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Abstract

Local seismic response (LSR) studies are considerably conditioned by the seismic input features due to the nonlinear soil behavior under dynamic loading and the subsurface site conditions (e.g., mechanical properties of soils and rocks and geological setting). The selection of the most suitable seismic input is a key point in LSR. Unfortunately, few recordings data are available at seismic stations in near-field areas. Then, synthetic accelerograms can be helpful in LSR analysis in urbanized near-field territories. Synthetic accelerograms are generated by simulation procedures that consider adequately supported hypotheses about the source mechanism at the seismotectonic region and the wave propagation path toward the surface. Hereafter, mainshocks recorded accelerograms at near-field seismic stations during the 2016–2017 Central Italy seismic sequence have been compared with synthetic accelerograms calculated by an extended finite-fault ground-motion simulation algorithm code. The outcomes show that synthetic seismograms can reproduce the high-frequency content of seismic waves at near-field areas. Then, in urbanized near-field areas, synthetic accelerograms can be fruitfully used in microzonation studies.
Are Synthetic Accelerograms Suitable for
Local Seismic Response Analyses at
Near-Field Sites?
Francesca Mancini*1 , Sebastiano DAmico2, and Giovanna Vessia3
ABSTRACT
Local seismic response (LSR) studies are considerably conditioned by the seismic input fea-
tures due to the nonlinear soil behavior under dynamic loading and the subsurface site
conditions (e.g., mechanical properties of soils and rocks and geological setting). The selec-
tion of the most suitable seismic input is a key point in LSR. Unfortunately, few recordings
data are available at seismic stations in near-field areas. Then, synthetic accelerograms can
be helpful in LSR analysis in urbanized near-field territories. Synthetic accelerograms are
generated by simulation procedures that consider adequately supported hypotheses
about the source mechanism at the seismotectonic region and the wave propagation path
toward the surface. Hereafter, mainshocks recorded accelerograms at near-field seismic
stations during the 20162017 Central Italy seismic sequence have been compared with
synthetic accelerograms calculated by an extended finite-fault ground-motion simulation
algorithm code. The outcomes show that synthetic seismograms can reproduce the high-
frequency content of seismic waves at near-field areas. Then, in urbanized near-field areas,
synthetic accelerograms can be fruitfully used in microzonation studies.
KEY POINTS
Are stochastic simulations suitable for local seismic
response analyses at near-field areas?
Results showed that simulations represent the high-fre-
quency content of natural signals at near-field areas.
Synthetic signals can be profitably used in microzonation
studies to predict local amplification effects.
INTRODUCTION
Local administration and national planning programs are pay-
ing increasing attention to ground shaking and seismic site
effects in urban centers especially set in near-field areas due
to the large, differentiated damages occurring during the mod-
erate-to-large earthquakes (Mw>5). Local site features can
significantly affect the site response (Lanzo et al., 2019;
Amanti et al., 2020) differently amplifying its seismic motion
even at hundreds of meters distance and migrating it to a
period or frequency range similar to those natural frequencies
of buildings and infrastructures (commonly above 1 Hz)
(Akinci et al., 2013;Panzera et al., 2016). Local seismic
response (LSR) is related to many different coseismic phenom-
ena: surface amplification due to the seismic impendence con-
trast between soft soil deposits overlaying a rocky bedrock
(DAmico et al., 2008;Cox et al., 2011;Grasso and Maugeri
2012;Puglia et al., 2013;Pino et al., 2018), seismic-wave focali-
zation due to basin-shaped bedrock (Frischknecht et al., 2005;
Vessia et al., 2011;Rainone et al., 2013;Vessia and Russo, 2013;
among others), topographic effects induced by soil and rock
reliefs (Paolucci 2002;Primofiore et al., 2020; and references
therein), slope instability (Lanzo et al., 2019), and liquefaction
occurrence in which loose soil deposits lay under the ground-
water level (e.g., Boncio et al., 2018). Thus, planning activities
worldwide are nowadays involved in evaluating the LSR aiming
at drawing microzoning maps (Vella et al. 2013;Panzera et al.,
2017,2019;Vessia et al., 2020,2021).
The aforementioned phenomena have been observed espe-
cially at near-field areas also named by Boore (2014) as the
fault damage zones (FDZs). These areas are where most of the
heavy damages occur. Highly scattered seismic ground
motions in FDZs are strongly dependent on the faulting
mechanism, source directivity, and nonlinear soil behavior
1. International Research School of Planetary Sciences, University, G. DAnnunzio
of Chieti-Pescara, Pescara, Italy, https://orcid.org/0000-0003-2410-0818 (FM);
2. Department of Geosciences, University of Malta, Msida MSD, Malta, https://
orcid.org/0000-0001-7429-4767 (SD); 3. Department of Engineering and Geology,
University G. DAnnunzioof Chieti-Pescara, Chieti, Italy, https://orcid.org/0000-
0003-1733-7112 (GV)
*Corresponding author: francesca.mancini@unich.it
Cite this article as Mancini, F., S. DAmico, and G. Vessia (2021). Are Synthetic
Accelerograms Suitable for Local Seismic Response Analyses at Near-Field Sites? Bull.
Seismol. Soc. Am. XX,116, doi: 10.1785/0120210074
© Seismological Society of America
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at the site. Because of these effects, ground motions in the
FDZs may exhibit impulsive character (Kalkan and Kunnath,
2006;Mollaioli et al., 2006;Baker, 2007), high-frequency con-
tents, and directivity (Peruzzi and Albarello, 2017;Mancini
et al., 2018). FDZ observed data show peculiar characters that
are dissimilar to records taken away from the source.
Unfortunately, at FDZs observed data are few and only avail-
able for the most recent strong seismic events.
Several national building codes allow carrying out LSR
numerical simulations using as input motions alternatively
observed or synthetic accelerograms: the first ones are widely
used and collected in databases (i.e., ITalian ACcelerometric
Archive, Observatories and Research Facilities for European
Seismology-European Integrated Data Archive, Global
Seismographic Network Heliplot, among others) although they
are not requested to be selected depending on the distance to
the source but only to the spectral compatibility to the Code
Reference Spectrum (De Normalisation, 1998;International
Code Council Inc. [IBC], 2009;Norme Tecniche per le
Costruzioni [NTC18], 2018). The second ones are admitted
as input motions when generated by simulation procedures
that take into account both the source mechanism and the
propagation, provided that the hypotheses concerning the seis-
mogenic characters of the source and propagation are
adequately supported (i.e., Uniform Building Code [UBC],
1997;De Normalisation, 1998;National Earthquake Hazards
Reduction Program [NEHRP], 2001;IBC, 2009;NTC18, 2018).
Recently, an Italian web repository called SYNTHESIS has
been proposed (DAmico et al., 2017).
In this study, synthetic records reproducing near-fault
observed data recorded during the 20162017 central Italy seis-
mic sequence have been simulated by an extended finite-fault
ground-motion simulation algorithm (EXSIM) code (Boore,
2003;Motazedian and Atkinson, 2005). To get reliable results,
it is decisive to set the simulations with robust model parameters.
It is important to use as much as possible a priori information;
this method requires region-specific source, path, and site char-
acterizations as input model parameters. Thus, to reproduce the
high-frequency content, the regional attenuation model cali-
brated for the area (Malagnini et al., 2011;Pischiutta et al.,
2016,2021) and the source models provided by Chiaraluce et al.
(2017) were used. The source mechanism of the 20162017 cen-
tral Italy events and regional path effects are well constrained
from previous studies of these earthquakes (Malagnini et al.,
2011;Pischiutta et al.,2016,2021;Ren et al., 2017). The local
site effects at the selected stations are studied as a combination
of the kappa operator and frequency-dependent soil amplifica-
tion. EXSIM was employed to generate synthetic accelerograms
by the stochastic finite-fault approach. Comparison between
observed and synthetic input motions has been accomplished
to check the capacity of synthetic accelerograms to reproduce
the peculiar characters of the seismic records at some stations
in FDZs.
The article briefly introduces seismic characters of the study
area, then summarizes the evolution of the methodologies for
generating synthetic inputs in the last 50 yr, and after describes
the method used herein to generate the synthetic accelero-
grams illustrating the main features of the simulated seismic
sequence. Finally, a discussion on the results and comparisons
among recorded and synthetic data is given.
SEISMOTECTONIC CHARACTERS OF THE ITALIAN
CENTRAL APENNINE RANGE
The Italian area struck by the 20162017 seismic sequence is
located along the central Apennines (Fig. 1). Several tectonic
phases with opposite kinematics characterized the develop-
ment of this sector of the Apennine chain, with abundant reac-
tivations and tectonic inversion processes of the structural
elements (Chiaraluce et al., 2017). Nowadays, the Apennines
are characterized by 1.53 mm/yr northeastsouthwest post-
orogenic extension according to Global Positioning System
data (Devoti et al., 2011;Carafa and Bird, 2016), expressed
at the surface by north-northwestsouth-southeast Quaternary
normal faults (Pizzi and Galadini, 2009) and by the series of
moderate magnitude historical and instrumental seismic
events (Luzi et al., 2016,2017;Locati et al., 2019;Rovida et al.,
2019). The moment tensor solutions of the 20162017 seismic
events show mainly normal-faulting mechanisms coherent
with extensional kinematics (Fig. 1). The present extensional
regime followed an earlier compression phase: the Mt.
Sibillini thrust (MST) is the evidence of a previous compres-
sional tectonic phase, as widely accepted (Scisciani et al., 2014).
This pre-existing structural barrier is interposed between two
different faults involved in the 20162017 sequence, namely
the Mt. VettoreMt. Bove fault system (MVBF) and the Laga
Mts fault system, respectively, located at the hanging wall and
the footwall of the MST. This system juxtaposes the Triassic
Miocene UmbriaMarche carbonate succession on to the
Messinian siliciclastic turbidites of the Laga Formation
(Calamita et al., 2000;Galadini and Galli, 2000).
The 20162017 central Italy seismic sequence occurred at
upper crustal depths (<1011 km) and started on 24 August
2016 with Mw6.0 mainshock, located nearly 20 km south-
southeast of the 30 October mainshock (Chiaraluce et al.,
2017). No foreshocks were felt in the months beforehand.
Geodetic and seismological data showed that the first main-
shock ruptured both Mt. Vettore and Mt. Bove fault systems
(Chiaraluce et al., 2017). Indeed, field surveys in the epicentral
area (Emergeo et al., 2016) revealed about 5-km-long and con-
tinuous surface ruptures along the southern portion of the
MVBF. Intense seismic activity with smaller earthquakes
(Mw<5) continued for months as aftershocks until the end
of October. On 26 October, an Mw5.9 seismic event activated
the northern MVBF, located at about 15 km north-northeast
farther from the strongest event (Chiaraluce et al., 2017). On
30 October, Mw6.5 mainshock activated ruptured multiple
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segments of the MVBF: it resulted in a remarkable system of
surface ruptures that overprinted those of the previous two
main 2016 surface-faulting earthquakes. This was the strongest
earthquake in the last 30 yr in Italy, and it was so strong that
the morphology of the territory changed in some areas (Lanzo
et al., 2019). Finally, almost five months after the first seismic
emergency, on 18 January 2017 four events ranging Mw5.05.5
occurred along the Campotosto fault, in the Apennine
southern part of the seismic sequence epicenters.
SYNTHETIC SEISMOGRAM: STATE OF THE ART
Since their introduction in the 80s, synthetic accelerograms
were generated by two main approaches: deterministic
(Hartzell et al., 1999;Frankel, 2009) and stochastic (Hanks
and McGuire, 1981). This latter combines the seismological
models represented by a particular spectral amplitude shape
of the ground-motion data
with the notion that high-
frequencies motions (com-
monly > 0.1 Hz) have a
random distribution of the
phases (Hanks, 1979;
McGuire and Hanks, 1980).
The stochastic method is
widely used to compute
ground motions at frequencies
of engineering interest. Since
its introduction and after the
enhancements described by
Boore (1983), the method has
been extended to model sto-
chastic finite-fault effects
(Beresnev and Atkinson,
1998a;Motazedian and
Atkinson, 2005), and deter-
ministic finite-fault effects at
low frequencies to produce
hybrid stochasticdeterminis-
tic simulations (Frankel, 2009;
Graves and Pitarka, 2010).
Beresnev and Atkinson
(1997,1998a,b) proposed to
divide the fault plane into a
grid of several subfault units.
Each subfault is treated as a
point source. The synthetic
ground-motion time histories
of all subfaults are then
summed up at the observation
site (i.e., the site under study).
The contributions of each sub-
fault are estimated by the sto-
chastic point-source method.
Owing to the rupture propagation, a proper time delay of
the subsources must be taken into account. Each subfault
can roughly be considered as a point source that has a ω2spec-
tral shape (Aki, 1967;Brune, 1971;Boore, 1983). This latter
was the first mathematical expression to represent the spec-
trum of the seismic waves radiated from complex faulting.
In this mathematical expression, assumptions about the form
of the autocorrelation function of the fault slip in space and
time are used to derive a ω-square model of the spectrum.
Then, the similarity assumption was used to derive a
source-scaling law, demonstrating that the spectral amplitude
follows the inverse-cube power function of the corner fre-
quency (Brune, 1971).
One of the first stochastic codes was developed by Beresnev
and Atkinson (1998b) and was called FINSIM. Finally,
Boore (1983,2003,2005) developed the Stochastic-Method
Figure 1. Instrumental 20162017 seismicity in the central Apennine, with a magnitude between 2 and 6.5 (gradu-
ated blue circles and stars) from ISIDe (see Data and Resources). ISSs from DISS website with their surface trace
(orange line) and their fault-plane projection (light-orange box). Earthquake mechanisms were obtained from the
Global Centroid Moment Tensor Project (see Data and Resources). The inset map on the top right shows the affected
area by the 20162017 Central Italy seismic area (red polygon). Red denotes the main fault system. The map has been
realized with ArcMap. DISS, Database of Individual Seismogenic Source; ISIDe, Italian Seismological Instrumental and
Parametric Data-Base; ISSs, Individual Seismogenic Sources; LF, Laga Mts fault system; MST, Mt. Sibillini thrust; MVBF,
Mt. VettoreMt. Bove fault system. The color version of this figure is available only in the electronic edition.
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SIMulation (SMSIM) program for which the point-source
stochastic models were considered for each subsource to
calculate the synthetic time series.
Recently, Motazedian and Atkinson (2005) introduced
improvements and novelties to the subsource characterization
that have been implemented in EXSIM code.
Boore (2009) made additional bettermentstothistechniquein
EXSIM code by considering the following adjustments: (1) scaling
the high-frequency motions based on the squared Fourier accel-
eration spectrum integral; (2) avoiding truncating subfaults
time histories; (3) taking the inverse of the corner frequency
of subfaults for deriving the motion duration from each subfault;
and (4) for each subfault, using a filter function to increase
the spectral amplitudes at the frequencies equal and less than
the corner one. The preceding improvements have been imple-
mented into the release of the EXSIM code used hereinafter.
EXSIM is an open-source stochastic finite-source simula-
tion algorithm written in FORTRAN that generates accelera-
tion time series of ground motions for tectonic earthquake
simulation (Motazedian and Atkinson, 2005;Boore, 2009).
A fault plane, with an assigned dimension related to its seismic
moment, is divided into an array of subsources, each of which
is treated as a point source.
The steps for simulating ground motions for each subsource
follow the methodology implemented in the SMSIM algorithm
by Boore (2003,2005): a dynamic corner frequency has been
introduced so that each subfault is assigned one to improve the
stochastic finite-fault method. The corner frequency is
assumed dynamic because it is a function of time. The fre-
quency content of the subsources is controlled by the rupturing
process: after the rupture triggering, the larger the rupture the
lower the frequency (Motazedian and Atkinson, 2005).
EXSIM METHOD AND DATA PROCESSING
The peak ground acceleration (PGA) values and the response
spectra, at fixed periods of 0.10.3 and 1.0 s, are highly scat-
tered at FDZs, that is, 3040 km from the epicenter (Mancini
et al., 2018). Owing to the scarcity of observed data recorded
within FDZ especially on soil Acategory (shear-wave veloc-
ity > 800 m/s, according to NTC18 the numerical simulations
of the seismic events are helpful (Hollender et al., 2018). In
seismic design codes of many countries, site effects are taken
into account through the shear-wave velocity profile and the
average shear-wave velocity of the subsoil in the first 30 m,
given by the VS30 parameter. The Italian building code defines
five seismic soil categories: A (VS30 >800 m=s), B (360 m=s<
VS30 <800 m=s), C (180 m=s<VS30 <360 m=s), D
(100 m=s<VS30 <180 m=s), and E (soils velocity like C
and D with thickness lower than 30 m).
Fault-finite modeling, indeed, uses the same process as the
point-source model; however, the fault plane is divided into a
smaller rectangular cell. In this way, each subfault acts as a sep-
arate point source and the temporal contribution of each of
them is added, with an appropriate time delay, to produce
the desired effects.
Analyzing the accelerometric stations located within
100 km radius from the epicenters of the three seismic events
occurred in central Italy, 24 August Mw6.0, 26 October
Mw5.9, and 30 October Mw6.5, it can be seen that only 18%
of them are placed on subsoil category A and A*. A* means the
soil category is not directly measured but estimated by indirect
methods or based on geological maps and, therefore, not char-
acterized by the VS30 measure.
For local seismic response, the input motion must be
recorded at the outcropping seismic bedrock (VS>800 m=s).
In this study, only the seismic station data recorded on soil A
or A* have been selected for being compared with the simu-
lated accelerograms.
The parameters needed for stochastic fault-finite modeling
describe the effects due to the source, propagation, and site.
The parameters used (and their references) in these simulations
are shown in Table 1and the synthetic waveforms are related to
the three largest seismic events of the 20162017 central Italy
seismic sequence. The time domain moment tensor (TDMT)
solution for the events of 24 August, 26 October, and 30
October suggests moment magnitudes 6.0, 5.9, and 6.5, respec-
tively, with normal-fault kinematics and northwestsoutheast
fault-plane direction. This information is available on the
Istituto Nazionale di Geofisica e Vulcanologia (INGV) website
or the portal of the European Engineering Strong Motion
Database (Luzi et al.,2016). The moment magnitude as well
as the fault mechanism type was double checked using the
cut-and-paste method (e.g., DAmico et al., 2010,2013,2014).
The seismogenic boxes in Figures 2a,3a, and 4a used for the
simulations are those proposed by Chiaraluce et al. (2017). The
calculation code, through the coordinates of the upper edge
fault and the strike, dip, and hypocenter depth, models the
source. The size assigned to each subfault is 2 × 2 km. The
near-field effects are ruled by the source properties, such as
the distribution of asperities on the fault plane, intermediate
distances are controlled by path effect, the seismic-wave propa-
gation, and the attenuation of seismic waves. Many studies
confirmed that the ground motions recorded at near-fault sta-
tions were strongly affected by the local slip asperities along the
fault rupture (Akinci and Antonioli, 2012;Pitarka et al., 2019,
2021;Cheloni and Akinci, 2020;Pischiutta et al., 2021;Ojeda
et al., 2021). In this work, the slip distributions given in the
Finite-Fault Analysissection in the supplemental material
of Chiaraluce et al. (2017) were considered, and the slip model
of Amatrice earthquake is taken from Tinti et al. (2016).
The stress drop is one of the most critical source parame-
ters: as evidenced by Moghaddam et al. (2008), the spectral
accelerations of the simulations increase with an increasing
stress drop. For simulations, the stress-drop values proposed
by the recent study Bindi et al. (2018) have been used.
They are focused on the temporal variation of this parameter
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in central Italy, starting from the sequence of LAquila 2009.
For each earthquake, the authors used the generalized inver-
sion technique to define the stress drop. Converting the values
from Pa, the values are equal to 115 bar, 81 bar, and 110 bar for
Mw6.0, 5.9, and 6.5 events, respectively.
In addition, propagation parameters are a relevant compo-
nent in seismic motion simulations. With the stochastic
method, the propagation effects are modeled through the geo-
metric and inelastic attenuation functions in the frequency
domain (Boore, 2003). They depend on both the hypocenter
and the fault-plane orientation, due to the different propaga-
tion paths between the source and the site.
To insert the geometric attenuation in the EXSIM algo-
rithm, the number of lines or segments of the function must
be assigned, the distance ranges of the segments in kilometers,
and the exponents at each distance interval. The inelastic
attenuation is inversely related to the quality factor Q(f), which
is commonly considered equal to:
EQ-TARGET;temp:intralink-;df1;41;146QfQ0fη;1
in which Q0is the initial quality factor and the parameter η
defines the frequency dependence of Q(f). The initial quality fac-
tor Q0and the exponent ηare input values to be inserted in the
EXSIM algorithm; they are empirically determined for different
study areas. As illustrated by Moghaddam et al. (2008),the
quality factor affects the simulated spectral acceleration in the
low-period range. The quality factor and the exponent ηand
the site effect parameter kappa used in the present simulations
are those proposed by Pischiutta et al. (2021) (see Table 1).
In this study, the duration model valid for active crustal
regions proposed by Boore and Thompson (2014) and the
crustal amplification of a rock site suggested by Boore and
Joyner (1997) has been adopted.
The rock layer alterations in the first meters below the surface
are one of the reasons for the amplification of the seismic motion
due to site conditions. To simulate this phenomenon, the fre-
quency and the amplification factor associated with the subsoil
category must be assigned in the algorithm. The subsoil catego-
ries are referred to the American classification National
Earthquake Hazards Reduction Program (NEHRP) (Federal
Emergency Management Agency, 2003). They are based on the
value of the VS30: the American category B corresponds to the
Italian A. According to Italian NTC18, the seismic bedrock is
represented by soil category A with VS30 >800 m=s, whereas
American NEHRP considers VS30 >1500 m=s for rocky behav-
ior (Atkinson and Boore, 2006). However, the NEHRP B/C
boundary with VS30 >760 m=s is close to NTC18 class A
(VS30 >800 m=s). For this reason, the site amplification func-
tion proposed by Atkinson and Boore (2006) for NEHRP site
category B/C boundary was used.
TABLE 1
Input Parameters Used for the Stochastic Simulations of the Central Italy Earthquakes with EXSIM, Considering the Fault-Slip
Models Given by Chiaraluce et al. (2017)
Factor Parameters Value References
Source Moment magnitude 6.5 5.9 6.0 Luzi et al. (2016)
Fault kinematics Normal INGV-webservice
Length and width of the fault 26.0 km, 14.0 km 10.0 km, 10.0 km 26.0 km, 12.0 km Chiaraluce et al. (2017)
DirectionImmersionDepth 151°47°9.2 km 159°47°7.5 km 155°49°8.1 km Luzi et al. (2016)
Hypocentral localization 42.83°13.11° 42.91°13.13° 42.70°13.23° Luzi et al. (2016)
Upper wedge fault 42.9493°13.1517° 43.0122°13.131° 42.8773°13.2101° Chiaraluce et al. (2017)
Subfault dimensions 2.0 × 2.0 km 2.0 × 2.0 km 2.0 × 2.0 km Chiaraluce et al. (2017)
Stress drop 110 bars 81 bars 115 bars Bindi et al. (2018)
Shear-wave velocity 3.5 km/s Motazedian and Atkinson (2005)
Density 2.8 g/cm3Motazedian and Atkinson (2005)
Vrup=VS0.8 Atkinson and Boore (2006)
Box window SaragoniHart Saragoni and Hart (1973)
Pulsing percentage 50% Motazedian and Atkinson (2005)
Path Geometric spreading R1:1for 1 <R<10 km,
R1:0for 10 <R<40 km,
R0:7for 40 <R<100 km,
R0:5for R>100 km
Pischiutta et al. (2021)
Quality factor Qf27510f2for f<0:2Hz;
Qf68:755f0:584 for 0:2>f<0:6Hz;
Qf140f0:25 for f>0:6Hz
Pischiutta et al. (2021)
Duration Path-duration model for active tectonic regions Boore and Thompson (2014)
Crustal amplification Crustal amplification for active tectonic regions Boore and Joyner (1997)
Site Kappa 0.02 Pischiutta et al. (2021)
Site amplification Site amplification factors for NEHRP B/C boundary (VS>800 m=s) Atkinson and Boore (2006)
INGV, Istituto Nazionale di Geofisica e Vulcanologia; NEHRP, National Earthquake Hazards Reduction Program.
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Figure 2. (a) Geologicaltechnical map and related legend of the hamlet of
Casanova for the microzonation studies in scale 1:5.000 (see Data and
Resources). (b) Numerical model of Casanova necessary for the local seismic
response (LSR) analysis with indications of the lithologies present along the
reconstructed section AA(black line) from southwest to northeast, with
control points (red circles). ALS-B2, Marne a Orbulina or Pteropodi
Formation; ALS-B3, arenaceouspelitic association II; ALS-B4, pelitic
arenaceous association; AS, substrate alteration; LPS, Marne con
CerrognaFormation; MH, inorganic silts and fine sands. The color version
of this figure is available only in the electronic edition.
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LOCAL SEISMIC RESPONSE ANALYSES AT NEAR-
FIELD AREA OF CORTINO VILLAGE
Casanova is a small Italian hamlet set in the territory of
Cortino village, located in Teramo province (Abruzzo region,
Italy): it falls in the central southern portion of the Apennine
chain, on the footwall of the MST within the Laga basin.
The historic center of Cortino belongs to an area with a
medium seismic hazard in which the expected 475 yr return
period PGAs range from 0.17 to 0.25g. Moreover, it belongs
to the so-called seismic craternamely the territorial area that
was heavily affected by the earthquake sequence of 20162017.
The earthquake of 24 August 2016 was the most relevant for
this village, within the 20162017 seismic sequence, because of
its vicinity to the epicentral distance (23 km). Brando et al.
(2020) discussing the damage patterns observed after the
20162017 central Italy seismic sequence for all the masonry
buildings in Cortino pointed out that heavy structural damage,
very heavy nonstructural damage with serious failure of walls
and partial structural failure of roofs and floors have been
observed in the east sector of Cortino.
In the Casanova hamlet, there is the most complex geologi-
cal pattern due to the variation of the units of the bedrock.
According to the seismic microzonation studies of the
Macroarea 8 Casanova(see Data and Resources), the silici-
clastic deposits at the site under study correspond to the
Campotosto member of the Laga Formation that is subdivided
into many units characterized by different arenite and pelite
Figure 3. (a) Shake maps of the seismic events of 24 August considering the
seismogenic box proposed by Chiaraluce et al. (2017) (see Table 1). Location
and shear-wave velocities VS30 of the acceleration stations were obtained from
the Engineering Strong-Motion (ESM) database (see Data and Resources)The
map has been made with ArcMap. (b) Mean acceleration response spectra
ratio between observed and simulated at all the A and A* stations. Fourier
amplitude spectra (FAS) processed in the MATLAB (see Data and Resources)
environment from the observed and simulated waveforms related to the: 24
August earthquake for the (c) LSS, (d) ANT, (e) SPM, (f) MNF, and (g) GNU
stations. Observed ground-motion data were downloaded from the ESM
database, and the simulated ones were obtained through an extended finite-
fault ground-motion simulation algorithm (EXSIM) code (see Table 1). The color
version of this figure is available only in the electronic edition.
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Figure 4. (a) Shake maps of the seismic events of 26 October considering the
seismogenic box proposed by Chiaraluce et al. (2017) (see Table 1).
Location and shear-wave velocities VS30 of the acceleration stations were
obtained from the ESM database (see Data and Resources) The map has
been made with ArcMap. (b) Mean acceleration response spectra ratio
between observed and simulated at all the A and A* stations. FAS
processed in the MATLAB environment from the observed and simulated
waveforms related to the 24 August earthquake for the (c) LSS, (d) CLO,
(e) MMO, (f) MNF, (g) T1212 (h) ACC, (i) MZ19, (j) MZ11, (k) MZ14,
(l) GNU, (m) T1211, and (n) ANT stations. Observed ground-motion data
were downloaded from the ESM database, and the simulated ones were
obtained through EXSIM code (see Table 1). The color version of this figure
is available only in the electronic edition.
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ratio. In the studied area, two lithofacies associations partially
crop out along the slope and on the hill (Fig. 2b): the arena-
ceouspelitic association II (ALS-B3) and the peliticarena-
ceous association (ALS-B4). The pre-evaporitic member of
the Laga is in onlap on the preturbidite transitional carbonate
units, known as Marne con CerrognaFormation (LPS) and
Marne a Orbulina or PteropodiFormation (ALS-B2): the
first is constituted by layered marly limestones and the second
one consists of alternations of marly arenaceous rocky levels.
These formations lay at the bottom of the slope and in the flat
area of the village, overlain by 7 m of quaternary deposits. The
eluvialcolluvial layers represent the alteration product of the
basic geological units (AS); therefore, they have different char-
acteristics depending on their parent rocks. In the analyzed
area, they are inorganic silts and fine sands (MH). The map
is shown in Figure 2a together with the related numerical cross
sections in Figure 2b. The numerical model was reconstructed
from the geophysical investigations carried out in the built-up
area of Casanova. The mechanical properties of the lithologies
found in the reconstructed technicalgeological cross section
in Figure 2b are shown in Table 2. Shear-wave velocities have
been defined from a down hole investigation in the green circle
(Fig. 2a). From the map, it can be noticed that the urban area is
placed in correspondence of the quaternary deposits and the
instable zone.
Two-dimensional numerical analyses have been performed
through the commercial code LSR2D by STACEC based on the
equivalent linear finite-element method, and the amplification
factors have been calculated along with a vertical section of
Cortino village.
RESULTS AND DISCUSSION
Figures 3a,4a, and 5a show the Shake maps of the three seismic
events: 24 August 2016 Mw6.0, 26 October 2016 Mw5.9, and
30 October 2016 Mw6.5, respectively, considering the seismo-
genic box proposed by Chiaraluce et al. (2017) (see Table 1).
The Fourier amplitude spectra (FAS) of the strong-motion
records compared with the synthetics are given in Figures 3cg,
4cp, and 5coat selected stations placed on soil category A
and A* in near-field areas at different Joyner and Boore dis-
tances from the three considered events up to a maximum
of 47 km.
The frequency content and the duration of the synthetic
acceleration time histories are similar to those observed.
From Figures 3to 5, it is worth noticing that Fourier spectra
of the simulated data are in good agreement with those
recorded according to their general shapes and amplitudes
at most of the stations and especially within the frequency
range 101Hz (10 s)2 Hz (0.5 s). In some cases, in the syn-
thetic Fourier spectra, the observed peaks are not reproduced:
for example, at 0.45 and 1.1 Hz in MNF stations in Figure 4f.
Nonetheless, such a misfitting at low frequencies is not that
relevant for microzonation studies. The input signals used
in finite element numerical simulations of local seismic
response investigate the soil behavior in the period range
0.1 s (10 Hz)1.1 s (0.9 Hz). To this end, it is helpful to com-
pare simulated and observed acceleration response spectra in
the period range 0.11.1 s. A common approach to check the
ability to simulate recordings accelerograms (Zhong, 2016)is
to calculate the logarithmic spectral ratios Rbetween the two
signals, in terms of acceleration response spectra:
EQ-TARGET;temp:intralink-;df2;308;354Rln SAsyn
SAnat
;2
in which SAsyn and SAnat are the acceleration response spec-
trum of both synthetic and recordings data, respectively. Mean
acceleration response spectra ratio between observed and
simulated data at all A and A* stations are shown in
Figures 3b,4b, and 5b. Note that the Rvalues vary for most
cases from 0 to 0.5: this variation range is commonly consid-
ered a good quality interval of Rfor simulated Fourier spectra.
Thus, the model effectively reproduces observed data at near
field, in the period range 0.11s.
The simulated stochastic accelerations have been compared
with the commonly used ground-motion prediction equations
(GMPE) for the active shallow crustal regions: the Italian
model of Bindi et al. (2011; hereafter, ITA10). The selected
GMPE was derived from the normal-faulting style and for site
conditions related to NTC18 classes-A. In Figure 6, we show
the simulated PGAs up to 100 km as a function of Joyner and
Boore distance for the selected seismic stations on soil A: 5, 12,
and 11 for the 24 August in Figure 6a, 26 October in Figure 6b,
and 30 October in Figure 6c, respectively. The observed ground
TABLE 2
Input Parameters Used for the 2D Local Seismic Response Analysis with LSR2D
Layers γ(KN=m3)VS(m/s) νDecay Curves
MH 20 230 0.39 Darendeli and Stokoe (2001) (PI = 30, 2 atm)
AS 23 490 0.42 Modoni and Gazzellone (2010)
ALS-B3 24 1180 0.38 Linear D=1%
ALS-B4 22 900 0.40 Linear D= 0.5%
ALS-B2 22 1090 0.32 Linear D= 0.5%
LPS 23 1250 0.30 Linear D= 0.5%
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Figure 5. (a) Shake maps of the seismic events of 30 October considering the
seismogenic box proposed by Chiaraluce et al. (2017) (see Table 1).
Location and shear-wave velocities VS30 of the acceleration stations were
obtained from the ESM database (see Data and Resources) The map has
been made with ArcMap. (b) Mean acceleration response spectra ratio
between observed and simulated at all the A and A* stations. FAS
processed in the MATLAB environment from the observed and simulated
waveforms related to the: 24 August earthquake for the (c) LSS, (d) CLO,
(e) T1212, (f) ACC, (g) MMO, (h) MZ19, (i) MNF, (j) MZ14, (k) T1211,
(l) GNU, and (m) ANT stations. Observed ground-motion data were
downloaded from the ESM database, and the simulated ones were obtained
through EXSIM code (see Table 1). The color version of this figure is
available only in the electronic edition.
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motion and the aforementioned GMPE together with their σ
standard deviations are plotted. As it is seen in Figure 6, the
observed ground motions show large variability and highly
scattered data that might be due to several parameters concern-
ing the source location, propagation, and site effects. It is
observed that the simulated ground motions are in close agree-
ment with observations.
Taking into account the complexity of the three seismic
events and the calibrated model used in the study, three repre-
sentative acceleration response spectra can be provided to be
compared with the acceleration code spectrum (NTC18) in
Figure 7at a site located nearby the three considered epicenters
that is Cortino village (ag0:25g). The Italian building code
NTC18, at section 3.2.3.6, states that for the input motions
of the response seismic analyses
recordings accelerograms or
synthetic ones are allowed. In
particular, the use of ground-
motion time histories generated
by simulating the source and
propagation mechanism is
allowed provided that assump-
tions about the seismogenic
characteristics and the propaga-
tion paths are adequately justi-
fied. These simulated spectra
show higher spectral ordinates
than the code spectrum, but
they are spectrum compatibles
in the requested range of 10%
less and 30% more than the
code spectrum within the
period range 0.11.1 s. Thus,
they can be used in LSR analy-
ses at Cortino village.
Two LSR analyses have been
performed: (1) with observed
and (2) synthetic accelerograms.
For the first case, seven
accelerograms whose average response spectrum was code-spec-
trum-compatible were selected through the REXEL software
(Iervolino et al., 2010), for the municipality of Cortino. All
the records are related to normal-fault source mechanism and
Figure 7. Comparison of the response spectra of the 24 August (in red), 26 October (in light blue), and 30 October
(in green) simulated events with the normative response spectrum related to soil A (NTC18) for the Cortino site. The
map on the top right shows the localization of Cortino village on a national scale. The color version of this figure is
available only in the electronic edition.
Figure 6. Ground-motion parameters peak ground acceleration (PGA) of sto-
chastic synthetics related to the considered stations and using slip models
available in the Finite-Fault Analysissection in the supplemental material of
Chiaraluce et al. (2017). We show comparison with ground-motion prediction
equations by the Italian model of Bindi et al. (2011; hereafter, ITA10) of the
(a) 24 August, (b) 26 October, and (c) 30 October seismic events. The recorded
values on the horizontal geometrical mean components at the seismic stations
are reported as well (crosses). Lines color are related to the soil A seismic site
class according to Norme Tecniche per le Costruzioni (NTC18). The color
version of this figure is available only in the electronic edition.
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reference site A*. For the second kind of analyses, the response
spectra of the simulated events in Figure 7have been used.
To have a better insight on the differences between the LSR
analysis, we compared the two in terms of the Housners
amplification factor along the cross-section AA. From the
Housner spectral intensity, amplification factors were quanti-
fied at the control points of the numerical section located on
the surface (Fig. 2b):
EQ-TARGET;temp:intralink-;df3;53;458FAHRT2
T1PSVoutdT
RT2
T1PSVindT ;3
in which PSVout is the output pseudovelocity spectrum at a
location; PSVin is the input pseudovelocity spectrum at the
same location as the PSVout;T1and T2represent the extremes
of two-period ranges of calculation. A shortperiod range
with extremes T10:1 s and T20:5 s and a longperiod
range with extremes T10:5 s and T21:0s.
These results are summarized in the curves of the Housners
amplification factors in Figure 8calculated in two-period
ranges: FA10:10:5 s (continuous lines), FA20:51s
(dashed lines). It appears that in the studied case, the ampli-
fication levels evaluated using the synthetic input (red lines)
are perfectly consistent with the results obtained using the
recordings ones (green lines).
CONCLUDING REMARKS
In this study, we investigate whether the EXSIM simulations can
successfully produce the recorded ground motions from the
2016 central Italy seismic events with Mw>5:5. The general
good consistency found between synthetic and observed spectra,
suggests that the use of regional specific source scaling and
attenuation parameters in the stochastic simulations permits
to reasonable reproduce ground-motion estimates. Fourier spec-
tra are generally compatible with records, demonstrating that
our model can adequately explain spectral amplitudes and to
detect near-source effects, not considering the effects of direc-
tivity. The stochastic finite-fault model is a very practical
application for calculating the ground-motion parameters
(Atkinson and Assatourians, 2015). Furthermore, it can be easily
used both for region-specific
and path-specific applications
as well as for areas where no
or few acceleration stations
are available.
Nonetheless, an extensive
application of synthetic accel-
erograms is not feasible for
the following reasons: (1) the
elaborations require high-
computational efforts; (2) the
calibration of the input param-
eters is not easy, and it is nec-
essary to determine some
critical parameters such as stress drop, quality factor, geomet-
ric attenuation, and kappa effect; (3) scarcity of near-field
strong-motion records to compare with simulated data.
Concerning amplification effects, the synthetic accelero-
grams processed with the input parameters valid for the central
Apennines are a valid alternative to recording seismic inputs.
Results showed that synthetic spectra effectively represent the
high-frequency content of observed data at near-field areas
using the simulation parameters shown in this work. The syn-
thetic seismograms can be profitably used in microzonation
studies of urbanized centers within FDZs, in which a few records
are available at rocky sites, to predict local amplification effects.
DATA AND RESOURCES
Extended finite-fault ground-motion simulation algorithm (EXSIM)
code was downloaded from http://www.daveboore.com/software_
online.html (last accessed June 2020). Individual seismogenic sources
are located in the Database of Individual Seismogenic Source (DISS)
version 3.2.1 (v.3.2.1). Data were downloaded as ESRI shapefiles from
http://diss.rm.ingv.it/diss/ (last accessed November 2020). Peak accel-
eration values are related to the ShakeMaps in peak ground acceleration
(PGA), collected as ESRI shapefiles from http://shakemap.ingv.it/shake/
index.html (last accessed December 2020). The Global Centroid
Moment Tensor (Global CMT) Project database was searched using
https://www.globalcmt.org/CMTsearch.html (last accessed August
2020). The time domain moment tensor (TDMT) solution for the
events of 24 August, 26 October, and 30 October suggests a magnitude
of Mw6.0, 5.9, and 6.5, respectively, with normal-fault kinematics and
direction of the northwestsoutheast fault planes. This information is
available on the Istituto Nazionale di Geofisica e Vulcanologia (INGV)
website (http://cnt.rm.ingv.it/, last accessed December 2020). Location
and shear-wave velocities VS30 of the acceleration stations and the
observed ground-motion data were both downloaded from the engi-
neering strong-motion (ESM) database at https://esm.mi.ingv.it/ (last
accessed December 2020). Epicenters of the central Italy seismic
sequence 20162017 were collected from the Italian Seismological
Instrumental and Parametric Data-Base (ISIDe). Data can be found
from the ISIDe at http://terremoti.ingv.it/iside (last accessed January
2021). Slip distributions can be found in the Finite-Fault Analysis sec-
tion in the supplemental material of Chiaraluce et al. (2017). The def-
inition of the geological model of the Cortino subsoil refers to the work
Figure 8. Housners amplification factors calculated using recordings and synthetic input along the cross section
AA. Green curves represent LSR analysis with recordings input and red curves represent LSR analysis with
synthetics input. Full line denotes 0.10.5 s and dashed line denotes 0.51 s. The color version of this figure is
available only in the electronic edition.
12 Bulletin of the Seismological Society of America www.bssaonline.org Volume XX Number XX 2021
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carried out by the appointed professional relating to the microzonation
studies carried out in the municipalities of central Italy affected by the
seismic events as of 24 August 2016 as provided for in Ordinance num-
ber 24 of 12 May 2017 of the Extraordinary Commissioner; it is avail-
able at https://sisma2016data.it/microzonazione/ (last accessed January
2021). The seven observed accelerograms were selected using REXELite
(http://esm.mi.ingv.it/DYNA-stage/CadmoDriver,lastaccessed
January 2021). Two-dimensional local seismic response analysis has
been performed through the commercial code local seismic response
(LSR2D) by STACEC (http://www.stacec.com, last accessed January
2021). MATLAB software downloaded from https://www.mathworks.
com/products/matlab.html (last accessed November 2021).
DECLARATION OF COMPETING INTERESTS
The authors acknowledge that there are no conflicts of interest
recorded.
ACKNOWLEDGMENTS
The authors would like to thank the editors of BSSA, Aybige Akinci,
and two anonymous reviewers for the helpful suggestions that
improved the quality and the readability of this article. The article
shows the results of Mancinis Master thesis developed in jointed
tutorship between University G. dAnnunzioof Chieti-Pescara
(UdA) and University of Malta within the ERASMUS+ project. The
material about Cortino site has been collected during the Research
Agreement established between IGAG (Department of the Italian
National Council of Research) and UdA (Prot. n.0002282 on 27
July 2017) for central Italy microzonation studies.
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The great influence of site effects on damage patterns produced by seismic events of the past, stresses out the need to find proper relationships between the site local conditions and the potential damage that existing buildings may experience under earthquakes of different intensity. This issue proved to be relevant, especially for those masonry buildings in small historic centers, such as the ones belonging to the Apennine areas of Italy that suffered serious damage after the 2009 L'Aquila and the 2016–2017 Central Italy seismic sequences. Some of these centers showed a not regular damage distribution, suggesting that site effects influenced the seismic response of their buildings. In this perspective, this paper provides an attempt to correlate earthquake damage to site effects, focusing the attention on two historic centers of the Abruzzi region, Campotosto and Cortino, which were damaged by the 2016–2017 Central Italy seismic sequences and for which the amplification factors of the subsoil were carefully identified, resulting more influencing were higher damage was observed. First, the damage patterns observed after the 18 January 2017 seismic shock are represented in terms of Damage Probability Matrices and maps developed in GIS environment. Moreover, the data collected in terms of stats are interpreted through the application of the binomial probability distribution, in order to check its reliability in reproducing the gathered frequencies. Finally, in order to justify the damage of some specific buildings, the results of site response analyses are compared with the damage observed punctually on three damaged masonry buildings.
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Ambient vibrations spectral ratios at forty-five sites and shear wave velocity profiles obtained by inverting surface waves dispersion curves were used to assess seismic site response properties in the Floridia village located in southeastern Sicily. The results of the horizontal-to-vertical spectral ratios analysis often indicate fundamental in the frequency range 1.5-4.0 Hz. Conversely, where the bedrock outcrop, spectral ratios are flat. The calcarenites outcropping in the north-west sector of the village and the soft sediments in the south-east are characterized by shear wave velocity of about 700 m/s and ranging between 200-600 m/s, respectively. The geological substratum has instead shear wave velocity of about 1500 m/s. The achieved geophysical and geological data help to build a subsoil model of the studied area, classifying the main lithotypes above the bedrock and determining the hidden morphology. Moreover, amplification functions were computed for zones with homogenous geological and geophysical properties. Therefore, these site amplification functions were used to simulate ground motion scenarios corroborating the efficiency of the obtained results.The used strategy, involving geophysical and geological methods, representsan important step to derive ground motion scenarios and seismic risk maps. Moreover, the characterization of amplification due to local site effects could help to understand the pattern and the damage distribution of historical earthquakes