NHESSNatural Hazards and Earth System SciencesNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus PublicationsGöttingen, Germany10.5194/nhess-18-2387-2018Revisiting seismic hazard assessment for Peninsular Malaysia using
deterministic and probabilistic approachesRevisiting seismic hazard assessment for Peninsular MalaysiaLoiDaniel WeijieRaghunandanMavinakere Eshwaraiahmavinakere.raghunandan@monash.eduhttps://orcid.org/0000-0002-3212-0352SwamyVarghesehttps://orcid.org/0000-0002-9006-386XCivil Engineering Discipline, School of Engineering, Monash University Malaysia, 47500 Bandar Sunway, MalaysiaMechanical Engineering Discipline, School of Engineering, Monash University Malaysia, 47500 Bandar Sunway, MalaysiaMavinakere Eshwaraiah Raghunandan (mavinakere.raghunandan@monash.edu)14September20181892387240826February20185March201829June20189July2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://nhess.copernicus.org/articles/18/2387/2018/nhess-18-2387-2018.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/18/2387/2018/nhess-18-2387-2018.pdf
Seismic hazard assessments, both deterministic and probabilistic, for
Peninsular Malaysia have been carried out using peak ground acceleration
(PGA) data recorded between 2004 and 2016 by the Malaysian Meteorological
Department using triaxial accelerometers placed at 19 seismic stations on
the peninsula. Seismicity source modelling for the deterministic seismic
hazard assessment (DSHA) used historical point sources whereas in the
probabilistic (PSHA) approach, line and areal sources were used. The
earthquake sources comprised the Sumatran subduction zone (SSZ), Sumatran
fault zone (SFZ) and local intraplate (LI) faults. Gutenberg–Richter law
b value for the various zones identified within the SSZ ranged between 0.56
and 1.06 (mean=0.82) and for the zones within the SFZ, between 0.57 and
1.03 (mean=0.89). Suitable ground motion prediction equations (GMPEs) for
Peninsular Malaysia along with other pertinent information were used for
constructing a logic tree for PSHA of the region. The DSHA “critical-worst”
scenario suggests PGAs of 0.07–0.80 ms-2 (0.7–8.2 percent g),
whilst the PSHA suggests mean PGAs of 0.11–0.55 ms-2 (0.5–5.4
percent g) and 0.20–1.02 ms-2 (1.9–10.1 percent g) at 10 % and
2 % probability of exceedance in 50 years, respectively. DSHA and
PSHA, despite using different source models and methodologies, both conclude that
the central-western cities of Peninsular Malaysia, located between
2 and 4∘ N, are most susceptible to high PGAs, due to
neighbouring active Sumatran sources, SFZ and SSZ. Of the two Sumatran
sources, surprisingly, the relatively less active SFZ source with low
magnitude seismicity appeared as the major contributor due to its proximity.
However, potential hazards due to SSZ mega-earthquakes should not be dismissed. Finally,
DSHA performed using the limited LI seismic data from the
Bukit Tinggi fault at a reasonable moment magnitude (Mw) value of 5.0
predicted a PGA of ∼0.40 ms-2 at Kuala Lumpur.
Introduction
Seismic hazard assessment (SHA) of a particular region can generally be
defined as the estimation of hazard at a specific site due to occurrence of
a hypothetically damaging earthquake originating within the geographic
region. The ground shaking experienced at a given site is directly related
to the intensity of seismic waves emitted by this natural phenomenon.
Violent ground shaking caused by devastating earthquakes can lead to both
massive fatalities and economic losses, as reported for past earthquake
events such as the 2004 Aceh earthquake, 2011 Christchurch earthquake, 2015
Nepal earthquake and 2016 Italy earthquake. The ground motions are normally
expressed through response parameters such as peak ground acceleration
(PGA), peak ground velocity (PGV) and response spectrum amplitude (RSA). An
understanding of the ground motion is one of the fundamental understandings
required to develop reliable seismic resistance design codes. These design
codes established from the ground motion information of a specific region
are valuable for practising engineers in the design of earthquake resistant
structures.
As Malaysia is a developing nation with new infrastructure being built at a
relatively fast rate in its major cities, it is essential that seismic
hazard assessment is undertaken to reliably predict ground motion scenarios
due to potential earthquakes. The ground motion values obtained will serve
as a reference for upcoming constructions and also for existing structures
as an evaluation to determine if retrofitting is required to mitigate the
seismic risk. Currently, the design code BS8110 is widely used by the
construction industry in Malaysia and the ongoing usage of this design code
can be deemed unwise as it does not include any seismic considerations
(Megawati et al., 2005; Shoushtari et al., 2016). It is worth noting that the
inherent seismic hazard for the Malaysia region has been acknowledged by the
government of Malaysia. In view of the lessons learnt from the devastating
earthquakes of the Sumatran region, especially in the aftermath of the 2004
Aceh earthquake, there have been initiatives such as publication of a
handbook on the requirement of incorporating seismic design, in particular
for concrete buildings in Malaysia based on Eurocode 8 and IBC2000 design
codes (Ministry of Science Technology & Innovation, 2009). However, the
values proposed in these codes may not be suitable for usage as they were
not specifically developed for this region (Sooria, 2012). Note that the seismotectonic parameters such as earthquake magnitude and
frequency, distance from the sources, among others, vary for different
regions of the world.
The SHA methods developed to deal with strong ground motions have been
elaborated in the literature (Baker, 2008; Kolathayar et al., 2012; Kramer,
1996; McGuire, 2001; Panza et al., 1999) with the most common methods
utilizing a deterministic or probabilistic approach. Deterministic seismic
hazard assessment typically uses earthquake magnitude and distance
associated with the highest hazard from historical records for a specific
seismic source to predict the ground motion at a site. This is commonly
achieved using a pre-determined seismic wave attenuation model also known as
ground motion prediction equation (GMPE). This method can be termed as a
“scenario-like description” for earthquake hazard (Reiter, 1991). Deterministic seismic hazard assessment (DSHA) is
often desirable for regions with well-defined seismotectonic models, for
example, California, where DSHA dictates the design ground motion parameters
for bridges and buildings (Wang, 2011). The application of this approach is
straightforward and less complicated, allowing engineers to make clear-cut
decisions, for consideration of other earthquake parameters unrelated to the
site is seldom required. However, DSHA has its own shortcomings in that it
does not take uncertainties (i.e. frequency of recurrence and ground
motion) into proper account (Baker, 2008; Kramer, 1996). This has inevitably
led to the development of probabilistic seismic hazard assessment (PSHA),
which resolves some of the inadequacies in DSHA including probability of
recurrence and earthquake magnitude uncertainty.
The use of PSHA has gained popularity in the past two to three decades with
the expansion of seismic networks throughout the world and consequent
availability of abundant seismic data. The method of PSHA was pioneered by
Cornell (1968) and further enhanced by a number of researchers including
Esteva (1969), Reiter (1991), McGuire (2004) and Atkinson et al. (2014). In
contrast to the straightforward DSHA method which uses a single absolute
value to estimate hazard at a site, PSHA allows the inclusion of
multi-valued parameters that consider uncertainties in earthquake factors
such as the location, size and the recurrence rate. The combination of
these parameters provides an advantage for PSHA as it enables assessment of
the likelihood of an earthquake ground motion exceeding a certain threshold
at a site of interest. PSHA employs flexible mathematical approaches which
are oftentimes presented in the ground motion annual return rate of
exceedance or return period, which facilitates engineers to perform seismic
risk assessment for a site of interest. Subsequently, with better
understanding of the seismic hazard, specifically on the relationship
between different sources and the potential shaking caused by impending
earthquakes, engineers can ascertain suitable design ground motion that a
structure should be able to withstand. PSHA, nonetheless, is not free of
criticism as some studies have observed that it is merely a numerical
creation with a hazy mathematical concept and the use of it may lead to
risky or overly conservative engineering design (Klügel, 2010; Wang,
2011). Therefore, it is always a good practice to supplement PSHA results
with analysis using DSHA.
In view of both its methodological limitation in not treating uncertainties
adequately and that ground motions felt within Peninsular Malaysia have been
predominantly due to infrequent distant events, the utilisation of DSHA in
Peninsular Malaysia has been relatively scarce. Unsurprisingly, PSHA has
been the choice for SHA by a number of researchers in this region. The PSHA
outcomes reported for this region have been recently discussed by
Loi et al. (2016) and Shoushtari et al. (2016). These authors have discussed possible
reasons for the variation in the published PSHA outcomes including the
utilisation of different GMPEs and data sets (either synthetic or recorded
ground motions), employment of different methodologies for PSHA and
site-specific conditions.
The major motivation for the current study is the lack of a dedicated GMPE
for Peninsular Malaysia. The past studies adopted regional GMPEs not
specifically developed for Peninsular Malaysia for SHA of this region.
Moreover, awareness of potential earthquake hazards in the country has
gained traction over the last decade, owing to a series of minor earthquakes
in Bukit Tinggi between 2009 and 2010 and the Sabah earthquake in 2015. In
2016, the Department of Standards Malaysia (2016) also drafted an Annex –
denoted as DMS16 in this paper, based on Eurocode 8 on the applicability
of seismic resistant design in Malaysia. With intensifying interest in
earthquake studies in Malaysia, the present work aims to contribute a
detailed study of the seismic hazard faced by Peninsular Malaysia including
the development of seismic zonation maps. To this end, updated strong ground
motion records obtained from the Malaysian Meteorological Department (MMD)
for the period of 2004–2016 in conjunction with recent findings on the
suitability of existing and new GMPEs for this region (Loi, 2018; Shoushtari
et al., 2016; Van et al., 2016) will be used in performing DSHA and PSHA for
Peninsular Malaysia encompassing a rectangular area of 1–7∘ N and 99–105∘ E.
The outcomes of the present research comprises (a) seismic hazard maps
based on both DSHA and PSHA via ground motion in terms of PGA at bedrock and
(b) hazard curves for major cities throughout the peninsula. The PSHA hazard
map will also present the PGA with 2 % and 10 % probabilities of
exceedance (PE) in 50 years.
Location of Peninsular Malaysia on the Sunda Plate and the seismic
sources around it (modified from Loi et al., 2016). The subduction lines,
fault lines and tectonic boundary were obtained from ArcGIS 10.4.
Tectonic setting and seismicity of Peninsular Malaysia
The foremost step in the SHA for a region is the identification of the
potential earthquake sources capable of yielding substantial ground motion
at a given site. The earthquake sources vary from active interplate
subduction regions where earthquake activity is relatively high as the
result of constant interactions between tectonic plates to stable
continental intraplate regions which are away from the plate boundaries and
can be identified based on historical seismological events and geological
data. The knowledge of the seismotectonic setting of a region is derived on
the basis of past seismicity and geological structures. The area considered
in the present study consists of the whole of Peninsular Malaysia located
between the latitudes 1 and 7∘ N and
longitudes 98 and 105∘ E (Fig. 1).
Peninsular Malaysia covers an area about 0.3 million km2 at the
southern tip of mainland Asia and is connected by land to Thailand to the
north while separated from Singapore by Johor Strait to the south and from
Sumatra of Indonesia by Malacca Strait to the west. Borneo, which contains the states of Sabah and Sarawak,
is located east of Peninsular Malaysia and is separated by South China Sea.
Tectonically, Peninsular Malaysia is located within the stable Sunda Plate.
Seismicity within the Sunda Plate has been historically low with progressive
collision with the Eurasian Plate relatively slow (Baroux et al., 1998). The
axis of rotation of the Sunda block is believed to be at
49.0∘ N to 94.2∘ E with a clockwise rotation
of 0.34 degree/million years (Simons et al., 2007). The general movement of
this block is eastwards at a slow rate of 6±1 and
10±1 mm yr-1 in its southernmost and northern boundaries, respectively (Simons et
al., 2007). Despite being located on a stable continental region, ground
motions due to earthquakes (both major and minor) are still experienced
within the country (Megawati et al., 2005; Ministry of Science Technology
& Innovation, 2009; Sun and Pan, 1995). Based on the chronological events
documented by various agencies such as the United States Geological Survey
(USGS), International Seismological Centre (ISC) and MMD, it could be
established that ground motions detected due to seismic activity within and
around Peninsular Malaysia can largely be attributed to two main sources:
far-field Sumatran sources and local intraplate earthquakes. These two
sources can further be grouped into three seismotectonic regions: Sumatran
subduction zone (SSZ), Sumatran fault zone (SFZ) and intraplate zones
within the Sunda Plate. Historical statistics obtained from MMD showed that
states located on the western coastline of Peninsular Malaysia are more
vulnerable to felt ground motions (Loi et al., 2016; Sooria, 2012). The
location of Peninsular Malaysia within the Sunda Plate and its nearby
seismic sources are presented in Fig. 1.
Schematic cross section of A–A from Fig. 1 showing the subduction
of Indian–Australian Plate beneath the Eurasian Plate and the location of
major seismic activities along the Sumatra subduction and fault zone. The
diverging white arrows merely indicate the separation between the Eurasian
Plate and the Indian–Australian Plate; and also the Indian Ocean and Sumatra.
Interplate faults in the Sumatran region
Figure 2 schematically illustrates the tectonic movements around the
Sumatran region that lead to major seismic activities. The island of Sumatra,
located on the Eurasian Plate, overrides the subducting Indian–Australian
Plate along the Sunda Trench. The subduction zone which lies on the Indian
Ocean bed is not as distinctive as the fault lines on Sumatra. This zone,
where the two plates converge, has generally been identified as the Sumatran
subduction zone. The SSZ is relatively younger south of the Equator
(approx. 50 Ma) and older towards the north (approx. 90 Ma) with historical records
showing that earthquakes of high magnitudes happening frequently at younger
and faster moving subducting plates (Cassidy, 2015; Gradstein et al., 1994;
Gutscher, 2016). This does not imply that mega-earthquakes are not likely to
happen at zones that are moving at a slower convergence: the 2004 Aceh
earthquake being a prominent example of the latter (McCaffrey, 2009). The
convergence of these plates is highly oblique to the southwest of Sumatra,
lying almost parallel and approximately 150–200 km away from its
coastline. The vector of plate motion varies around
57±8 mm yr-1 and
is oriented about N10∘ E (McCaffrey, 1991; Megawati et al., 2005;
Petersen et al., 2004; Prawirodirdjo et al., 2010). The resultant mega
earthquakes are directly related to the strong coupling between the
overriding and subducting plates with studies indicating that the focal
mechanism and hypocentral distribution being shallow and dips gradually
beneath the outer arc ridge (Newcomb and McCann, 1987; Pan and Megawati,
2002; Prawirodirdjo et al., 1997). SSZ has accounted for most of the
megathrust earthquakes in this region with records showing one of the
largest earthquakes ever to strike had a massive 9.0±0.2 on the
moment magnitude (Mw) scale in 1833 (Newcomb and McCann, 1987). Another
massive earthquake happened in 1861 at an estimated Mw of 8.4±0.1, which
was felt in Java and Peninsular Malaysia (Newcomb and McCann,
1987). More recently, the Aceh earthquake recorded at ∼Mw9.1–9.3 near the island of Simeulue (Nalbant et al., 2005)
generated giant tsunamis that led to thousands of fatalities and posed
colossal financial losses in terms of rebuilding and restoration work to the
surrounding regions. Although high-rise buildings were not structurally
damaged in distant countries such as Malaysia and Singapore, tremors were
still reportedly strongly felt even as far as India (Martin, 2005).
Lying east about 200 km away, parallel to the trench, is the Sumatran fault zone
that accommodates the oblique convergence along the plate margin. This
1900 km long dextral strike-slip fault runs in a northwest–southeast direction along the spine
of Sumatra, spanning 10∘ N to 7∘ S (Sieh and
Natawidjaja, 2000). The slip rate of this fault accelerates northwestwards
at varying speeds of 6 to 27 mm yr-1 with relatively high seismicity rates in
the vicinity of Sumani, Sianok and Angkola (Petersen et al., 2004;
Prawirodirdjo et al., 2000). This is in line with the Global Positioning
System (GPS) data studied by McCaffrey et al. (2000) that suggested a
uniform slip rate of 21±5 mm yr-1 across central Sumatra. A
geomorphology study of the SFZ by Sieh and Natawidjaja (2000) and Acocella
et al. (2018) found it to be highly segmented with 19 major geometrically
defined segments. Termed “equatorial bifurcation”, the largest irregularity
is located at the Equator, where the fault separates into two subparallel
branches at approximately 35 km apart (Sieh and Natawidjaja, 2000). The
geometrical irregularities exhibited along the sinusoidal shape of Sumatran
faults have tectonic and seismological significance that affects the rupture
dimensions, limiting the energy that could be released from this active
strike-slip fault (Balendra et al., 2002). This is supported by historical
data, noting that major earthquakes in this zone have never exceeded Mw7.8
(Natawidjaja and Triyoso, 2007). The same study also concluded, on the
basis of the assumption that all the fault zones are locked from surface to
a depth of 15 km, that the recurrence of large earthquake Mw7.2–7.4
is approximately 0.2/year while an earthquake of Mw7.4–7.7 is likely
to strike 0.1/year. Although earthquakes from SFZ are comparatively lower in
magnitude compared to those from the SSZ, the effects of major ruptures
belonging to the former such as the 2010 and 2011 events were still felt in
Peninsular Malaysia. The logical explanation is that the lower magnitude
effect of the earthquakes from SFZ is offset by the shorter distance to the
peninsula.
Intraplate faults within Peninsular Malaysia
The geological map published by the Mineral and Geoscience Department of
Malaysia (JMG) recognises three prominent set of fault systems trending in
northwest–southeast, north–south and east–west directions. Seven major faults were listed within the
Peninsular Malaysia, including the Bok Bak fault, Lebir fault, Terengganu fault,
Bukit Tinggi fault, Kuala Lumpur fault, Lepar fault and Mersing fault
(Mineral and Geoscience Department Malaysia, 2014). The location of these
mostly normal and strike-slip faults (Khoo and Tan, 1983) is shown in
Fig. 2. From November 2007 to May 2008, a series of low-magnitude (Mw<4.0) earthquakes were registered at Bukit Tinggi. These events
generated tremors felt by nearby residents and minor hairline cracks on the
wall at a nearby police station and school (Lat and Tajuddin, 2009; Lau et
al., 2005). Such occurrences were unanticipated as seismicity within
Peninsular Malaysia has historically been of low intensity around level VI
on the Modified Mercalli (MM) scale due to tremors instigated by Sumatran
events (Chai et al., 2011). These events presumably suffice after the
megathrust earthquakes at Aceh and Nias in 2004 and 2005, respectively, with
recent geophysical studies suggesting that the core of Sundaland to be
gradually deforming (Shuib, 2009). This notion is supported by GPS and
Shuttle Radar Topography Mission – Digital Elevation Mapping (SRTM-DEM)
measurements showing distortion of plates due to intraplate stress build-up
in the northwest of Peninsular Malaysia (Jhonny, 2009). Such movements
seemingly activate the intraplate faults, eventually leading to low
magnitude intraplate earthquakes. Considering that Kuala Lumpur (KL), the
capital of the nation, is located only about 30 km away, these events
warrant general public's interest and concern. The presence of these local
intraplate (LI) earthquakes requires further geomorphological studies for a
better understanding of the faults' behaviour and level of seismicity these
faults are capable of producing. A new hazard map incorporating potential
hazards posed by these active faults will certainly be useful for engineers
during seismic resistant design.
Seismic data completeness for (a) Sumatra subduction zone and
(b) Sumatran fault zone.
Location of MMD seismic stations across Peninsular Malaysia and the
ground motion values recorded for the period 2004–2016 by the MMD.
* Seismic sensor located inside the building. Records not utilised for the current study.
Earthquake database and catalogue
Over the past 15 years, the Malaysian Meteorological Department (MMD) has
set up a network of seismic stations across Peninsular Malaysia. In view of
economic and scientific importance, majority of these stations are located
in the west coast of Peninsular Malaysia where major cities are situated.
Moreover, they are located closer to the active Sumatran region. The network
comprises of 19 stations that use FBA-EST triaxial accelerometers, of
these 19 stations, 7 are equipped with broadband seismometers (Streckeisen
STS-1 and STS-2). The sensors used at these stations by MMD capture the
horizontal, vertical and surface accelerations due to an earthquake event.
Real-time data are transmitted via VSAT telemetry to the headquarters of MMD
for processing and analysis. These stations were built on various
foundations: granite, sandstone and soft soil. The sites are
referenced to the National Earthquake Hazards Reduction Program (NEHRP) site
classification by the Building Seismic Safety Council (2003). The
aforementioned two foundations on which 13 seismic stations have been
established can be classified as NEHRP site class B rock sites (average
shear wave velocity in the upper 30 m (VS30) of the soil profile with
VS30 ranging from 760 to 1500 ms-1) whereas the soft soil
foundation on which five seismic stations are situated is considered to be
NEHRP site class E (VS30 less than 180 ms-1). The data from one
remaining seismic station located within a building were not considered in
the current study. The details of these stations (location, foundation,
NEHRP site class and recorded PGA ranges) are listed in Table 1.
For the period of 2004 to 2016, a total of 88 earthquake events within a
rectangular area of 10∘ S to 10∘ N and 95
to 110∘ E that triggered considerable ground motion were recorded
by the MMD. The data set for PGA consists of 103 recordings for local
earthquakes and 368 recordings from far-field Sumatran earthquakes, 34 out of
88 events were categorised as low-magnitude local earthquakes which occurred
within Peninsular Malaysia and are of Mw≤4.0 whereas the
remaining 54 earthquakes were classified as far-field earthquakes from the
SSZ and SF. These latter events were located more than 400 km away and have
Mw ranging from 5.0 to 9.1. The focal depth of LI earthquakes ranges
from the surface to 22.5 km while the focal depths for far field earthquakes
range from 9 to 580.9 km. PGA data utilised in this study were from the
original uncorrected accelerograms and were not post-processed as they are
normally smaller due to time decimation and frequency band-limited filtering
(Campbell, 1981). As the recorded PGA values (in vertical and two
perpendicular horizontal directions) across Peninsular Malaysia were very
low (0.00003 to 0.0616 ms-2), the peak value from an individual
recording was utilised as the worst-case scenario in this study. The 378 records
were from rock sites (NEHRP class site B) while the remaining were from soil
sites (NEHRP class site E).
A comprehensive SHA requires a sizeable amount of data. In addition to the
data from MMD, we obtained data due to past earthquakes around the Sumatran
region from the USGS and ISC earthquake catalogues. The combined catalogue
comprises earthquake data for the region 10∘ N–7∘ S
and 90–106∘ E with minimum earthquake magnitude of
Mw≥4.0 for the period of 4 January 1907 to 31 December 2016.
The total events in the raw catalogue were 22 734. However,
considering that earthquake hazard is usually estimated using a Poisson
model, not all data from the catalogue were suitable as they contained both
foreshocks and aftershocks. The “de-clustering” (removal of the dependent
events, i.e. foreshocks and aftershocks from background seismicity) leads to
a better estimation of random events which is a vital aim in SHA (Kolathayar
and Sitharam, 2012). For this purpose, the de-clustering was performed using
the algorithm proposed by Gardner and Knopoff (1974). This process, together
with the removal of duplicates, eliminated 19 886 dependent events with the
remaining 2848 events identified as main shocks. Out of these 2848 events,
1128 events were from SFZ with Mw≥4.0 and the remaining 1720
were from SSZ with Mw≥5.0. The catalogue completeness analysis
was subsequently conducted using Stepp's (1972) method. Based on the
catalogue completeness analysis, the earthquakes from the SSZ for magnitude
interval between 5.0<Mw<5.4 are reported complete
for the past 45 years, while the earthquakes interval between 5.5<Mw<6.4
and magnitude Mw≥6.5 are considered
complete for the past 70 and 115 years, respectively. As for the SFZ, the
magnitude interval between 4.0<Mw<4.9 is reported
complete for the past 45 years, while the magnitude interval between 5.0<Mw<5.9 and magnitude Mw≥6.0 are
considered complete for the past 60 and 100 years, respectively. The results
are shown in Fig. 3.
Source modelling
Identification of the seismic source model based on geological evidence,
geotectonic province, historic seismicity, geomorphic investigation and
other relevant data is one of the crucial steps in SHA. For the present
study the earthquake sources utilised to define the source models have been
confined to an area encompassing 91∘–106∘ E and
10∘ N–7∘ S. Here the assumption is that earthquakes
that are capable of causing significant ground motion originate as far as
approximately 800 km radius away from the most northwestern point of
Peninsular Malaysia, the island of Langkawi, and the southernmost point,
considered here to be Singapore.
DSHA oftentimes presents the worst-case scenario of an earthquake event, and
consideration of the probability of location and time of occurrence plays a
less critical role compared to PSHA (Moratto et al., 2007). Although ground
motion data collection only began in 2004 in Peninsular Malaysia, records
of great earthquakes (Mw>8.0) from the Sumatra region are
available for the period since 1797 (Newcomb and McCann, 1987). It would be
insightful to model these historical events also to predict the PGA values
across Peninsular Malaysia. For this purpose, point sources instead of line
and areal sources are utilised here to replicate the historical events. With
no clear segmentation for the SSZ, as opposed to the SFZ, a grid of
1.0∘× 1.0∘ and a limitation of 200 km on either side of
the digitised subduction line were considered to cover the entire area. The
maximum possible earthquake (MPE) utilised for the analyses was the largest
earthquakes with Mw≥7.0 that occurred within the same grid since
1797. In addition, a simulated event of Mw9.1 was presumed at the
Mentawai–Siberut segment (2∘ S, 99∘ E) as studies have
reported the possibility of a mega-earthquake within the next couple of
decades (Lay, 2015; Philibosian et al., 2014). In contrast, the fault
lines on the SFZ have been researched more extensively and are better wedged
compared to the SSZ, with 19 segments spanning across Sumatra, as
listed in Sieh and Natawidjaja (2000). Therefore, events with Mw≥6.0
along these segments were considered as the MPEs. As for the LI events,
although a few major faults have been identified within the peninsula, only
minor earthquakes from Bok Bak and Bukit Tinggi faults have produced notable
ground motion, and thus only the six events with magnitude Mw>2.4 were considered.
With the MPEs thus determined, the next step was to assign a maximum
possible magnitude to these locations. Multiple scenarios were considered
for this objective. Scenario 1 represents the maximum historical earthquake
recorded by ISC, USGS and also Newcomb and McCann (1987) for the Sumatran
region while the maximum magnitudes for local earthquakes were recorded by
the MMD. Earthquake magnitudes that were recorded in body-wave magnitudes
(mb) especially for the data collected from MMD were converted to
Mw using the regression suggested in Loi (2018). As it is almost
impossible to determine if past events will be superseded by earthquakes of
larger magnitude, one standard rule of thumb that has been employed to
consider the “worst-case” scenario is to increase the magnitude of past
events by Mw0.25 or 0.5 (Naik and Choudhury, 2014; Secanell et al.,
2008; Shukla and Choudhury, 2012). Hence, this method was assigned to
Scenario 2. Due to its slower convergence, an increment of 0.3 Mw was
applied to events originating above the Equator from the SSZ. In addition,
this zone has undergone massive rupture, frequently releasing strain energy
in recent times which have resulted in mega-earthquakes of Mw 8.6, 8.6
and 9.0 in the year 2005, 2012 and 2004, respectively. In contrast, an
increment of Mw 0.5 was applied to events located below the Equator
from the same region due to this region's faster convergence and also
because researchers have predicted that a major earthquake may happen along
the Mentawai segment within the next few decades (Lay, 2015; Nalbant et al.,
2005). However, the maximum magnitude applied was limited to Mw9.5
considering that the largest ever earthquake recorded was the Mw9.5
1960 Chilean earthquake. Similarly, an increment of Mw0.5 was assigned
for events emanating from the SFZ with a maximum magnitude of Mw8.0.
Within the peninsula, records for the local intraplate events have been
scarce and sporadic. Hence, the MPEs for the local intraplate events were
retained as per Scenario 1 as it is difficult to estimate now a credible
maximum magnitude for the faults. Nonetheless, taking into account that KL
lies in close proximity to three major fault lines (Bukit Tinggi, Seremban,
and KL faults) and records indicating that stable continental earthquakes
have the odd capability of striking above Mw6.0 (Johnston and Kanter,
1990; Schulte and Mooney, 2005), a plausible increment of Mw1.0 was
assigned to the Bukit Tinggi event. The values from Scenarios 3 and 4, by
contrast, were obtained from literature and are only applicable for the SFZ.
Scenario 3 tabulates the predicted maximum magnitude for each of the 19
segments with a 200-year return period by Natawidjaja and Triyoso (2007),
while Scenario 4 represents the predicted maximum magnitude for each of the
16 tessellated zones in SFZ using k-means algorithm analytical approach by
Burton and Hall (2014). The maximum magnitudes for each of the four
scenarios were thereafter compared with the largest value being utilised as
the MPE.
An epicentre map of historical earthquake magnitudes: Mw≥5.0
for the Sumatran subduction zone, Mw≥4.0 for the Sumatran
fault zone and low-magnitude earthquakes within Peninsular Malaysia for the
period of 1906–2016. The records for these events were taken from USGS
earthquake catalogue, MMD and published literature. Earthquake sizes are
given on scales and colours proportional to the earthquake magnitudes. The
asterisks show the locations of the MPEs utilised for DSHA for each region.
Seismic zonation map for the Sumatra regions with SSZ and SFZ
being split into two different source models (line and area) for PSHA. The
details of these zones such as length, slip rate and Mw Max are listed
in Table 3.
List of MPEs from all three sources used in the DSHA.
EQTimeSourcea–Maximum MPEdno.Date(UMT)LocationcountrybEpicentre magnitudec (Mw) (Mw)SourceLat.Long.1234114 Sep 196415:29:38Nicobar IslandSSZ-IND8.8693.107.17.4––7.4USGS26 Dec 201019:26:50Nicobar IslandSSZ-IND7.8891.947.57.8––7.8USGS326 Dec 200404:21:29Nicobar IslandSSZ-IND6.9192.967.27.5––7.5USGS417 May 195514:49:55Nicobar IslandSSZ-IND6.8293.877.07.3––7.3USGS523 Aug 193621:12:16Northern SumatraSSZ-IND5.3294.727.07.3––7.3USGS626 Dec 200400:58:53Northern SumatraSSZ-IND3.2995.989.19.4––9.4USGS74 Nov 201208:38:36Northern SumatraSSZ-IND2.3393.068.68.9––8.9USGS821 Nov 196902:05:38Northern SumatraSSZ-IND2.0094.497.67.9––7.9USGS920 Feb 190808:08:30SimeulueSSZ-IND2.7795.967.47.7––7.7USGS1028 Mar 190516:09:36Northern SumatraSSZ-IND2.0997.118.68.9––8.9USGS111 Apr 190705:19:11Northern SumatraSSZ-IND1.8794.217.88.1––8.1USGS124 Nov 191210:43:10Northern SumatraSSZ-IND0.8092.468.28.5––8.5USGS1317 Nov 198406:49:30NiasSSZ-IND0.2098.037.17.4––7.4USGS1428 Dec 193502:35:31Kepulauan BatuSSZ-IND-0.2998.267.68.1––8.1USGS155 Aug 194605:20:27Southern SumatraSSZ-IND-0.7599.107.37.8––7.8USGS1610 Feb 1797–MentawaiSSZ-IND-1.0099.008.48.9––8.9NM87f17e––Mentawai–SiberutSSZ-IND-2.0099.009.19.5––9.5–1825 Feb 200808:36:33MentawaiSSZ-IND-2.4999.977.27.7––7.7USGS1925 11 1833–MentawaiSSZ-IND-2.50100.509.29.5––9.5NM87f2025 Oct 201014:42:22MentawaiSSZ-IND-3.49100.087.88.3––8.3USGS2125 Jun 201419:07:25Southern SumatraSSZ-IND-3.92101.827.68.1––8.1USGS229 Dec 200711:10:26Southern SumatraSSZ-IND-4.44101.378.59.0––9.0USGS236 Apr 200016:28:26Southern SumatraSSZ-IND-4.72102.097.98.4––8.4USGS2425 Sep 193105:59:52Southern SumatraSSZ-IND-5.43102.287.47.9––7.9USGS252 Mar 201612:49:48Southern SumatraSSZ-IND-4.9594.337.88.3––8.3USGS262 Apr 196401:11:50SeulimeumSFZ-IND5.5795.377.07.57.67.37.6USGS278 Mar 193503:58:00AcehSFZ-IND4.4096.407.27.77.97.07.9USGS2810 Oct 199615:21:04TripaSFZ-IND3.4697.946.36.87.87.67.8USGS295 Sep 201117:55:11RenunSFZ-IND2.9797.896.77.27.97.67.9USGS3019 May 200814:26:45ToruSFZ-IND1.6499.156.06.57.47.67.6USGS3111 Nov 199918:05:43AngkolaSFZ-IND1.28100.326.26.77.77.87.8USGS328 Mar 197723:17:28BarumunSFZ-IND0.45100.026.06.57.67.87.8USGS337 Nov 200718:37:45SumpurSFZ-IND0.2499.966.16.66.97.87.8USGS346 Mar 200603:49:38SianokSFZ-IND-0.49100.506.46.97.47.87.8USGS359 Jun 194303:06:20SumaniSFZ-IND-0.83100.747.88.07.27.88.0USGS3619 May 197922:34:34SulitiSFZ-IND-1.08100.965.45.97.47.87.8USGS376 Oct 199518:09:45SiulakSFZ-IND-2.05101.446.87.37.37.87.8USGS381 Oct 200901:52:27DikitSFZ-IND-2.48101.506.67.17.27.87.8USGS398 Jun 194320:42:43KetaunSFZ-IND-2.90102.157.47.97.47.87.9USGS4015 Dec 197900:02:41MusiSFZ-IND-3.30102.716.67.17.37.87.8USGS4110 Oct 197421:32:10MannaSFZ-IND-4.14102.836.06.57.47.87.8USGS4224 Jun 193321:54:49KumeringSFZ-IND-5.23104.607.68.07.77.68.0USGS432 Apr 191900:34:59SemangkoSFZ-IND-5.50104.496.46.97.27.27.2USGS4425 Oct 200009:32:23SundaSFZ-IND-6.55105.636.87.37.77.17.7USGS4525 May 200801:36:22Bukit TinggiLI-MYS3.36101.754.05.0––5.0MMD4627 Mar 200901:46:25JerantutLI-MYS3.86102.522.8–––2.8MMD4729 Apr 200913:53:54ManjungLI-MYS4.15100.732.4–––2.4MMD4820 Aug 201300:26:27Kupang (Baling)LI-MYS5.59100.883.8–––3.8MMD496 Apr 198513:34:35Hulu TerengganuLI-MYS5.10102.603.8–––3.8MMD503 Jan 201617:33:15TemenggorLI-MYS5.55101.362.8–––2.8MMD
a Source: SSZ – Sumatran subduction zone; SFZ –
Sumatran fault zone; LI – local intraplate. b Country: IND – Indonesia. MYS –
Malaysia. c 1: maximum historical earthquake; 2: maximum historical earthquake
+0.3Mw for SSZ above the Equator, or +0.5Mw for SSZ below the Equator up to
a maximum of 9.5 and SFZ until a maximum of Mw 8.0 and +Mw 1.0 for Bukit
Tinggi;
3: maximum earthquake predicted from Natawidjaja and Triyoso (2007);
4: maximum earthquake from Burton and Hall (2014). d MPE: maximum magnitude from column
1, 2, 3 and 4. e. Event 16 is a simulated event which predicts that the Mentawai gap
(0∘–2.5∘ S) may produce large EQ in the next few decades
(Nalbant et al., 2005; Lay, 2015). f NM87: Newcomb and McCann (1987).
A total of 50 MPEs were identified from all three regions (SSZ, SFZ and LI).
The 25 events were for the SSZ, with the largest anticipated events coming from
the 2004 Aceh earthquake and the simulated Mentawai–Siberut earthquake at
Mw9.4 and Mw9.5, respectively, while smaller events (Mw of
7.3–7.8) were projected around the Nicobar Islands cluster between
6 and 9∘ N. The least maximum magnitude for the SFZ
was located near the Toru, Barumun and Manna segment, recorded at Mw6.0 while the largest was from the Sumani segment, recorded at Mw7.8.
Despite the relatively low magnitudes recorded for the former, Natawidjaja
and Triyoso (2007) estimated based on rate of seismic moment calculation
that a maximum magnitude for these three segments may be as high as Mw7.4. The maximum magnitude calculated by Burton and Hall (2014) for the same
zones was even higher, in the range of Mw=7.6–7.8. The maximum
magnitude estimated by these two literature sources was noticeably higher
when compared to actual recordings and thus were selected as the MPEs
for our DSHA. As for the local earthquake scene, the highest MPE utilised
for DSHA was that of the Bukit Tinggi earthquake. A detailed list of these
events from all three regions with four different scenarios and the selected
MPEs is presented in Table 2 and the locations are illustrated in Fig. 4.
Summary of locations, earthquake recurrences and seismic activity
quantification for the SSZ and SFZ.
Similar to DSHA, one of the crucial steps in PSHA is to identify the seismic
source model. While DSHA in the current study utilises point source, linear
and areal sources were used for the PSHA. Although the utilisation of the
latter two sources have been well documented in the literature (Anbazhagan
et al., 2008; Kramer, 1996; Ornthammarath et al., 2010; Vipin et al., 2009),
specifying the linear and area sources for SSZ is complicated owing to the
following: the SSZ is extremely long (over 4000 km), its location off the
coast of Sumatra and key tectonic parameters such as its
segmentation length, displacement and area are not well defined. The
subduction line utilised in the PSHA analyses for the SSZ was approximately
digitised using the USGS maps. In regard to the upper and lower boundaries
of SSZ, past observations have noted that majority of the earthquakes tend
to strike at a certain depth to the east of the subduction line, instead of
to the west, due to the subduction of the Indian–Australian Plate (Fig. 5).
This phenomenon is more prominent to the south of the Equator as illustrated
in Fig. 5. Keeping in mind that large earthquakes are capable of striking on
both sides of the subduction line, the boundary width of the areal source
for SSZ was confined to be within 200–250 km on either side of the
subduction line and away from Sumatra. As the age of the plate and
slip rates differ from north to south, with literature suggesting that the
slip rate increases from north to south along the subduction line (Chlieh et
al., 2007; Moeremans et al., 2014; Subarya et al., 2006), this zone was
further segmented into seven different zones at every 2∘ or
3∘ latitude intervals with different modelled maximum magnitude
(Mw Max) for each individual zone.
In contrast to SSZ, the occurrences of earthquakes to the east and west of
the SFZ are almost equal throughout. Although the SFZ has been better
defined, as shown by Natawidjaja and Triyoso (2007), some of the subdivided
segments tend to overlap making the fault line boundary determination
somewhat complicated. Therefore, for the latitudinal margin for the SSZ, the
boundary was divided as per the suggestion by Burton and Hall (2014).
However, the SFZ in the present work is subdivided into 13 instead of 16
segments as suggested by Burton and Hall (2014). This is achieved by
combining the southernmost three segments into one segment in view of the
fact that these are located relatively far off from the area of our
interest. However, the width of these zones was not uniform: to the left of
the fault line the zone width was constrained to be within Sumatra
while to the right, the width varied from approximately 20 to 100 km away
from the fault line. Although the segmentation of this study follows the
suggestion by Burton and Hall (2014), the slip rate was approximately
extracted based on Natawidjaja and Triyoso (2007). For example, even though
the length of Zone 1 is shorter and falls into the Seulimeum fault in
Natawidjaja and Triyoso (2007), the slip rate was assumed to be the same as
suggested in Natawidjaja and Triyoso (2007). A map showing source modelling
zonation for the PSHA is illustrated in Fig. 5.
While multiple scenarios were considered to determine the MPEs in the DSHA,
in the PSHA for SSZ the present analysis considered that a Mw Max
earthquake could take place all along the SSZ even though the values vary
from north to south. With slip rates towards the north relatively slower
compared to those in the south, the upper boundary Mw Max for Zone 1 was
fixed at 9.0 with the values gradually increasing until a maximum of 9.5 for
Zone 7. By contrast, multiple MPEs for the SFZ from Table 2 fall within a
same zone for some cases in the current study. As such, the Mw Max is
selected based on the highest MPE within the same zone. The length, slip
rate and Mw Max for each zone are given in Table 3.
Magnitude vs. cumulative number relation obtained using the
Gutenburg–Richter Law (GR-Law) for the (a) Sumatran subduction zone and the (b) Sumatran fault zone. The
b values are listed in Table 3.
Although not directly related to the PSHA, Table 3 also summarises the
observations for earthquake occurrences per year for the past 40 years
(since 1976) for every interval of Mw1.0 from both zones. This is
despite that the SHA considers records from USGS since 1907. The approximate
range of 40 years was chosen based solely on observation. The reason is that
the records for the years prior to 1976 are relatively scarce. Besides,
throughout the years, the expansion of ground motion stations worldwide and
collection of earthquake data have progressively increased, and it is
difficult to determine a cut-off point to which time should reliable data be
considered. Moreover, data prior to 1976 consist of <8 % of the
overall records, after the removal of foreshocks and aftershocks. The
records for the SSZ clearly show that earthquake occurrences in Zone 7 are
relatively higher compared to that in Zone 1, in line with studies
suggesting movement rates are higher in the south of the SSZ, thereby
indicating that higher slip rates result in higher frequency of earthquakes.
However, a similar pattern cannot be observed for the SFZ wherein the
earthquake frequency is rather scattered with no clear correlation between
the slip rate and the frequency of earthquake occurrences. This is reflected
for the SFZ in Zones 1 and 13 wherein although the pair have similar fault
lengths and slip rates, the difference in frequency of occurrences was still
relatively distinct at 0.44 and 2.18, respectively. Similarly, both Zones 8
and 9, despite having analogous fault lengths and slip rates did not result
in similar frequency of occurrences. Apart from that there also seems to be
no direct link between slip rate and the upper boundaries of Mw for
both regions.
Regional seismicity recurrence
One of the most commonly used methods to characterise seismicity for a
region is the Gutenberg–Richter earthquake recurrence law (Gutenberg and
Richter, 1944). This law estimates the seismic parameter b value which
follows a magnitude exponential distribution expressed as follows:
log10Nm=a-bM,
where Nm is the total number of earthquakes exceeding M for the predetermined
region, a is a constant that reflects the earthquake productivity or seismic
activity, and b indicates the relative occurrence of small and large events.
Larger b values, the slope of frequency vs. magnitude distribution (FMD),
implies a larger proportion of small earthquakes whereas a small b value
represents relatively small number of high-magnitude earthquakes (Nanjo et
al., 2012). Of the two variables, the b value has often been prioritised by
researchers and has undergone many statistical and analytical evaluations
over the past few decades. It has been widely recognised that this value
normally hovers around 1.0 for seismically active regions (Baker, 2008;
El-Isa and Eaton, 2014; Mogi, 1962; Singh et al., 2015).
Plots at various magnitude intervals for the GMPEs used in the
current study with respect to recorded ground motion data for the (a) Sumatran
subduction zone, using GMPEs proposed by Loi (2018) and Shoushtari et al. (2016),
denoted as SSZL18 and S16, respectively; the (b) Sumatran fault
zone,
using the GMPEs proposed by Loi et al. (2018) and Si and Midorikawa (2000),
denoted as SFZL18 and SM00, respectively; and the (c) local intraplate fault
zone, using the GMPE proposed by Nguyen et al. (2012),
denoted as N12.
A least-squares regression method was utilised to obtain the b values for
the studied region with earthquake threshold magnitude above Mw5.0 for
the SSZ and 4.0 for the SFZ. Figure 6 presents the FMD plots for the SSZ and
SFZ as a whole and also for each of the 7 and 13 zones individually with the
b values listed in Table 3. However, it should be remembered that the
b values in the table have no relation to the observation column in Table 3
as the FMD plots considered data since 1907 and not only for the past 40 years.
As illustrated in Fig. 6, the b values range between 0.56 and 1.06 for the
SSZ and between 0.57 and 1.03 for the SFZ. The estimated b value for Zone 3
in SSZ was noted to be particularly low as this zone has been associated
with only a few earthquakes of with Mw>8.0 since
2000. As for the SFZ, the estimated low b values for Zone 1 is due to the
moderately short length of Zone 1 with historically large earthquakes
(Mw>6.0). The low b value for Zone 9, in spite of its
relatively long length, is due to the comparatively low earthquake
recurrences on top of the occurrence of odd earthquakes with high magnitude
(Mw>7.0). Despite their relatively low b values, the average
for the overall regions of SSZ and SFZ was higher at 0.82 and 0.89,
respectively. These values concur well with the b values for the PSHA
obtained for Sumatra and KL by Irsyam et al. (2008) and Nabilah and
Balendra (2012). Petersen et al. (2004) performed PSHA for Sumatra,
Singapore and Peninsular Malaysia using proposed
b values between 0.63 and 1.08. Pailoplee
et al. (2005) and Pailoplee (2017)
also calculated relatively low b values, especially for Sumatra, at
0.61 and 0.27, respectively.
Ground motion prediction equations (GMPEs)
Suitable GMPEs that can predict or estimate ground motions in good agreement
with recorded ground motion data due to past seismic events are fundamental
to SHA. Although numerous GMPEs have been developed and applied worldwide,
not many GMPEs are available exclusively for Peninsular Malaysia due to its
relatively lower local seismicity and distant location from active seismic
hotspots such as the Sumatran region. Naturally, past attempts either
adapted or adopted regional GMPEs or relied on the available limited data
for developing GMPEs suitable for this region (Adnan et al., 2005; Pan and
Megawati, 2002; Petersen et al., 2004). The collection of seismic ground
motion data since 2004 by MMD, albeit relatively smaller in quantity
compared to more earthquake active regions, has since allowed researchers to
either identify suitable GMPEs (Van et al., 2016) or develop independent
GMPEs for the peninsula (Adnan and Suhaltril, 2009; Loi, 2018; Nabilah and
Balendra, 2012; Shoushtari et al., 2016) using the available ground motion
records. Loi et al. (2016), Van et al. (2016) and Shoushtari et al. (2015)
have compared the adaptability of selected worldwide GMPEs revealing their
limitations wherein most of them either overestimated or underestimated the
actual ground motion data for the peninsula. Therefore, more accurate GMPEs
developed for this region by Loi (2018) and Shoushtari et al. (2016),
together with the GMPE developed for Japan by Si and Midorikawa (2000) are
used here to carry out the DSHA and PSHA. As for the LI earthquakes, only
DSHA was carried using the Nguyen et al. (2012) GMPE that best fits the
scarce data of low-magnitude events (Loi, 2018). The pertinent details of
the GMPEs including their functional form, magnitude and distance ranges,
tectonic environment and standard deviation utilised to conduct DSHA and
PSHA are shown in Table 4. It should be noted that although the distance
range of SM00 may not be applicable to the current scenario for SFZ,
extrapolation of the model predicts the recorded ground motion data quite
well. PSHA was not conducted for the LI earthquakes due to the limited
availability of information such as slip rate and recurrence rate of the
existing faults. The relationship of these GMPEs to recorded ground motion
data due to the SSZ, SFZ and LI earthquakes is plotted at various magnitude
intervals in Fig. 7.
R is the hypocentral distance utilised
to conduct DSHA and PSHA in the current work.
Logic tree structure with weightages for PSHA.
Logic tree structure
There are inherent uncertainties associated with earthquake data and these
uncertainties can be broken down into two categories: aleatory (statistical)
and epistemic (systematic) (Bommer et al., 2005). Whereas aleatory
uncertainty is unavoidable due to the fact that an earthquake is a random
process, epistemic uncertainly (limited knowledge and data) can be accounted
for using a logic tree structure (Bommer et al., 2005; Delavaud et al.,
2012; Marzocchi et al., 2015; Youngs and Coppersmith, 1985). A logic tree
consists of a series of nodes that lead to multiple branches. The branches
allow a formal characterisation for addressing uncertainties in the analysis
by including parameters and models (hypothesis), each being subjectively
weighted on the basis of engineering judgment and their probability of being
accurate. The weightage for each individual branch leading up to the end
branch can be multiplied to obtain the weightage of that particular route
and the sum the weightages should equal to 1. Parameters selected for
constructing logic tree formation in this study include different regions,
source modelling, magnitude uncertainty model, b values and GMPEs.
For DSHA, the selected GMPEs from the respective regions were weighted to
predict the value of PGA at a site of interest. Two different GMPEs were
suggested for SSZ and SFZ in Loi (2018) denoted as SSZL18 and SFZL18,
respectively. As SSZL18 showed more reliability compared to S16 (the GMPE by
Shoushtari et al., 2016) for the ground motion data due to SSZ sources,
especially at lower magnitude range (Fig. 7a), weightages of 0.6 and 0.4
were assigned to the respective GMPE. In contrast, weightages were
equally split for the GMPEs applicable to the SFZ as both SFZL18 and SM00
(GMPE by Si and Midorikawa, 2000) showed close estimation in relation to
recorded ground motion data. The GMPE suggested by Nguyen et al. (2012),
denoted N12 here, was utilised for LI earthquakes. An in-house Microsoft
Excel based program was designed to perform the DSHA with hazard outcome
being the maximum possible PGA estimated as a function of distance and
magnitude taking into consideration each of the 50 MPEs.
PGA maps of Peninsular Malaysia obtained using DSHA. (a) Case 1
– mean GMPE, (b) Case 2 as “critical-worst” case – mean GMPE plus
standard deviation.
For PSHA, the source geometries were split into line and area source with
equal weightages for the two geometries. The line and areal sources were
further split into individual zones and entire zone (see Fig. 8).
Individual zones M1 and M3 represent the segmented zones from the SSZ and
SFZ. M1 consists of 7 zones from the SSZ whereas M3 consists of 13 zones
from the SFZ. M2 and M4, in contrast, represent the entire length of
SSZ and SFZ, respectively. The weightage of individual zones was assigned to
be 0.7 while the weightage for the entire zone was assigned to be 0.3. The
reason for assigning higher weightage to individual zones is that the
frequencies of earthquakes that rupture over a short length or small area
are much higher compared to that for an extended length or larger area such
as the 2004 Aceh event. Besides, the probability of the entire zone
rupturing and producing extremely high-magnitude earthquake is lower
compared to that for an individual zone. The weightages for b values,
separated into fixed (mean b value calculated for the entire zone) and
variable (b value calculated for the individual zones), were also equally
split for the individual zones, while only the fixed b values were utilised
for the entire zone. The PSHA was subsequently conducted using the same
weightages for the GMPEs as used in the DSHA. A PSHA logic tree structure
with the respective weightages to the branches is shown in Fig. 8. PSHA
calculations using the input parameters such as geometry, source models,
b values and GMPEs were conducted using EZ-Frisk v8.00, developed by Risk
Engineering Inc, USA.
While PSHA performs integration on all the possible earthquake occurrences
and ground motions to predict the mean frequency of exceedance, the
knowledge of the source relative contribution to the hazard in terms of
distance and magnitude is oftentimes valuable and disaggregation is one such
method (Bazzurro and Allin Cornell, 1999; McGuire, 1995; Trifunac, 1989).
Disaggregation of PGA was carried out in terms of bin pairs of distance and
magnitude (R, M) at 20 km and Mw0.1, respectively, following the
procedure presented in EZ-Frisk.
PGA maps of Peninsular Malaysia for Case 2 for the sources
originating from (a) Sumatran subduction zone and (b) Sumatran fault zone
based on Case 2.
PGA map based on a simulated event of Mw5.0 and 6.0 from
the Bukit Tinggi fault.
Hazard curves for different cities in Peninsular Malaysia and
Singapore at rock sites.
Results and discussionHazard maps
Two cases were considered for this study. Case 1 considered the mean values
from the GMPEs to predict the PGA whereas Case 2 considered the mean values
from the GMPEs plus their respective upper boundary standard deviation to
predict the PGA. However, it should be noted that for the local intraplate
MPEs, only the mean values from N12 were used for both cases (see below).
Two separate DSHA maps for Case 1 and Case 2 were subsequently plotted with
the hazard values for each grid point using ArcMap 10.4 (Fig. 9a and b).
Figure 10a and b were plotted for SSZ and SFZ individually using Case 2
considering this can be termed as the “critical-worst” case to determine
the MPEs that contribute to the ground motion hazard for the major cities
across Peninsular Malaysia.
PGA Maps of Peninsular Malaysia at rock site condition affected
by the Sumatran sources at 10 % and 2 % in 50 years probability of
exceedance, respectively. PM02 denotes Pan and Megawati (2002), P04 denotes
Petersen et al. (2004), A05 denotes Adnan et al. (2005), A06 denotes Adnan
et al. (2006) and DMS16 denotes the Draft National Annex by the Department of
Standards Malaysia (2016).
As observed for Case 1 in Fig. 9a and b, the PGA value varies from 0.02 to
0.34 ms-2 across the peninsula while the PGA values expectedly rise
approximately 2.5 times for Case 2 in the range of 0.07–0.80 ms-2.
Both figures clearly show that lower central-western area (below latitude
4.0∘ N) of Peninsular Malaysia is more susceptible to higher
seismic hazard with PGA values decreasing from the southwest to northeast of
Peninsular Malaysia. When the overall DSHA map is split into the regional
sources (SSZ and SFZ), as shown in Fig. 10a and b, it is observed that the
source that contributes to the high PGA in the cities of KL, Seremban and
Malacca was the SFZ with the MPE associated located close to the Angkola
segment. Although this event is noted to occur slightly off the Sumatra
fault line compared to the remaining events from the SFZ, this hypothetical
MPE further illustrates that the controlling earthquake could be located
closer to the peninsula and hence fits in with worst-case scenario often
associated with DSHA. Conversely, the high PGA predicted in the northwestern
islands of Penang and Langkawi originates from the SSZ with the MPE
associated being the epicentre of the 2004 Aceh earthquake, hereby modelled
at Mw9.4. It is also worth noting that in spite of having simulated a
hypothetical event near the Siberut-Mentawai segment at Mw9.5, the PGA
estimated at KL, Seremban and Malacca from this SSZ event was still lower
when compared the event originating at Angkola from the SFZ, thereafter
highlighting the hazard that the SFZ may produce. Nevertheless, the PGAs
predicted on the southern peninsula and Singapore from both regions were
similar, with the SSZ capable of producing PGA ranging from 0.16 to 0.20 ms-2
while the SFZ is expected to produce PGA between 0.18 and 0.24 ms-2 at Johor Bahru and Singapore.
Disaggregation plots showing PGA relative contribution from the
Sumatran region for Peninsular Malaysia and Singapore as a function of
distance and magnitude at various major cities at 10 % and 2 % PE,
respectively.
Although there were six MPEs in total associated with the LI earthquakes,
only three MPEs were large enough to produce high PGAs compared to the
events originating from the Sumatran region (see contour lines in Fig. 9).
The remaining three MPEs were of very low magnitude at less than Mw3.0.
Of particular interest is the MPE modelled at Mw5.0 close to the
Bukit Tinggi fault. In relation to this fault, the PGA predicted within the
20 km vicinity from the centre of KL (3.14∘ N and
101.69∘ S) can reach as high as 0.4 ms-2 with the value
peaking at 0.5 ms-2 approximately 10 km away from the
epicentre (Fig. 11). Although this work considered Mw5.0 as a plausible case, concerns
have been raised by Looi et al. (2013) in an extreme event of Mw6.0
that cannot be ruled out. Therefore, utilizing the same source but altering
the maximum magnitude to Mw6.0, the PGA values for this special case
were further calculated and plotted in Fig. 11 for comparison purposes. The
PGA observed was exceedingly high and the predicted values were capable of
rising as high as 3.0 ms-2. This value is approximate 6 and 4 times
more than the PGA expected from the MPE for the LI events and Case 2 for the
Sumatra region, respectively. Although the predicted PGAs show a sharp drop
to 1.2 ms-2 at the centre of KL, these values are still alarmingly
high. This is certainly expected as the Nguyen et al. (2012) GMPE used for
the DSHA is applicable up to a suggested local magnitude (ML) of 4.6.
Therefore, until a better understanding of the critical magnitude that these
LI faults can produce is achieved coupled with a more suitable GMPE, this
value may be too conservative to be implemented for seismic resistant design
in KL. Furthermore, seismic resistant design for countries located on stable
continental regions with low seismicity worldwide mostly have a threshold of
0.2 g (Giardini et al., 2013; Woessner et al., 2015). The PGA values from the
current work fall within previous DSHA studies performed by local
researchers. Manafizad et al. (2016) predicted the PGA across the country in
the range of 0.01–0.191 ms-2 while the estimate by Adnan et al. (2005)
was relatively low, between 0.03 and 0.07 ms-2.
Probability of exceedance (PE) maps and hazard curves
Now considering the PSHA, it has been well established that earthquake
designs are based on 10 % and 2 % probability of exceedance (PE) in
50 years (return period of 475 and 2475 years, respectively) with the outcome
expressed in hazard curves and macrozonation contour maps of mean PGA. For
the current study, it should be noted that these hazards are calculated
based on rock site condition with references to NEHRP class B with the
average shear-wave velocity being 760 ms-1 in the upper 30 m of the
crust. Figure 12 presents the hazard curves in terms of mean annual rate of
exceedance against PGA at various cities across Peninsular Malaysia, which
clearly highlights that the hazard in central-western cities (between
latitudes 2 and 4∘ N) being the highest, followed by
the northwestern (above 4∘ N) and southern (below 2∘ S)
cities (including Singapore). The information from the hazard curves is
reflected in the regular PE maps displayed in Fig. 13. The ground motions
across Peninsular Malaysia expressed in PGA at bedrock range from 0.11 to
0.55 ms-2 and 0.20 to 1.02 ms-2 for 10 % and 2 % PE in
50 years, respectively. Although the PGA values differ, both maps exhibit a
similar pattern in that the PGA values gradually decrease from west towards
east of the peninsula. Once again, higher PGA values were observed for KL
and Malacca with the lowest PGA estimated at Kuantan. Even though the DSHA
indicated that the southern region is more susceptible to higher hazard in
comparison to the northwestern region, the PSHA suggests that the hazard at
northwestern region to be higher compared to the southern region. The region
for this discrepancy lies with the source model whereby DSHA utilised
historical point sources whereas PSHA utilised linear and areal sources.
PSHA results from this work are further compared with similar PSHA work from
the past literature and seismic resistance values suggested in DMS16.
The bars next to the PE maps in Fig. 13 show the PGA ranges estimated across
Peninsular Malaysia by various researchers in the past. The PGA estimated
from a study by Pan and Megawati (2002), denoted as PM02, for 10 % and
2 % PE in 50 years was between 0.13 and 0.30 ms-2 and 0.24 and 0.55 ms-2
across Singapore and Peninsular Malaysia, respectively. A separate
study conducted by Petersen et al. (2004), denoted as P04, predicted
relatively high PGA values of 0.40–1.17 ms-2 and 0.78–1.96 ms-2
while Adnan et al. (2005), denoted as A05, predicted values
between 0.10 and 0.25 ms-2 and 0.15–0.35 ms-2 across the
peninsula at 10 % and 2 % PE in 50 years. Another separate study by
Adnan et al. (2006), denoted as A06, predicted rather high PGA with
values ranging from 0.20 to 1.00 ms-2 and 0.40 to 2.00 ms-2 for
the same 10 % and 2 % PE in 50 years. As for the more recently drafted
DMS16, a definitive range was not clearly indicated for the same return
periods, but it was suggested that ordinary buildings (defined as low-rise
structures or individual dwellings) were to be designed against 0.69 ms-2
at 10 % PE in 50 years while important critical structures such as
hospitals, emergency services, power stations and communication facilities
should be designed against 0.98 ms-2 at 2 % PE in 50 years. The PGA
calculated from this work presents a wider range of hazard across the
peninsula when compared to the predictions by A05 and PM02. While the PGA
calculated at the higher spectrum coincides with the PGA for the lower range
of A06, the PGA data from this study do not agree well with the PGAs
calculated by P04. We believe that the current work possibly represents the
seismic ground motion experience in the peninsula better than the previous
studies given that the earthquake data used here are richer and the GMPEs
applied more reliable in relation to the actual ground motion records.
Source contribution hazard curve for KL, Penang and Singapore.
Disaggregation and hazard source
The combined disaggregation results from both regions at 10 % and 2 % PE
in 50 years across the major cities in the peninsula and Singapore are
displayed in Fig. 14. The results provide information regarding the
magnitude–distance combinations that have a major contribution to the PGA
values together with the mode and mean distances and magnitudes.
The results show that the SSZ is the main hazard contributor at northwestern
region (Penang and Langkawi), southern region (Johor Bahru and Singapore)
and eastern region (Kuantan) at 10 % PE over 50 years. Penang (despite being
situated relatively close to Langkawi in the northern region), the
cities of the central-western region (Ipoh, KL and Malacca) and the southern region
(Johor Bahru and Singapore) are more susceptible to hazards originating from the SFZ,
especially at 2 % PE over 50 years. However, at a longer return period,
the higher PGA predicted at central-western and southern peninsula was
noted to originate from the SFZ.
Furthermore, hazard sources affecting three major cities representing the
north and central regions along the west coast and also Singapore were
selected for comparison in Fig. 15. It can be observed that the major source
that contributes to the hazard in Penang especially at the lower PGA range
(less than 0.1 ms-2) originates from the SSZ. Meanwhile, hazards
calculated at KL were likely due to events located from the SSZ for PGA
approximately less than 0.30 ms-2 while events from SFZ contribute more
at higher PGA, albeit at a noticeably lower frequency. A similar trend was
also observed for Singapore where the hazard contribution at PGA
approximately less than 0.65 ms-2 mainly originates from the SSZ. The
hazard curve for SSZ gradually tapers towards the hazard curve for
SFZ at higher PGA, similar to all three other cities, indicating that hazard
posed by SFZ increases at higher PGA.
Conclusions
In summary, this paper presents an overall SHA in terms of PGA at bedrock
for Peninsular Malaysia using the DSHA and PSHA approaches. Historical point
sources were modelled in DSHA while line and areal sources were utilised for
PSHA. Earthquake data collected from the literature, ISC, USGS and MMD were
utilised for source modelling and the estimation of seismic hazard parameter
“b”. The b values for various zones from the SSZ and SFZ range between
0.56 and 1.06 and 0.57 and 1.03 with mean values of 0.82 and 0.89, respectively,
using the GR-Law. Suitable GMPEs were subsequently employed with the
assistance of a logic-tree structure for the SHA. Both DSHA and PSHA,
despite having different seismic source models and conducted using different
software (in-house Microsoft Excel based for DSHA and EZ-Frisk for PSHA),
conclude that the central-western cities (latitudes 2 to
4∘ N) of Peninsular Malaysia are most susceptible to high PGAs due
to their location closer to the seismically active Sumatran region. The DSHA
using “critical-worst” case indicated that the hazard across Peninsular
Malaysia on bedrock in terms of PGA ranges from 0.07 to 0.80 ms-2,
while hazard conducted using PSHA at PE for 10 % and 2 % in 50 years
(return periods of 475 and 2475 years, respectively) showed that the mean
PGA ranges from 0.11 to 0.55 ms-2 and from 0.20 to 1.02 ms-2,
respectively. Similarly, the combined results from both the SHA showed that
the hazard across the peninsula (especially below 5∘ N latitude)
was mostly contributed by the SFZ despite the latter being less active and
the limited energy it releases. However, it is worth mentioning that the
current work only focuses on the PGA at bedrock without taking into
consideration the spectral acceleration and soil amplifications. Hence, the
contribution of mega-earthquakes from the SSZ frequently associated with
long duration seismic waves should not be dismissed.
The absence of good seismic data (small database and short duration
activities) for the local intraplate events prevented the utilisation of
PSHA. Nevertheless, a simulated DSHA near the Bukit Tinggi fault at a
reasonable Mw5.0 predicted a PGA of approximately 0.40 ms-2 at
the centre of KL. The overall hazard from both deterministic and
probabilistic analyses, despite their differences, leads to similar results
and offers valuable information on the seismic ground motion experience
across the peninsula. Finally, the PGA values from SHA were lower than the
recommended values from the drafted Annex on the seismic resistant design
from the Department of Standards Malaysia (2016), which was adjusted based on
Eurocode 8, suggesting that the usage of the Annex, for now, is suitable
across the peninsula. However, revisiting the SHA procedure with a new set
of earthquake data set and improved approaches is recommended in future,
which defines the accuracy and reliability of the assessment procedure.
The data used for the analyses in Figs. 3 to 5 and Table
3 are publicly available on the USGS earthquake catalog
(https://earthquake.usgs.gov/earthquakes/search/). The seismic records
collected from the Malaysia Meteorological Department (MMD) and the analysis
conducted in this study using the EZ-Frisk software are available from the
corresponding author upon request. Please note that the EZ-Frisk software
used in this study was purchased as an educational package (or licence) from the
Fugro USA Land, Inc. Use of the data from MMD shall be subject to
approval from the Malaysia Meteorological Department as
appropriate.
DWL collected and analyzed the seismic data,
formulated the GMPEs, conducted DSHA and PSHA, and
drafted the manuscript; MER supervised the overall
work and provided input and suggestions on analyses
and outcomes; and VS contributed to the geological
understanding of the region and its application in
approaching the SHA.
The authors declare that they have no conflict of
interest.
Acknowledgements
The authors would like to acknowledge the financial support from the
Ministry of Higher Education Malaysia through the Fundamental Research Grant
Scheme (grant FRGS/2/2013/TK03/MUSM/03/2). We also thank the Advanced
Engineering Platform, Monash University Malaysia for the financial support
to cover the publication charges of this paper. The authors would also
like to acknowledge the Malaysian Meteorological Department for providing
the earthquake data and details pertaining to their measurement and the
seismic network of the nation. The contributions of Biswajeet
Pradhan, University of Technology Sydney (formerly of Universiti Putra Malaysia) and
Zainuddin Md. Yusoff, Universiti Putra Malaysia, in terms of suggestions
during the initial stages of earthquake data collection in this study is
highly appreciated.
Edited by: Maria Ana Baptista
Reviewed by: Chung-Han Chan and two anonymous referees
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