In Israel, due to low seismicity rates and a sparse seismic
network, the temporal and spatial coverage of ground motion data is
insufficient to estimate the variability in moderate–strong (M>6) ground motions required to construct a local ground motion model (GMM).
To fill this data gap and to study the ground motion variability in M>6 events, we performed a series of 3-D numerical simulations of
M 6 and M 7 earthquakes. Based on the results of the simulations, we
developed a parametric attenuation model (AM) and studied the residuals
between simulated and AM peak ground velocities (PGVs) and the single station variability. We also
compared the simulated ground motions with a global GMM in terms of PGV and significant duration (Ds 595). Our results
suggested that the AM was unable to fully capture the simulated ground
motion variability mainly due to the incorporation of super-shear rupture
and effects of local sedimentary structures. We also showed that an imported
GMM considerably deviates from simulated ground motions. This work sets the
basis for future development of a comprehensive GMM for Israel, accounting
for local source, path, and site effects.
Introduction
The recent report by the Centre for Research on the Epidemiology of
Disasters (CRED) and the UN Office for Disaster Risk Reduction (UNDRR) –
The human cost of disasters: an overview of the last 20 years (2000–2019) – clearly shows that earthquakes are
the deadliest natural disasters. Accounting for only 3 % of the total
number of people affected by natural disasters, they count for 58 % of
deaths (more than 700 000) of all disaster types and 21 % of recorded
economic losses (Mizutori and D'ebarati, 2020). Over the
past 40 years, the global population exposed to a moderate to severe
intensity earthquake has increased by 93 % (to 2.7 billion people)
(Pesaresi et al., 2017). This
value is expected to grow with population growth and increasing
urbanization.
Seismic hazard is the intrinsic natural occurrence of earthquakes and the
resulting ground motion and other effects
(Wang, 2005). Ground motion models (GMMs)
are critical components in the mitigation of seismic hazard. Empirically
based GMMs, also known as ground motion prediction equations (GMPEs), are
parametric models that estimate the median and the variability in the
expected ground motions at a site. The main explanatory variables of such
models are typically earthquake magnitude, distance, and site conditions.
New generation GMMs also address faulting style, depth to rock, and others.
Many regions worldwide, either due to low seismicity rates and/or sparse
coverage of the seismic network, do not provide sufficient temporal and
spatial data to estimate the variability in ground motions required to
construct a local GMM or validate an imported GMM to local conditions. This
situation is specifically acute in the range of strong earthquakes at
relatively short distances that pose the most significant hazard to human
life and infrastructure.
The use of imported GMMs under the ergodic assumption attributes the ground
motion variability to the randomness of the process (i.e., aleatory
variability) rather than to local systematic source, path, and site effects
(i.e., epistemic uncertainty) (Anderson and Brune, 1999).
Abrahamson et al. (2019) showed that
the increased number of strong motion records over the past decade exhibits
significant differences in scaling of the ground motions even within
relatively small regions and that most of the variability typically treated
as aleatory is actually due to systematic source, path, and site effects.
Kuehn et al. (2019) showed the
importance of variations in quality factor (Q) over small spatial scales (30 km) in California. Specifically showing that accounting for path effects
leads to a smaller value of the aleatory variability and results in
different median predictions, depending on source and site location. To
achieve this improvement, Kuehn et al. (2019) divided California into a grid with a cell size of 30 km by 30 km and used 12 039 records from 274 events recorded at 1504 stations. This
approach can be employed only in data-rich regions, such as California.
Lan et al. (2019) showed that for
southwestern China, imported GMMs result in significant discrepancies
compared with regional instrumental data (including the Wenchuan Mw 7.9
event). In addition, despite the recorded ground motion data expanding, it
remains sparse for large, complex ruptures with recurrence intervals
generally exceeding the observation length of instrumental records.
The challenges met while predicting ground motion in data-poor regions turn
numerical modeling into an essential complementary method for seismic hazard
analysis (Chaljub et al., 2010).
Numerical modeling alleviates the need for the ergodic assumption as it can
augment the seismic data with strong motion records and account for ground
motion variability by systematically separating source, path, and site
effects. For example, Graves
et al. (2011) showed that the combination of rupture directivity and basin
response effects could lead to an increased hazard in particular sites,
relative to that calculated by GMM.
Pitarka et al. (2021) found that the
combination of rupture propagation effects with the amplification due to
local topography can result in large ground motion amplifications with
complex spatial variability.
However, the shift from ergodic models to non-ergodic models, which account
for local source, site, and path effects such as numerical models, leads to
large epistemic uncertainty in the median ground motion, resulting in
increased epistemic uncertainty of the hazard
(Walling and Abrahamson, 2012). Such uncertainty is
derived from both modeling and parametric uncertainties, as the model, is
not well constrained. Model uncertainty can be reduced by using more
accurate 3-D crustal models and source models.
Subsurface models with different levels of accuracy and completeness are
available around the world. With the increasing use of terrestrial and space
geodesy, the control of seismic sources is also improving with time.
Combining the two enables the construction of numerical models for regional
assessment of ground motions
(Pitarka et al., 2021;
Douglas and Aochi, 2008; Graves and Pitarka, 2015). A hybrid GMM, based on
empirical and synthetic ground motion databases, is expected to reduce the
epistemic uncertainty of the median ground motion and will lead to a lower
aleatory variability than GMMs based on data with limited magnitude and
distance bands.
In Israel, low seismicity rates (centennial and millennial return periods)
and a limited instrumental catalog, spanning only four decades and containing
mainly M<6 events, impede the development of a local empirical GMM.
The practical outcome of this shortcoming is the use of imported GMMs, such
as that of Campbell and Bozorgnia
(2008; hereafter, CB08) used in the Israel Seismic Design Code IS
413 (Israel Standards Institution, 2013). Contrary to the
instrumental catalog, the Israel pre-instrumental catalog spans over three
millennia (Agnon, 2014), including numerous M>6 events and up
to 14 M>7 events.
This paper presents numerical modeling of ground motions in Israel and is intended
to study ground motion variability from moderate (M 6) and strong (M 7)
earthquakes. The primary purpose of this work was to study the different
source, path, and site effects of simulated M 6 and M 7 earthquakes and
their contribution to ground motion variability in Israel. To this end, we
have improved the 3-D regional velocity model of
Shimony et al. (2021) and numerically
modeled M 6 and M 7 earthquakes with different source and path properties.
Subsequently, we developed a parametric model of median ground motions and
their variability in terms of peak ground velocity (PGV). The model
quantifies the spatial distribution of the ground motions in central and
northern Israel, accounting for source, path, and site effects.
We begin with a brief introduction to the seismotectonic setting of the
region. Then, we proceed to the methodology section to describe the process
of generating a synthetic ground motion database and the subsequent
construction of a parametric ground motion model. Next, in the results
section, we present the simulated ground motions and the respective
attenuation model. Then, we show the comparison between the results of our
simulations and global GMMs of Campbell and
Bozorgnia (2014; hereafter, CB14) and Afshari
and Stewart (2016). Finally, we discuss our findings and provide insights
regarding the seismic hazard from moderate to strong earthquakes and the
importance of developing a regional GMM to mitigate the seismic hazard in
Israel.
The seismotectonic setting of IsraelSeismicity and seismic hazard in Israel
The Dead Sea Transform (DST) fault system is an active tectonic boundary
separating the African and Arabian plates. Extending from the Gulf of Aqaba
to southern Turkey, a total length of approx. 1100 km, it dominates the
seismicity of Israel, the Palestinian Authority, Lebanon, and Syria
(Fig. 1a and b).
The DST is a left-lateral strike-slip fault with a total offset of 105 km
(Garfunkel, 2014). The average
long-term slip rate is 4 to 5 mmyr-1
(Bartov et al., 1980). Geodetic
slip rates along the Israeli part of the DST range from 3 to 5 mmyr-1
(Hamiel et
al., 2016; Sadeh et al., 2012).
(a) Israel seismic catalog (Mw) for the period 1985–2021. Orange circles are events with Mw>5 (expansion of
Wetzler and Kurzon, 2016a,
catalog). Red lines are active tectonic borders and faults, DST is Dead Sea
Transform, and CFZ is Carmel Fault Zone. (b) Demographics of Israel and the
Palestinian Authority (PA) and the deployment of the Israel Seismic Network.
Yellow triangles are the old (up to October 2017) Israel Seismic Network
stations, and brown triangles are the current (TRUAA) seismic network stations
(after
Kurzon et al., 2020a). GS is
Gaza Strip. The black rectangles define the computational domain presented
in Fig. 3a.
Splaying northwest from the DST is the Gilboa Fault and farther northwest
towards the Mediterranean the Carmel Fault. Both comprise an active zone
generalized as the Carmel Fault Zone (CFZ). The DST segments are capable of
producing M 6 and M 7–7.5 events
(Shamir et al., 2001; Hamiel
et al., 2009), and the CFZ is capable of producing up to M 6.5 earthquakes
(Grünthal et al., 2009).
The Israel Seismic Network (ISN), established in 1983 and upgraded over the
years, consists of a mixture of different instrumental and operational
stations, including short-period stations (14 in total), broadband stations
(24 in total), and a large broadband array (part of the Comprehensive
Nuclear Test Ban Treaty). The deployment of the ISN does not cover areas of
increased seismic hazard, e.g., densely populated zones and soil sites, or
areas designated by the Israel Seismic Code (IS413) as being suspected of extreme
ground motion amplification, such as the Zevulun Valley (Fig. 1b).
Currently, the seismic network is upgraded within the TRUAA project (an
early warning system), with up to 69 strong motion accelerometers and 12
broadband seismometers added to ISN
(Kurzon et al., 2020a). However,
most of the instrumentation are placed along the DST and Carmel Fault to
provide early warning and not in densely populated or industrialized areas
where the seismic risk is tangible. Based on demographic projections (the
Taub Center for Social Policy Study in Israel; for URL see data and
resources) the population of Israel is expected to grow from 9.05 million in
2021 to 12.8 million in 2040, and combined with the increasing demand for
housing and infrastructures, the seismic risk is expected to grow.
The Israel seismic catalog covers 36 years of measurements (1985–2021) and
includes more than 23 300 events
(Wetzler and Kurzon, 2016a), but
only 15 of them are of M>5 (Figs. 1a and 2). Moving back in
time, Israel's pre-instrumental catalog spans over 3000 years
(Agnon, 2014; Zohar, 2019)
with many catastrophic events, such as the 749 (M>7), 1202, (M>7.5), 1759 (M>7), and the 1837 (M>7)
earthquakes, among others. In total, 14 M>7 events were
cataloged by Ambraseys (2006) in the past two
millennia. Recent geodetic studies
(Hamiel et
al., 2016; Sadeh et al., 2012) identified a slip deficit on specific
segments of the DST, such as the Jordan Gorge Fault (JGF) and the Jordan
Valley Fault (JVF), equivalent to an M>7 earthquake.
Israel's ground motion database (blue circles) for the
period 1983–2021 as a function of epicentral distance
(Yagoda-Biran et al., 2021a). The shaded rectangle
spans the Mw>6 region of moderate–strong ground motion records.
The red circles are the simulated ground motions from this work.
Spatial heterogeneity of Israel
The geological structure of Israel exhibits strong spatial heterogeneity
over short scales (Fig. 3a and b). Deep pull-apart basins (up to 10 km) filled
with soft sediments (Vs∼600–800 ms-1) accompany the
active DST system, from south to north: the Dead Sea Basin, Beit Shean
Valley (BSV), the Sea of Galilee (SG), and the Hula Valley
(Rosenthal et al., 2019). Along the CFZ,
the Zevulun, Harod, and Jezreel valleys are formed. The vulnerability of
Zevulun Valley is particularly crucial because of its dense population and
the high concentration of strategic industrial infrastructure
(Shani-Kadmiel et al., 2020).
(a) The DST fault system and the Carmel Fault Zone (CFZ)
and accompanying structures. Sedimentary structures (yellow): BSV – Beit Shean
Valley, ZV – Zevulun Valley, JV – Jezreel Valley, HV – Hula Valley,
SG – Sea of
Galilee, and the sedimentary wedge; and hard rock structures (purple):
K – Korazim structural saddle, BB – Belvoir basalts, and GB – Golan basalts. The
yellow stars indicate the epicenter of the seismic sources simulated in our
work: Jordan Gorge Fault (JGF), with bilateral and unilateral slip
realization, Jordan Valley Fault (JVF), Jericho Fault, Shemona Fault (only
for M 7), and CFZ (only for M 6). (b) Representative depth velocity profiles
of the computational domain (green circles).
The Israeli coastal plain, one of the most densely populated regions of the
country (on average, 9000 people per square kilometer), is underlain by a westward-thickening sedimentary wedge (SW). In the Judea foothills area, east of the
SW, a strong reflector exists between the sandstones and clays (Pleistocene
Kurkar group, Vs∼300ms-1) and the hard carbonate rocks
(the Cretaceous Judea group, Vs∼2000ms-1). In the
coastal plain, the Kurkar group overlays the soft carbonates (Avedat group, Vs∼900ms-1) and clastic sediments (the Bet Guvrin Formation,
Vs∼800ms-1) (refer to Fig. 3b). The depth of the
Kurkar group base reflector is typically several tens of meters. Further to
the west, a prominent reflector is a contact between the clays (Pliocene
Yafo Formation, Vs∼600ms-1) and top of the Judea group
(Gvirtzman et al., 2008). These two reflectors,
when shallower than 250 m, were used for the latest update of the Israel
Building Code IS 413 (Israel Standards Institution, 2013) to
delineate areas of a high potential of ground motion amplification
(Gvitzman and Zaslavsky, 2009). This situation further
complicates the process of developing an empirical GMM for Israel.
Source effects
The impact of inter-basin sources along the DST on regional ground motions
was examined by Shimony et al. (2021). This work clearly showed that regional ground motions are determined
by source–path coupling effects in the strike-slip basins before waves
propagate into the surrounding areas. Ground motions are determined by the
location of the rupture nucleation, the near-rupture lithology, and the
local structures. Shimony et al. (2021) focused on symmetric sub-shear ruptures
and did not model rupture directivity or super-shear rupture velocities,
both known to amplify regional ground motions.
Under specific conditions, super-shear ruptures and directivity occur on
bi-material faults (Shi and Ben-Zion, 2006). Specifically, for subsonic
propagation, symmetrically initiated bilateral rupture evolves after some
propagation distance to a unilateral rupture in the positive direction,
which is the direction of slip on the compliant side of the fault containing
the softer layer. The magnitude of this effect increases with propagation
velocity and the degree of material contrast across the fault. At
super-shear propagation speeds, along a bi-material fault, the propagation
direction is reversed.
The DST is a mature left-lateral fault with a 105 km offset, resulting in
a strong material contrast between the hard layers on the Jordan side (east)
and the soft layers on the Israeli side (west). Thus, the rupture can
potentially propagate unilaterally southwards, discharging most of the
seismic energy into Israel or northward in super-shear mode. The Jordan
Gorge Fault and the Jordan Valley Fault (both active faults of the DST)
specifically can produce an earthquake with rupture propagating in
super-shear velocity since they border deep sedimentary basins,
characterized by a large shear wave velocity contrast along the rupture
propagation path. Thus, to quantify the seismic hazard ensuing from
bi-material faults, it is necessary to study the two propagation directions: both sub-shear and super-shear velocities.
Methodology and workflow
Developing a regional GMM for Israel requires a database of ground motion
records, including M>6 events at short, <100 km,
distances. To supplement the existing ground motion database, we added a
suite of synthetic ground motions from physics-based 3-D numerical models of
different M 6 and M 7 earthquakes (Fig. 2).
Our work comprised two main stages; first, we modified and expanded the
regional velocity model of Shimony et al. (2021) to represent a more realistic geological setting and contain
the Golan basalts, the central part of Israel, and the sedimentary wedge.
Then, we simulated five different earthquake scenarios for each magnitude,
with nucleation at different locations along the DST and CFZ. For each
scenario, we recorded synthetic ground motions at 129 stations (see
Fig. S1 in the Supplement), with 124 stations deployed in a uniform
grid with 10 km spacing and 5 more stations in areas of interest (such as
Zevulun Valley and Kiryat Shemona, among others). Next, we performed
statistical analysis of the synthetic database, using multivariable
regression, by minimizing residuals between data and model estimations. We
then formulated a parametric model of the ground motions and examined the
median ground motions and their variability for each of the simulated
scenarios.
Numerical model
Ground motions in this research were modeled using the SW4v2 software
(Petersson and Sjogreen, 2014, 2017a, b),
developed for large-scale simulations of seismic wave propagation on
parallel computers.
The velocity model covers the northern and central part of Israel (Fig. 4a)
and includes the main DST trough and the following basins/structures, from
south to north: Beit Shean Valley (BSV), Belvoir basalts (BB), Sea of
Galilee (SG), Korazim structural saddle (K), Golan basalts (GB), and Hula
Valley (HV). Along the CFZ, we model the major sedimentary basins of Jezreel
Valley (JV) and Zevulun Valley (ZV). The coastal plain is underlain by the
westward-thickening sedimentary wedge (SW). Geographically, the model
extends from the city of Ashdod in the south (31.8∘ N,
34.6∘ E) to the Hula Valley in the north
(33.23∘ N, 35.72∘ E) and from the
Mediterranean Sea in the west to the Golan basalts in the east. Figure 4b–d illustrate the north–south and east–west cross-sections of the
velocity profiles. The numerical domain spans 159 km in the north–south
direction and 124 km in the east–west direction. It covers almost 80 % of
the Israeli population and a significant part of the population of the
Palestinian Authority.
(a) The numerical model of the computational domain
accompanied with subsurface cross-sections, marked with red dashed lines:
(b) east–west cross-section through Zevulun Valley, C–C', (c) east–west
cross-section through the sedimentary wedge, B–B', and (d) north–south
cross-section through the DST trough, A–A'.
Subsurface geometry and the characteristics of the DST trough were obtained
from Rosenthal et al. (2019), with
modifications for the Hula Valley, obtained from the density log of the
Notera 3 borehole (Rybakov et al., 2003). The sedimentary
wedge structure retrieved from Gvirtzman et al.
(2008) and the Zevulun Valley structure was set using data from
Gvirtzman et al. (2011). The basement depth along the
model is based on Ben-Avraham et al. (2002). Five physical quantities describe the viscoelastic material
model used in this research: shear wave velocity (Vs), pressure wave
velocity (Vp), density (ρ), and seismic quality factors (Qs, Qp) for
each point in the computational space. The missing parameters were assessed
indirectly by using the correlation presented by
Brocher (2008). The main units with their
respective velocity, density, and quality factors are shown in Table 1.
Material properties of the main stratigraphic units used in this work.
Model partRock formationVs [ms-1]Vp [ms-1]QsQpρ [kgm-3]RegionalCrystalline basement355060004038062720Cenozoic and Mesozoic sediments (Judea/Talme Yafe,200037001603202350Mount Scopus Avedat, and Lower Saqiye)Local variations DSTCenozoic sediments (Umm Sabune, Bira, and Gesher)8872380621242054Miocene volcanics (lower basalt)36986330439.58792790Pliocene volcanics (upper basalt)294749002825642520Notera/Lisan608200039.8779.741900HulaCenozoic sediments15003100111.52232245Notera/Lisan608200039.8779.741900JVCenozoic sediments (Umm Sabune, Bira, and Gesher)8872380621242054Miocene volcanics (lower basalt)36986330439.58792790Cenozoic sediments15003100111.52232245ZVCenozoic and Senonian sediments (Mount Scopus Avedat8872380621242054and Beit Guvrin)Cenozoic sediments (Patish)15003100111.52232245Cenozoic sediments (Kurkar and Yafo)608200039.8779.741900SWCenozoic sediments (Lower Saqiye)8872380621242054Cenozoic sediments (Kurkar and Upper Saqiye)608200039.8779.741900
Seismic sources were modeled using the distributed slip model (DSM)
developed by Shani-Kadmiel et al. (2016). DSM is a kinematic model which describes the rupture patch as an
elliptic surface with maximum slip at the nucleation point, decaying toward
the edges as a pseudo-Gaussian function (Fig. S2 in the Supplement).
Shani-Kadmiel et al. (2016) present
validation of the DSM using macroseismic reports of the 1927 Jericho
earthquake, showing good agreement between the reported and simulated ground
motions. Rupture patch size and displacements were scaled following the
relations presented in Wells and Coppersmith (1994).
All sources were modeled as left-lateral, vertical strike slips (a dip of
90∘ and rake of
0∘), with a strike of
3∘ for sources on the DST and a strike of
325∘ for the CFZ. The moment rate time
function of each point on the rupture patch was set to a GaussianInt pulse
(Petersson and Sjogreen, 2017b) with a central frequency of
f0=0.4 Hz and a maximum frequency of fmax=1 Hz.
The depth of the model was set to 28 km corresponding to the maximum
seismogenic depth in this region
(Wetzler and Kurzon, 2016a). We
assigned a minimum shear wave velocity of 608 ms-1 for the uppermost
sedimentary layer due to the computational limitations of our system. Grid
spacing was set to 76 m in accordance with the minimum shear wave velocity
and the maximum frequency of the source. We set the simulation time to 120 s to allow the slowest waves to propagate across the entire
computational domain. The main parameters of the numerical setting are
summarized in Table 2.
Main parameters of the numerical model.
ParametersValueModel dimensions (L×W×D)159.63km×124.45km×28kmSpatial spacing (dh)76 mGrid size (points)1.27×109Time step spacing0.0125 sSimulated time120 sSource dimensions (L×D)M 6: 32km×15kmM 7: 38km×22kmSource maximum and average slipM 6: 0.5 and 0.2 mM 7: 3 and 1.57 mSeismic moment (M0)M 6: 2.57×1018Nm (Mw 6.21)M 7: 2.37×1019Nm (Mw 6.85)Source fundamental (f0) and0.4 andmaximal frequencies (fmax)1 HzEarthquake scenarios and database
To examine the variability in ground motions from moderate M 6 and strong M
7 earthquakes, we concentrated on earthquake events nucleating on active
segments of the DST system, with known slip deficit, and along the CFZ. We
modeled a symmetric bilateral rupture on the Jordan Gorge Fault (JGF-B),
Jericho Fault (JF), Carmel Fault Zone (CFZ), and the Shemona Fault (SF), a
southward unilateral rupture on the JGF (JGF-U), and a super-shear rupture
on the Jordan Valley Fault (JVF) (Fig. 3).
The hypocenter for the DST events was placed in the middle of the
seismogenic depth: 11 and 13 km for the M 6 and M 7 models, respectively; for the
M 6 CFZ, the value was set to 12 km. The rupture patch was designed to be
contained in uniform lithology to prevent super-shear rupture speeds in the
shallow parts of our model. Therefore, rupture speed for each scenario was
set to 0.9 VS of the lithology surrounding the nucleation zone. The
only exception was the JVF scenario for both M 6 and M 7, in which we
modeled super-shear effects. For this scenario, the rupture nucleates within
the hard rock with a sub-shear speed of 1800 ms-1 and evolves into a
super-shear rupture when it ruptures the sediments with a shear wave velocity
of <900ms-1. The rupture velocity of each scenario
corresponds to the local variations in the sediment's depth. Following the
transition of the nucleation zone from the shallow crystalline basement in
the south and west parts of the model to the thick Mesozoic and Cenozoic
sediments in the north and the east, the rupture velocity decreases from
3195 ms-1 along the Shemona, Carmel, and Jericho faults to 1800 ms-1 along the JGF and JVF faults. As a reference, we simulated a
simple two-layered reference model (Ref) on the JGF, with mechanical
properties similar to the regional setting, following
Aldersons et al. (2003). The scenarios
are summarized in Table 3.
Earthquake scenarios.
Fault nameScenarioMagnitudeRupture speedHypocentral depth(M)(ms-1)(km)Jordan GorgeBilateral rupture (JGF-B)6, 7180011 and 13Jordan GorgeSouthward unilateral rupture (JGF-U)6, 7180011 and 13Jordan ValleyBilateral super-shear rupture (JVF)6, 7180011 and 13JerichoBilateral rupture (JF)6, 7319511 and 13ShemonaBilateral rupture (SF)7319513CarmelBilateral rupture (CFZ)6319512ReferenceBilateral rupture (Ref)6, 7319511 and 13Results
In this section, we report the simulation results and the simulation-based
attenuation model for M 6 and M 7. We begin with elaborating on the
regression process and its deliverable, the attenuation model. Next, we
present the ground motion variability, starting from total and following
with within-event and between-event PGV residuals, as well as the contribution of
each earthquake scenario to the total deviation. Then, we proceed with
looking into single station variability, through maps of the predicted and
simulated PGV, with the corresponding residuals at each station. Finally, we
show the PGV and the 5 %–95 % ground motion significant duration
(Ds 595) correspondence between those predicted by global GMMs
(CB14; Afshari and
Stewart, 2016) and those simulated.
Simulation results
For each simulation, we attained a set of 129 synthetic ground motion
records (three components each; north–south, east–west, and vertical) from the network
deployed in the computational domain. Next, we calculated the PGV values for
each scenario at each station as the maximum value over the three
components. We decided to exclude some of the M 7 near-source records
(stations 104, 105, and 106 for the JVF scenario and stations 122, 123, and
129 for the JGF-B, JGF-U, and Shemona scenarios) due to high strain values
and possible nonlinear effects not compatible with the linearity
assumption of our model. In total, our ground motion database consists of
645 and 633 synthetic records for M 6 and M 7 models, respectively. Figure 5
presents our results in terms of PGV as a function of distance. We used
different markers for records from the sedimentary structures of the Zevulun
Valley and the sedimentary wedge to differentiate them from the remaining
data.
Simulation results and PGV distance space for bilateral
rupture on the Jordan Gorge Fault (JGF-B), Jericho Fault (JF), Carmel Fault
Zone (CFZ; for M 6), and the Shemona Fault (SF; for M 7), a southward
unilateral rupture on the JGF (JGF-U), and a super-shear rupture on the
Jordan Valley Fault (JVF) for M 6 (a) and M 7 (b). The records from
Zevulun Valley and the sedimentary wedge (SW) are marked with triangles and
rectangles, respectively. The other records are marked with circles; the
reference records are marked with pluses. For comparison, the CB14 is
plotted for a strike-slip fault, Z2.5=0.42 km and Vs30=1686ms-1 (representing averaged values over all the sites).
Statistical analysis of ground motion results
The next step was to formulate a parametric ground motion attenuation model
(AM) for the two magnitudes based on our simulations. Such a model will
provide an estimate for the median ground motions and their variability. The
general parametric form of the AM for both M 6 and M 7 is based on the CB14
function and presented in Eq. (1):
lnY=alnRRUP2+b+clnVs,surfVs,ref+dZ2+e±σ,
where Y is ground motion intensity measure (IM). Due to the bandwidth of our
numerical models (0.1 to 1 Hz), we formulated the AM in terms of PGV. We
used the closest distance to the fault rupture plane (RRUP as defined
in CB14) as the initial explanatory variable. To improve the accuracy of the
model, we incorporated two additional variables into the regressions:
surface shear wave velocity at the site (VS,surf) and the depth to
VS=2kms-1 (Z2), which is the depth to the hard
Mesozoic sediments (top Judea group) considered the primary reflector in the
region. Model coefficients are denoted a, b, c, d, and e, and σ is the
standard deviation. The VS,ref is the shear wave velocity
corresponding to the Judea group in the computational domain, which in our
model equals 2000 ms-1.
The process of minimizing the residuals as a function of each explanatory
variable can be found in the Supplement (Fig. S3 in the Supplement). We used
VS,surf instead of the more common VS30 as our grid resolution
is 76 m, preventing us from accurately determining the time-averaged shear
wave velocity in the top 30 m of each site in our model. The coefficients
and the total standard deviation for each model are summarized in Table 4.
Regression coefficients for the attenuation model (AM).
We then examined the simulated data and the contribution of each scenario to
the AM variability. We calculated the within-event (δW) and
between-event (δB) residuals (see Al Atik et al., 2010) for each magnitude and distance:
2δWi,j=lnPGVi,jsim-lnPGVim,3δBi=lnPGVim-lnPGVAM,
where PGVi,jsim is the simulation value for event i
and recording j, PGVim is the median for event i, and PGVAM is the AM median value. The total residual is the
sum of the within- and between-event residuals.
The residuals are presented in Fig. 6: total (Fig. 6a and b), within-event
(Fig. 6c and d), and between-events (Fig. 6e and f). The total residuals
(Fig. 6a and b) show a large underprediction of the PGV from the JVF
scenario (orange) on which we modeled a super-shear rupture, up to a ratio
of 2.5 and 2 in the Zevulun Valley (orange triangles), for M 6 and M 7,
respectively. However, the AM also exhibits overpredictions: the PGVs from
the scenarios nucleated in the crystalline basement (SF, JF, and CFZ), with
rupture speed=3195ms-1, are overpredicted down to a ratio of more
than -1 (in natural logarithm, ln, units).
Residuals between simulated and attenuation model (AM)
PGVs as a function of rupture distance (RRUP) for bilateral rupture on
the Jordan Gorge Fault (JGF-B), Jericho Fault (JF), Carmel Fault Zone (CFZ;
for M 6), and the Shemona Fault (SF; for M 7), a southward unilateral rupture
on the JGF (JGF-U), and a super-shear rupture on the Jordan Valley Fault
(JVF) for M 6 (left) and M 7 (right): (a, b) total
residuals, (c, d) within-event (δW) residuals, and (e, f) between-event (δB) residuals. The records from Zevulun Valley and the sedimentary wedge
(SW) are marked with triangles and rectangles, respectively. The other
records are marked with circles. Residuals are in ln units.
Some within-event residuals exhibit distance dependency; for M 7, the JVF
(super-shear) and JGF-U (directivity model) residuals increase with rupture
distances greater than 20 km. The JVF residuals also demonstrate the same
distance dependency for M 6; however, the effect is less prominent when
compared to M 7.
The effect of the rupture directivity (JGF-U) is demonstrated by comparing
the Zevulun Valley and the sedimentary wedge within-event residuals (Fig. 6c and d). While in a symmetric rupture (JGF-B) the seismic energy dissipates
equally into the northern and southern parts of the model, in an asymmetric
rupture (JGF-U), more energy propagates toward the south, resulting in
stronger ground motions at the sedimentary wedge (Fig. 5). However, the
ground motions are less intensive at the Zevulun Valley compared to the
symmetric rupture. As a result, the within-event residuals for Zevulun
Valley are higher for the JGF-B scenario compared to the JGF-U scenario,
while for the sedimentary wedge, the opposite is true. Most clearly, the JVF
between-event residuals are the highest for both M 6 and M 7 with a ratio of
1 (Fig. 6e and f).
We further studied the single station variation of ground motions and
quantified the misfit between the simulated PGV and the AM PGV. We
calculated the mean ground motion and its standard deviation at each
station. The residuals for single station k were calculated as follows:
δk=lnPGVksim-lnPGVkAM,
where PGVksim and PGVkAM are the simulated
and predicted mean PGV at station k, respectively. Figures 7 and 8
show the mean simulated and mean AM PGVs for M 6 and M 7, respectively. For
each station, we also plotted the standard deviation using a scaled diameter
circle.
Map view of simulated and AM mean PGVs (triangles) for M 6
and their standard deviation (diameter of the circles) at each station, with
the respective residuals in ln units (inverted triangles).
Map view of simulated and AM mean PGVs (triangles) for M 7
and their standard deviation (diameters of the circles) at each station,
with the respective residuals in ln units (inverted triangles).
Both figures show that simulated ground motion variability at a single
station is large and not fully covered by the AM. For example, simulated ground
motions at station 129 located on the Hula Valley exhibit a significant
standard deviation. For M 6, it is the largest value (green triangle) of
0.17 ms-1 compared to 0.09 ms-1 (indigo) predicted by the AM,
while for M 7, the largest standard deviation is 0.59 ms-1 (orange
triangle) compared to 0.02 ms-1 (light green triangle) observed at
station 127 located on the Zevulun Valley. As a result, there is a large
discrepancy between the simulated and AM values at specific stations.
In general, as expected from normal log distribution, higher mean PGV values
are accompanied by a larger standard deviation for both magnitudes. It is of
significance for seismic hazard assessment as outlier ground motions at
specific sites, mainly from M<7 earthquakes, could be a
significant source of damage (Minson et al.,
2020)
Comparison with global models
To examine the agreement between our simulations and an instrumental,
global GMM, we calculated the total residuals between PGVs from our
simulations and PGVs predicted by the CB14 model. We chose the CB14 model as
it is planned to supersede the CB08 model used in the Israel Building Code
(413). The CB14 PGVs were calculated for a strike-slip fault, for which we used
the surface shear wave velocity as the Vs30 parameter and the basin
response term Z2 as Z2.5. Figure 9 shows the total residuals for
the AM and CB14 models as a function of distance (RRUP). For both
magnitudes, the AM (mean and standard deviation) oscillates near the
zero-model bias (dotted horizontal black line). However, it deviates when
approaching the region containing rupture distances typical of the Zevulun
Valley. The effect is more noticeable for M 7. Figure 9 also shows that the
CB14 is less consistent and performs differently for each magnitude. While
for M 6 the GMM mostly overpredicts (negative values) the simulated PGV
(until reaching ZV and SW rupture distances zones), for M 7 it mostly underpredicts them (positive values), except for large distances, by up to a factor
of 2 and above. In addition, the residuals calculated with respect to CB14
exhibit a significant standard deviation of the mean ground motion, with
considerably larger variability for M 7.
PGV residuals between those simulated (Sim) and predicted by
the AM (blue) and CB14 (red) models as a function of rupture distance
(RRUP) for M 6 (a) and M 7 (b). Thick lines represent the mean,
and the shaded region denotes the standard deviation at each distance. The
green and yellow shaded regions indicate the range of rupture distances
related to the sedimentary wedge (SW) and the Zevulun Valley (ZV),
respectively. Residuals are in ln units.
It is important to note that, by averaging the PGVs, we subdue the
performance of both models at individual stations/rupture distances; thus,
we cannot analyze the residual's spatial variations at a specific location.
However, it is sufficient to demonstrate that the global model deviates
considerably from simulated ground motions.
Significant duration
Another important intensity measure is the significant duration (Ds 595),
the time interval between 5 % to 95 % of the cumulative seismic energy
(Arias Intensity) at a site. Figure 10 shows the simulated and empirical Ds 595 values as a function of rupture distance. The typical increase in the
empirical model with distance is captured in the reference (laterally
homogenous) model. However, for all other models, the significant duration
remains nearly constant at rupture distances larger than 20 km. In
addition, the empirical GMM mostly underpredicts the simulated values
between 2 and 50 km for both magnitudes.
The 5 % to 95 % ground motion significant duration
(Ds 595) comparison between simulated and empirical GMMs
(Afshari and Stewart, 2016) for bilateral
rupture on the Jordan Gorge Fault (JGF-B), Jericho Fault (JF), Carmel Fault
Zone (CFZ; for M 6), and Shemona Fault (SF; for M 7), a southward
unilateral rupture on the JGF (JGF-U), and a super-shear rupture on the
Jordan Valley Fault (JVF) for M 6 (a) and M 7 (b). Main plots (left)
accompanied with subplots showing only the records from the Zevulun Valley
and the sedimentary wedge (right). Solid and dashed lines represent the
median and the standard deviation of the empirical GMM, respectively. The
records from Zevulun Valley and the sedimentary wedge (SW) are marked with
triangles and rectangles, respectively. The other records are marked with
circles.
We postulate that this is caused by the complex geological setting of our
model. The impact of geological complexity is reflected in Ds 595 values
from near-source stations, Zevulun Valley (triangles), and the sedimentary
wedge (rectangles). At near-source stations, the significant duration is
large due to the effects of deep sedimentary structures along the DST, which
also prolongs the path duration of the ground motions in other sites
(Shimony et al., 2021), resulting in long
significant duration with almost no path dependency. On the contrary at the
Zevulun Valley and the SW, the energy accumulates faster than in other
sites as the ground motions are amplified, reaching 95 % of the total
energy over a shorter duration. Interestingly, the significant duration in
Zevulun Valley is lower than in the sedimentary wedge. As we expect
deep sedimentary structures to prolong shaking duration, it may sound
counterintuitive. However, it is explained by the relative proximity of the
Zevulun Valley to the rupture. Whereas in Zevulun Valley, most of the energy
arrives as a pulse at the beginning of the record, the energy at the more
distant sedimentary wedge accumulates more gradually and reaches its maximum
almost at the end of the record, resulting in longer Ds 595 values. In
general, there is no large deviation between the simulated significant
duration for M 6 and M 7. However, the empirical model shows a longer
duration for M 7. This resemblance in source duration is related to the DSM
settings and more specifically to the source fundamental frequency, which in
our study is the same for both magnitudes and is a subject for testing
in future works.
Discussion and summary
A strong earthquake in Israel is imminent. However, up to date, a
comprehensive regional GMM describing the spatial variability in ground
motions has not yet been developed. This is mainly due to low seismicity
rates and a magnitude-bounded strong motion database coupled with sparse
instrumental coverage. The current ground motion database lacks events with
magnitude M>6. To examine different source and path effects on
ground motion variability, we simulated M 6 and M 7 earthquakes with
different source and path properties. Subsequently, to study the ground
motion variability, we developed a parametric attenuation model (AM) of PGV
for M 6 and M 7 earthquakes, based on RRUP, Z2, and VS,surf explanatory values.
Our analysis showed that the AM was unable to fully capture the variability
in the simulated ground motions. Except for the Jordan Valley Fault (JVF)
scenarios, the AM overestimates most of the modeled ground motions. We
postulate that this overestimation results from the outlier, higher PGV
values from the JVF scenario (Fig. 5), shifting the average ground motion
toward them. Also, the within-event residuals for the JVF scenario show a
distance dependency for RRUP>20 km, continuing to grow
away from the fault. We describe this scenario as a “black swan” of our
simulations and account its outlier behavior to the effects of the
super-shear rupture, specific to this model, affecting both the source
(between-event residuals) and path (within-event residuals) terms of the
ground motions (Fig. 6). Super-shear ruptures behave differently from
sub-shear ruptures in many aspects. Most pertinent to our analysis is the
slow energy decay of the super-shears relative to sub-shears
(Bhat et al., 2007); thus, it cannot be
fully captured by our AM, which is based mainly on sub-shear ruptures. In
addition, it was found that Z2, depth to Mesozoic rock, has a very
small impact (<0.001) on the standard deviation for the M 6 model,
reducing it from 0.5998 to 0.5988 (Fig. S3). As a result, the M 6 model
depends only on rupture distance and VS,surf. For M 7, Z2 is
a good predictor for soil sites (Z2>0) located
>58 km from the source, including the Zevulun Valley and the
sedimentary wedge (Fig. 6d), imposing a great seismic hazard. We do not see
a clear dependence of the deep sedimentary structures with Z2 along
the DST. We speculate that the site response may be masked by nearby
source effects, and this requires additional analysis.
For each scenario, both magnitudes considered, we observed high PGV values
at the Zevulun Valley and the sedimentary wedge associated with local site
effects. These sedimentary structures exhibit a larger discrepancy between
the simulated and AM PGV values when compared with other sites. Such
deviation indicates that the AM does not fully capture the site effects of
these complex structures, and future model refinements are required.
Likewise, the single station variability shows that the simulated values'
highest mean and standard deviation were in Zevulun Valley and at near-source
stations. In addition, a relatively high standard deviation was also found
in the sedimentary wedge for M 7. This large single station variability is,
apparently, the impact of the outlier JVF PGV values. The AM does not
account for the standard deviation at near-source and Zevulun Valley
stations for the M 6 model and almost at all stations for the M 7. In fact, as the
AM was unable to capture the simulated JVF PGV values, it is expected that
the single station variability cannot be captured either. Furthermore, the
larger discrepancy for M 7 is due to the larger deviation of the JVF's ground
motions from those of sub-shear ruptures (Fig. 5), on which the AM is mainly
based.
Noteworthy to mention is that while the effect of the super-shear rupture on
the AM performance is systematic over the entire computational domain,
comprised of both source and path effects (Fig. 6), the effect of the
southward directivity is a distance-dependent path effect, increasing towards
the south and related to a larger amount of energy discharged in this
direction. Additional records of super-shear and directivity ruptures and
accounting for these source effects by additional model terms will improve
the performance of the AM and will assist in better understanding the
implications of these phenomena on the seismic hazard in Israel.
The comparison of the simulated ground motions with a global GMM (CB14)
showed that this model is not well constrained for the simulated ground
motions and does not capture their total variability. We note that the
comparison was performed on a single IM, the PGV values, one of several
intensity measures provided by the CB14. Thus, our findings are pertinent to
the variability in PGV solely. It should be noted that PGV is a good proxy
for structural damage (e.g., to
Kaestli and Fäh,
2006; Wald et al., 1999) and hence a crucial parameter for seismic hazard
mitigation. This discrepancy between modeled PGV and CB14 PGVs will
inevitably result in a discrepancy in the evaluation of structural damage.
The significant duration (Ds 595) comparison showed again that the imported
model performs differently than the simulated ground motion and cannot
explain the local variability due to complex geological structure, affecting
the source, path, and site terms of the ground motions, such as the path
independence of the significant duration. However, we note that the Ds 595 values
from our simulations were calculated based on low-frequency content
(<1 Hz) and may be biased from Ds 595 calculated based on the
complete spectrum comprised of both low and high frequencies. The effects of
the frequency content on significant duration may be a potential topic for
research in future works.
Regional simulations of near-fault ground motions from large Mw 7
earthquakes in Lebanon, based on a 1-D velocity approximation, were
presented by Fayjaloun et al. (2021). A comparison between the results reported by
Fayjaloun et al. (2021) with our
results is somewhat limited. Specifically, it was shown that structural and
material heterogeneity of the crust in Israel results in regional ground
motion variability
(Volk
et al., 2017; Shani-Kadmiel et al., 2020; Shimony et al., 2021). These
effects could only be captured by 3-D modeling.
We acknowledge that our AM is not independent of the evaluated models, thus
describing both their explanatory and predictive power
(Mak et al., 2017). However, our goal was not to
develop an independent and comprehensive GMM but to study the ground motion
variability through a parametric model.
Recently, Maiti et al. (2021) developed a suite
of nine GMMs for Israel in the magnitude range of 3 to 8 and distance range
of 1 to 300 km. These models are formulated in Fourier amplitude spectra
(FAS) and are based on one empirical and four simulated ground motion
datasets and two empirical host models. The simulated ground motions were
generated using the stochastic method simulation (SMSIM) model of
Boore (2003), with a unique set of parameters for
each simulation, calibrated with the empirical ground motion dataset
(discussed in detail in Yagoda-Biran et al., 2021a). However, the GMMs do not fully account for local source, path,
and site effects due to a sparse empirical database at large magnitudes (M>6) and the utilization of a point-source stochastic simulation
method. This method is useful for simulating mean ground motions. Yet, it is
less appropriate for simulating site-specific and earthquake-specific ground
motions and low-frequency ground motions, which are affected by the 3-D
geometry of the computational domain. The AM presented in this work is based
on 3-D simulations and incorporates a finite fault source with different
rupture properties. This is the first step toward developing a regional GMM
accounting for local source, path, and site effects. In subsequent work,
which is beyond the scope of the current research, we intend to develop a
complete GMM for Israel, which will include all the magnitudes and will be
based on empirical (M<6) as well as on synthetic (M>6) databases. In addition, we plan to incorporate new path and site terms
such as Z0.8 for the Zevulun Valley and the sedimentary wedge and
distance-dependent and rupture-velocity-dependent attenuation for
directivity and super-shear ruptures, among others, as well as a source term
for super-shear ruptures. Such a model is expected to perform better than
imported global models by maintaining both a lower aleatory variability
and, as new synthetic data will be added to the database, reduced epistemic
uncertainty of the median ground motions
(Abrahamson et al., 2019).
The population of Israel is fast-growing, with an annual rate of 1.8 %
(OECD 2020 data), compared with the 0.4 % average of the Organisation for Economic Co-operation and Development (OECD). Coupled
with fast economic growth of 4.5 % (OECD 2019 data), the demand for
housing and infrastructure constantly elevates the seismic risk in Israel.
Our work shows that the ground motions in Israel from M 6 and M 7
earthquakes are expected to be very damaging, up to 8–9 EMS (Fig. S4 in the Supplement).
Furthermore, the modeled ground motions exhibit considerable spatial
variability which imported GMMs do not fully capture. The development of a
local comprehensive GMM model is therefore critical for the mitigation of
seismic risk. In the foreseeable future, the moderate–strong ground motion data
gap will be filled by synthetic ground motion records from systematic
numerical simulations.
Data availability
Israel seismic catalog (Fig. 1a), expanded based on the
Wetzler and Kurzon (2016a)
catalog and the configuration of the Israel Seismic Network (Fig. 1b) based on
Kurzon et al. (2020a), can be
found at https://earthquake.co.il/en/earthquake/searchEQS.php (Wetzler and Kurzon, 2016b) and
https://earthquake.co.il/en/network/accNetwork.php (Kurzon et al., 2020b), respectively. The ground
motion database of Israel (Fig. 2) discussed in
Yagoda-Biran et al. (2021a) is available at
https://earthquake.co.il/en/hazards/EngSeismology.php (Yagoda-Biran et al., 2021b). The Taub Center
population projections for Israel are accessible at
https://www.taubcenter.org.il/en/pr/population-projections-for-israel-2017-2040/ (Weinreb, 2021).
OECD population and economic growth rates can be found at
https://data.oecd.org/israel.htm#profile-economy (The Organisation for Economic Co-operation and Development, 2021).
Simulations were
performed using SW4 version 2.0 (v2.0; Petersson and Sjögreen, 2017a),
an open-source package for wave propagation simulations, available at
https://github.com/geodynamics/sw4 (Petersson and Sjogreen, 2021). Data processing was
done with the pySW4 package from Shahar Shani-Kadmiel, available at
https://github.com/shaharkadmiel/pySW4 (Shani-Kadmiel, 2021),
and
“obspy” (Beyreuther et al., 2010), developed for
numerical seismology. Figures were prepared with Matplotlib
(Hunter, 2007) and Cartopy (Met Office, 2016).
Peak ground velocity (PGV) values, according to Campbell and Bozorgnia
(2014), were calculated using the Next Generation Attenuation West Project
(NGA-West2) ground motion prediction equations (GMPEs) Excel file, available
at https://apps.peer.berkeley.edu/ngawest2/databases/ (Seyhan, 2021).
The Supplement includes the following: (1) synthetic station network
deployed in our models (Fig. S1), (2) distributed slip model (DSM) slip
distribution and rupture time (Fig. S2), (3) the evolution of the residuals
between simulated and attenuation model (AM) PGV for M 6 and M 7 (Fig. S3),
and (4) map view of simulated mean EMS intensity calculated according to
Kaestli and Fäh (2006).
The supplement related to this article is available online at: https://doi.org/10.5194/nhess-22-1451-2022-supplement.
Author contributions
The authors confirm the contribution to the paper as follows: study conception: JG and MT; numerical simulations and data processing: JG; design (figures): JG and MT; interpretation of results: JG and MT; and draft manuscript preparation: JG and MT.
Competing interests
The contact author has declared that neither they nor their co-author has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Earthquake-induced hazards: ground motion amplification and ground failures”. It is not associated with a conference.
Acknowledgements
This research was partially funded by the Ministry of Energy, Israel (grant
number 219-17-02). Co-author Jonatan Glehman was partially supported by the Ministry of
Energy scholarship for graduate studies (tender 76/19). We would like to thank Ronnie Kamai for her assistance in formulating the AM.
Financial support
This research has been supported by the Ministry of Energy, Israel (grant no. 219-17-02). Co-author Jonatan Glehman was partially supported by the Ministry of
Energy scholarship for graduate studies (tender 76/19).
Review statement
This paper was edited by Paolo Frattini and reviewed by two anonymous referees.
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