all the FMDs are
represented after a normalization to 1 year. The visual comparison of the
observed rates shows a satisfying match between macroseismic and
instrumental data; there are no jumps or huge variations in slope, as often
happens when dealing with such analyses, for example due to nonuniform
magnitude assessment. For the Timpe sources (treated as a group, or
separated in main fault systems in FF, STF-SVF, MF-SLF) the macroseismic
FMDs are within the uncertainties of the instrumental ones, starting
approximately above Mw= 3.5. Above this point, historical data represent
the GR relationships for the high magnitudes, obviously not represented
during an interseismic phase; conversely, the macroseismic FMDs deviate from
the GR fitting at low magnitudes, thus representing the incompleteness of
historical records for small earthquakes, a fact that is widely known.
Regarding the PF, the minor fit of instrumental and macroseismic FMDs is
certainly due to the incompleteness of the macroseismic catalogue (short
seismic history and events “lost” because the area is largely uninhabited).
Finally, we calculated α from GR according to Zöller et al. (2008) (see Table 1).
In conclusion, since we believe that the FMDs from instrumental and
historical macroseismic catalogues match fairly well, we accept the
simplification of adopting the 2005–2015 instrumental seismicity rates as
proxies for the long-term seismogenic behavior of area sources. Therefore
a and b values are calculated from the instrumental seismicity detected by
high-quality monitoring during an interseismic period (i.e., in which no
seismic swarm due to eruptions or volcanic activity has significantly
affected our SZs) and will be used for characterizing the seismicity rates
and extrapolating the GR relationships beyond the maximum value observed in these 11 years.
The maximum magnitude has to be fixed on independent criteria that
will be historical and/or geological, as described in the following.
(a, b) Histograms showing the frequency distribution of b and
a values. (c) Plot of a and b values obtained for the grid nodes;
discarded values are in gray.
Distribution of the b values beneath Etna calculated from the
instrumental earthquake catalogue (2005–2015): the horizontal sections show
the grids at different depths.
Distributed seismicity
An alternative gridded seismicity approach has been used to depict 3-D point
sources in a crustal volume beneath Mt. Etna. After several sensitivity
tests, we calculated the a and b values of the GR relationship as follows: we
created a three-dimensional grid with an inter-nodal distance of 2 km and
applied a constant search radius of 3 km to sample the 2005–2015 instrumental
earthquake dataset; grid nodes with less than 20 earthquakes
were discarded. The maximum-likelihood method according to Wiemer and Wyss (2002)
was applied for GR interpolation of events above the Mc threshold (1.3);
a values have been normalized according to the volume
represented. In this way, we obtained a grid consisting of 422 nodes;
however, since the obtained sample of a and b values features scattering, we
filtered the dataset by removing the outliers and considered only the values
between the 25th and 75th percentiles (Fig. 7a and b). As a result,
the number of grid nodes used to characterize distributed seismicity is 359 (Fig. 7c).
We considered only the spatial variation of the b value since the number of
earthquakes in the grid nodes is not generally sufficient to be split into
different time windows. Figure 8 shows the variability of the b values at
different depths beneath the Etna region. Variations are noteworthy in the
first 7 km of the crust, with low b values (≤ 0.8) characterizing the
northern sector of the volcano around PF at very shallow levels of -2/-1 km
and higher b values (≥ 1.2) in the central sector of Etna at a depth of
4 km. Note that in the eastern sector, including the SZ Timpe at depths ranging
from 2 to 6 km b.s.l., the b value pattern varies widely both in value (0.7–1.2)
and in space (patches extending a few kilometers). Finally, a relatively
minor variation of b values (0.9–1.1) is evident at intermediate crustal
levels in the range of 10–16 km, while at depths higher than 20 km low
b values (≤ 0.9) prevail again.
This overall picture shows analogies with the pattern found by Murru et al. (1999,
2007) on a temporally different earthquake dataset (1999-2005),
highlighting two areas characterized by higher b values than other
surrounding areas: (i) beneath the central craters and (ii) in the eastern
flank, at a depth range of 5–7 km. Although the b values cannot be compared
as absolute numbers because they were calculated from two different
magnitude scales – ML in this study, Md in the Murru et al. (2007)
paper – the aforementioned spatial variations remain constant during time
though the datasets cover contiguous time windows. The spatial distribution
of the statistical parameters obtained from the IET analysis (Sicali et al.,
2014) also displays similar lateral variations, indicating that the
characteristics of earthquake occurrence in the central sector are very
different from PF or SZ Timpe, the latter being more similar to the IET
distribution observed in purely tectonic settings.
In conclusion, even if we cannot rule out that transient properties of the
state of stress may influence the b value in some cells, we believe that the
above comparisons – as well as the overall good match between short-term
instrumental and historical catalogue seismicity rates (respectively red and
blue dots in Fig. 6) – are sufficient evidence that seismicity rates deduced
from a few years of instrumental seismicity during an interseismic period
are representative of the longer-term seismicity rates. They can thus be
considered to represent the distributed seismicity in the source model.
Pattern of individual sources used in the geological model and related
geometric–kinematic parameters: red boxes in the left frame represent the
projection at the surface of the fault planes, while lines indicate the vertical
planes. Note that lengths refer to the seismogenic fault segments only, whereas
the ones governed by prevailingly creeping behavior are not considered.
Individual sources: seismic rates from geometric–kinematic fault parameters
In the previous chapters, seismic rates assigned to faults and area sources
have been defined by historical macroseismic and instrumental earthquake
data. Taking advantage of the huge amount of geological field data and
active tectonics evidence, we also performed a fault source modeling. This
is based on a purely geological approach by converting the
geometric–kinematic parameters representing fault activity into a budget of
seismic moment potentially released by the structure through a computational
scheme that also accounts for a MSR.
For each fault, we then obtain the most probable value of expected
characteristic magnitude (Mchar) with the associated standard
deviation σ, the corresponding mean recurrence time (Tmean) and the
aperiodicity factor α, which are the basic ingredients to compute
earthquake occurrence probabilities, both under a Poissonian assumption and in a time-dependent perspective.
Method and input data
The analysis has been carried out using the software FiSH, a
MATLAB® routine developed to quantify the seismic activity of
a fault from its geometric–kinematic parameters (Pace et al., 2016). The
adopted approach is an evolution of the one by Peruzza et al. (2010) based
on the criterion of “segment seismic moment conservation” (Field et al.,
1999). It takes into account the formal propagation of uncertainties in
magnitude and slip rate and uses directly the 3-D fault geometry (length,
dip angle, thickness of the seismogenic layer) and slip rate of a
seismogenic structure. If a fault has a list of events associated with, the
mean values (magnitude, recurrence time) and their variability derive
directly from historical or paleoseismological observations. However, there
are very few cases of effective repetition of major earthquakes on the same
fault segment in Italy, mostly along the Apennines in Central Italy (Galli
et al., 2010; Cinti et al., 2011; Moro et al., 2013; Peruzza et al., 2011).
At Mt. Etna, some 10 major earthquakes (Mw= 4.3–5.2) occurred repeatedly
along the fault segments of the Timpe and Pernicana systems (Azzaro et al., 2012b).
The FiSH code uses different empirical and analytical relationships available in
the literature between fault geometry and the characteristics of the
expected earthquake in order to quantify several values of Mmax and
associated Tmean. Taking uncertainties of magnitude and slip rate into
account, the software provides budgeting of the seismic moment rate.
Finally, it uses the selected values to calculate the hazard rates, for a
given exposure time, according to a Poissonian distribution or, in a
time-dependent perspective that also considers the time elapsed since the
last event, using some other widely used probability density function. For
this study, the BPT (Matthews et al., 2002) is
adopted to represent time dependency.
Regarding our input data, the geometry, slip rate and kinematics of the
fault segments are constrained by detailed geological/geomorphological field
investigations (Azzaro et al., 2012a; D'Amato et al., 2017) and geodetic
data, the latter providing information on the vertical extension of faults
as well as short-term slip rates (Azzaro et al., 2013a). The 3-D model of the
individual sources considered in our application is shown in Fig. 9,
together with the related geometric–kinematic parameters.
(a) Plot of earthquake magnitude vs. rupture length for the Etna
region (this study); (b) comparison with the magnitude–size scaling
relationships for the Taupo volcanic zone (Villamor et al., 2001) and other
relationships worldwide (Wells and Coppersmith, 1994; Mason, 1996; Wesnousky,
2008). Abbreviations: L is fault length; N is normal kinematics; RA is rupture area;
RLD is rupture length at depth; SRL is surface rupture length.
MSR for volcano-tectonic events
The characterization of an earthquake scaling relationship, which is
suitable for a volcanic domain such as Etna, is a key step for modeling the
rupture extent of these low- to moderate-magnitude events. Whereas empirical
relationships derived for tectonic domains are widely available in the
literature for both worldwide applications and regional contexts, those
calibrated for active volcanic areas are relatively few. Among these,
Stirling et al. (2013) mentioned those developed for thin-crust
volcano-tectonic contexts (Mason, 1996; Wesnousky, 2008) and the one
specifically derived for the Taupo volcanic zone in New Zealand (Villamor et al., 2001).
At Mt. Etna, major shallow volcano-tectonic earthquakes produce surface
faulting with end-to-end rupture lengths up to 6.5 km and vertical offsets
up to 90 cm. Systematic historical investigations and recent observations
have enabled compiling an earthquake rupture catalogue that reports some
50 coseismic faulting events (Azzaro, 1999, 2004). In this analysis, we
use the most reliable observations of this dataset (43 data points) to
derive a magnitude–scaling relationship that is specific for the Etna
region, calibrated in the range Mw= 2.8–5.2 (Fig. 10a).
In Fig. 10b, Mt. Etna MSR is plotted together with the ones available for
tectonic and volcanic domains. Considering the approximations due to the use
of different dimensional measurements – magnitude scales, rupture length
vs. rupture area – and the limitation in extrapolating the fitting outside
the original magnitude ranges, the comparison is quite satisfactory. We note
a strong analogy with respect to the trend of the relationship suggested by
Villamor et al. (2001) for the Taupo volcanic zone, although the Etna one is
scaled by ca. 1 order of magnitude, whereas discrepancies are substantial
for thin-crust volcano-tectonic context relationships proposed by Mason (1996)
and Wesnousky (2008). Also the set of worldwide relationships by
Wells and Coppersmith (1994) based on rupture length (rupture length at depth and surface rupture length) tends, at
different degrees, to overestimate the earthquake magnitude.
Maximum magnitudes (Mmax) estimated by the FiSH
code for the studied faults. Abbreviations: MEtna-MTaupo is
the magnitude from earthquake scaling relationships for Etna and Taupo; MMo is
the scalar seismic moment magnitude; MAR is the magnitude from aspect ratio
relationships; Mobs is the maximum observed magnitude. Uncertainties are
represented by probability curves (see text for explanation).
These considerations suggested using both the Etna and Taupo MSRs to
calculate the seismic rates of the individual sources; in this way we tend
to minimize the epistemic uncertainty associated with them. However, the
effective interval of extrapolation of Taupo MSR is narrow, since the length
of faults to be used for estimating expected Mchar is mostly in the
range 7–11 km, i.e., next to the lower part of the Taupo MSR (see Fig. 10b,
length in logarithm scale).
Maximum expected magnitude and related mean recurrence times
The FiSH code calculates the value of magnitudes expected for the full rupture of
each fault by the above-defined empirical scaling relationships. In order to
check the geometrical consistency of the sources, it also estimates a
maximum magnitude (Mmax) according to (i) the scalar seismic
moment (MMo) by using the modified formulation of magnitude (IASPEI, 2013)
and a constant strain drop value of 3 × 10-5 and (ii) an additional
constraint based on the aspect ratio relationships (MAR) derived by
Peruzza and Pace (2002). Figure 11 shows probability curves of all the
Mmax values derived so far, assuming that a normal distribution
represents the associated uncertainty, with a symmetrical bell shape
distributed around the central value; the maximum historical observed
magnitude (Mobs) is also reported using the standard deviation of M
assigned in the earthquake catalogue. The dashed curve (SUM) represents the
summation of the probability density functions, whereas the vertical black
line indicates the central value of its Gaussian fit to be considered as the
reference mean value (Mmax), with the associated standard deviation (σMmax)
given by the horizontal dashed line (for details see Pace et al., 2016).
In general, the magnitude values calculated by the different relationships
are not drastically different from each other if the wide uncertainty ranges
are taken into account. Note that the Mmax values are consistent with
the Mobs for the simplest and best documented sources (FF, STF); in the
cases of more complex structures (e.g., PF and SLF) that are characterized by
coseismic slip and creeping alternating in space and even in time along
strike (Azzaro, 2004), the maximum observed magnitude always lies in the
range of full rupture magnitude minus 1 standard deviation (Mmax-σMmax).
The mean recurrence times (Tmean) associated with Mmax values are
computed, accounting for slip rate values and related uncertainties, which
are strongly dependent (see Fig. 9); resulting Tmean vary from 22 to
166 years (Table 1). However, these values cannot merely be compared with
those resulting from the analysis of the historical earthquake dataset,
representative of the entire SZ Timpe (Table 1). Finally, the aperiodicity
factor α, defined as the standard deviation of the recurrence times
over their mean, has been estimated by introducing the formal error
propagation to take account of the uncertainties in Mmax and slip rates
and so to explore how these uncertainties affect the variability of Tmean.
The final source models
The three types of seismic sources described above are used in the final
seismic hazard assessment following the conceptual scheme reported in Fig. 12;
for further details of the computation, the reader can refer to the
companion paper, Part 2 (Peruzza et al., 2017). The following is a brief
summary:
Area seismic sources (cf. Fig. 4) are horizontal planar
surfaces of distributed (uniform) seismicity that encompass the best-known
seismogenic fault systems on the eastern and northern flanks of Mt. Etna;
a and b values are calibrated on instrumental seismicity, effective depths
are estimated by the analyses of strain release profiles and Mmax is
based on historical earthquake data. These sources represent the so-called
“Level 1”, the simplest one with no branches, a first evolution of the
Poissonian model used by the current seismic hazard map of Italian
regulation (MPS04, Stucchi et al., 2011), where the whole volcanic edifice
was enveloped into a single polygonal area.
Fault sources (cf. Fig. 9) representing major earthquakes
(M> 4.5) are combined with the areas in a more complex source
model, namely “Level 2”. Here, the faults are individually modeled in terms
of 3-D geometry based on tectonic field data and geodetic information; they
are assumed to behave according to a characteristic earthquake model.
Background seismicity is represented by the area sources of Level 1, where
only earthquakes between M=Mmin and Mmax= 4.5 are modeled. The
logic tree is in this case represented by four branches, based on historical or
geological parameterization of characteristic earthquakes and on Poisson or
time-dependent assumptions on recurrence intervals.
The most complex source model is “Level 3”, which combines fault
sources as in Level 2 with point sources (cf. Fig. 8) which are used to
represent distributed (gridded, nonuniform) seismicity. We prefer this the
model as it is less driven by subjectivity in source definition, though it
is not free of problems or questionable choices.
These levels form alternative seismic source models, stated in order of
increasing complexity, to represent the epistemic uncertainties.
Schematic chart describing the three levels of the source models
defined for the Mt. Etna region: increasing complexities are introduced from
levels 1 to 3; the final logic tree we adopt is Level 3, with four branching
levels. Details of computations are given in Part 2 (Peruzza et al., 2017).
Conclusive remarks
In this paper we tackled the problem of characterizing low-magnitude,
shallow seismic sources, capable of affecting the seismic hazard for short
exposure times at Mt. Etna, the largest active volcano in Europe. Usually the
problem of ground shaking due to local superficial volcano-tectonic faults
is discarded in favor of estimates based on large-scale regional crustal
faults capable of generating strong earthquakes (M> 6); in
addition, other major threats related to the eruptive activity, or to the
flank instability (Acocella et al., 2013; Acocella and Puglisi, 2013), can
be first-order priorities for land planning and risk mitigation actions. But
on Mt. Etna's slopes, several inhabited localities have been repeatedly and
heavily damaged as a consequence of local earthquakes with M< 5.5
that may be connected to the eruption phases or not. In the documented
history, such damage occurred on average every 20–25 years, the last
sequence being along SVF in 2002. To tackle these
issues, the Italian Department of Civil Defense (DPC) has funded two
research programs on Mt. Etna aimed at mitigating, among other risks, the
seismic one (Acocella and Puglisi, 2010; Azzaro and De Rosa, 2016). In this
framework we started to characterize, with different methodological
approaches, shallow sources and finally to assess the seismic hazard at the
local scale of the volcano (Azzaro et al., 2012b, 2013b, 2016; Peruzza et
al., 2017). Some basic ideas have driven our analyses. Firstly, a few years of high-quality seismic monitoring in an “interseismic” period can be representative
of the long-term seismic rates of faults. Secondly, fault size and slip rate can
constrain the maximum magnitude and the seismic moment budgeting, and
geologic–geodetic-derived seismic rates must be coherent with historical and
instrumental data. If such ideas are true, we can extend the modeling of
seismic sources to the whole volcanic complex by addressing “unknown” faults
by distributed point sources. We are then no longer forced to use
independent events (i.e., the declustered earthquake catalogue, assuring
stationarity of the process) but can compute the probabilities of events
for any magnitude–frequency distribution for a generalized non-Poissonian model.
We focused our analyses on two main volcano-tectonic fault systems evaluated
at the surface and by geophysical investigations. Table 2 reports an
overview of the relevant parameters to be used as input data in the
companion paper by Peruzza et al. (2017). PF is an
E–W-oriented, S-dipping system of brittle and creeping transtensional segments:
very shallow instrumental seismicity (located very often above the sea
level) depicts quite well the 3-D geometry of this structure characterized by
low b values (< 0.7). The Timpe system in the SE flank is a group of
nearly vertical normal faults. Their deep geometry cannot be precisely
detected even by the high-quality instrumental earthquake dataset available
in recent years. Area seismic sources have been depicted with increasing
detail by using space buffers around the surface trace faults. Taken as a
whole, the FMD of the SZ Timpe – as derived from the instrumental dataset
of 2005–2015, a period that represents the “interseismic background” level not
affected by main earthquake sequences – is similar to the FMDs and depth
distributions of the MF-SLF, whilst the
FF and STF-SVF show,
respectively, lower and higher b values and activity rates. This apparent
discrepancy can be accounted for by (i) the SZ Timpe, which also includes two small
triangular areas (see upper right panel in Fig. 6), adding another
183 earthquakes (cf. Fig. 4 caption); and (ii) the “weight” of earthquakes of MF-SLF
in terms of seismic moment released, which is much higher compared to the ones of
FF and STF-SVF, and hence the similarity between SZ Timpe and MF-SLF is more evident.
Regarding the seismicity rates to be assigned to the faults, we note a
global consistency by using the geometric–kinematic approach and
the historical earthquake dataset. The maximum magnitudes (Mmax)
calculated by scaling relationships appear ca. 0.3–0.6 units higher than the
observed magnitudes (Mobs), whilst the related mean recurrence
times (Tmean) are sometimes lower, modulated by the fast slip rates. There
may be a number of reasons for these discrepancies, such as
(i) uncertainties of the geologic slip rate estimations, (ii) geometries of the
modeled faults not being well constrained, (iii) difficulty in discriminating
pre- and post-seismic slip with respect the coseismic rupture length and
(iv) the role of fault segments in accommodating deformation (slip rate partitioning).
Finally, the aperiodicity coefficients suggest sensitivity tests and care in
modeling faults by a time-dependent approach: the α's obtained by
geologic data indicate a quasi-stationary behavior of the maximum-sized
events, whilst the one calculated from the intertimes of historical
earthquakes suggests a certain degree of periodicity. Both the seismicity
rates for Mmax, however, are within the uncertainties of rates derived
by the GR relationships of instrumental data.
This work helps to improve our basic knowledge of seismogenic processes at
Etna. Furthermore, it represents an effort to provide the international
scientific community with original procedures and methodological approaches
to produce hazards maps in other volcanic areas.
Most of the data are available on the institutional websites.
Those data not available on the institutional websites can be provided upon request.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Linking faults to
seismic hazard assessment in Europe”. It is not associated with a conference.
Acknowledgements
Many thanks are due to G. Weatherill and C. Beauval for their fruitful
comments and constructive criticism. The editor O. Scotti is also
acknowledged for her valuable suggestions. R. Gee gave useful hints for
developing some of the numerical models in the initial stage of the work.
This study has benefited from funding provided by the Italian Presidenza del
Consiglio dei Ministri – Dipartimento della Protezione Civile (DPC), in the
frame of the 2012–2014 Agreement with Istituto Nazionale di Geofisica e
Vulcanologia (INGV), project V3 “Multi-disciplinary analysis of the
relationships between tectonic structures and volcanic activity”.
This paper does not necessarily represent DPC official opinion and policies.
The authors are grateful to S. Conway for revising the English text.
Edited by: Oona Scotti
Reviewed by: Graeme Weatherill and Céline Beauval
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