Author`s personal copy

Author's personal copy
J Seismol
DOI 10.1007/s10950-012-9340-5
ORIGINAL ARTICLE
Ground motion estimation in Delhi from postulated regional
and local earthquakes
Himanshu Mittal & Ashok Kumar & Kamal
Received: 25 July 2011 / Accepted: 17 October 2012
# Springer Science+Business Media Dordrecht 2012
Abstract Ground motions are estimated at 55 sites in
Delhi, the capital of India from four postulated earthquakes (three regional Mw 07.5, 8.0, and 8.5 and one
local). The procedure consists of (1) synthesis of
ground motion at a hard reference site (NDI) and (2)
estimation of ground motion at other sites in the city
via known transfer functions and application of the
random vibration theory. This work provides a more
extensive coverage than earlier studies (e.g., Singh et
al., Bull Seism Soc Am 92:555–569, 2002; Bansal et
al., J Seismol 13:89–105, 2009). The Indian code
response spectra corresponding to Delhi (zone IV)
are found to be conservative at hard soil sites for all
postulated earthquakes but found to be deficient for
Mw 08.0 and 8.5 earthquakes at soft soil sites. Spectral
acceleration maps at four different natural periods are
strongly influenced by the shallow geological and soil
conditions. Three pockets of high acceleration values
H. Mittal (*) : A. Kumar
Department of Earthquake Engineering,
Indian Institute of Technology,
Roorkee 247667 Uttarakhand, India
e-mail: [email protected]
A. Kumar
e-mail: [email protected]
Kamal
Department of Earth Sciences,
Indian Institute of Technology,
Roorkee 247667 Uttarakhand, India
e-mail: [email protected]
are seen. These pockets seem to coincide with the
contacts of (a) Aravalli quartzite and recent Yamuna
alluvium (towards the East), (b) Aravalli quartzite and
older quaternary alluvium (towards the South), and (c)
older quaternary alluvium and recent Yamuna alluvium (towards the North).
Keywords Delhi . EGF . Himalayas . Response
spectra . RVT
1 Introduction
India has a long history of earthquakes. In the last
50 years, the population of India has doubled and
resulted in very rapid growth of settlements, especially
in urban areas. Presently about 50 million people in
India, living in the Himalayan region and adjoining
plains, are at risk from earthquakes. During the last
200 years, the Indian peninsula has experienced several great earthquakes, namely: Kutch, Gujrat (1819),
M08.0; Assam earthquake (1897), M08.7; Kangra
earthquake (1905), M08.0; Bihar-Nepal earthquake
(1934), M08.3; and Assam earthquake (1950), M0
8.5. Most of the Himalayas comprising North India
and North eastern India are mapped as either seismic
zone IV or V in the seismic zonation map of India (on
a scale of II to V). Two of the recent significant earthquakes in North India are the Uttarkashi earthquake of
1991 (Mw 06.8) and Chamoli earthquake of 1999
(M w 06.5). The maximum intensity of Uttarkashi
Author's personal copy
J Seismol
earthquake was estimated IX (MSK scale) in an area
of 20 km2 in the epicentre zone while for Chamoli
earthquake it was VIII (Kayal 2007).
Despite being one of the seismically active regions
of the world, the strong motion (SM) data set in India
is, in general, very sparse. For this reason, the seismic
hazard in India, till now, has been estimated using
either the intensity data or attenuation relations derived for other regions (e.g., Khattri et al. 1984;
Krishna 1992; Sharma 1998; Nath et al. 2005;
Sharma et al. 2009). Therefore, in the absence of
adequate SM data, the ground motion can be synthesized from postulated earthquakes. Since this is the
key input in earthquake-resistant design, modeling of
the strong ground motion is a natural starting point.
Delhi lies approximately 200 and 300 km from
Main Boundary Thrust (MBT) and Main Central
Thrust (MCT), respectively—the most seismically active thrust planes of the Himalayas (Seeber and
Armbruster 1981). Delhi and its surroundings have
experienced many earthquakes since the ancient times
and considerable damage has been caused to civil
structures and human lives. According to Oldham
(1883), the first known earthquake in Delhi dates back
to AD 893, and Delhi is being shaken by the movements of dominant faults of the Himalayan region
such as the MBT and MCT (Fig. 1). Delhi also experiences local earthquakes, which may be attributed to
the prominent tectonic features in Delhi (Kamble and
Chaudhury 1979). Figure 2 shows the surface geology
of Delhi and the location of the stations from where
data were acquired. Delhi is underlain by old and more
recent alluvium except in the southern and central part
where the Alwar quartzite of the Delhi Super Group
rocks aligns in a NE-SW direction. The thickness of
the alluvium overlying the quartzite increases away
from the quartzite outcrops. The eastern part of Delhi
is covered by flood plains from the Yamuna River
which are overlain by unoxidized sand, silt, and clay.
The flood plains of the Delhi region are in particular
prone to high seismic hazard. Table 1 lists some of the
earthquakes that were felt in Delhi. Many studies
dealing with site effect, microzonation, estimation of
ground motion, and seismic hazard have been performed in Delhi (e.g., Khattri 1999; Iyengar 2000;
Singh et al. 2002; Mukhopadhyay et al. 2002; Nath
et al. 2003; Sharma et al. 2003; Parvez et al. 2004;
Iyengar and Ghosh 2004; Parvez et al. 2006; Mohanty
et al. 2007; Bansal et al. 2009; Mundepi et al. 2010;
Singh et al. 2010). Again, hardly any ground motion
data are available in Delhi except at the India
Meteorological Department (IMD) station at ridge
(NDI) (see Fig. 2 for location).
Given this scenario, a possible strategy to map
ground motion in the city from postulated earthquakes
is to estimate it first at a hard reference site and, then,
through known transfer functions for different sites
with respect the reference site, to compute ground
shaking at these sites through the application of random vibration theory. It is worth noting that, because
the synthesis approach is based on site response from
very small ground motion, it is only valid for linear
material response. Therefore, soil nonlinearity is not
taken into account in this study. Since at NDI, situated
on hard rock, digital recordings of local and regional
earthquakes are available, this site may be conveniently taken as the reference site. Singh et al. (2002) used
this approach to synthesize ground motions in Delhi
from postulated large/great earthquakes in the central
seismic gap of the Himalayan arc. The motions at NDI
were synthesized by random summation of empirical
Green’s functions (EGFs), and point-source and finitesource stochastic methods. The motions could be estimated at only three additional sites since these were
the only ones whose transfer functions with respect to
NDI could be computed. Using recordings of two
small, local earthquakes, Bansal et al. (2009) estimated transfer functions at five more sites in Delhi. They
found evidence that the transfer functions estimated
from sources at close and intermediate distances were
similar to those from far sources. These authors also
computed ground motions from a postulated Mw 05.0
earthquake in Delhi. The motions at the reference site
of NDI were synthesized using an EGF summation
technique introduced by Ordaz et al. (1995).
Since the local geological and top soil conditions show large variation in Delhi, it is critically
important to determine the transfer function of as
many sites as possible. Towards this goal, transfer
functions of 55 additional sites in Delhi have been
estimated (Mittal 2011). This was accomplished
by a deployment of portable seismographs in the
city, recording 13 local and regional earthquakes.
Three out of these 13 earthquakes were also
recorded by the Delhi SM instrumentation network deployed by Indian Institute of Technology,
Roorkee (Kumar et al. 2012). We take advantage
of these transfer functions to estimate response
Author's personal copy
J Seismol
Fig. 1 a Tectonic map of
the region (after Singh et al.
2002). Hatched areas
denote felt intensity greater
than or equal to VIII. The
segment between the rupture
areas of the 1905 and
Herrmann 1985 earthquakes
is known as the central
seismic gap. MCT main
central thrust, MBT main
boundary thrust. Locations
and focal mechanisms of
1991 Uttarkashi and 1999
Chamoli earthquakes are
shown. b A closer view of
the area in the box shown in
the top figure (modified
after Singh et al. 2002).
Earthquake symbols are
epicenters of 1991 and 1999
earthquakes. Target rupture
area is confined within MBT
and MCT, with center at
30° N, 79.2° E. Rectangle is
the rupture area of a
Mw 08.0 earthquake in the
region of the Chamoli. A–C
Possible epicenters.
Earthquake symbol in Delhi
area represents the local
earthquake Mw 04.1 used for
synthesis using Empirical
Green’s function technique
spectra of postulated Mw 07.5, 8.0, and 8.5 earthquakes
in the central seismic gap of the Himalayan arc,
and a local Mw 05.5 in Delhi. For input ground
motions at the reference site of NDI, we generated
time histories following the methods used by
Singh et al. (2002) and Bansal et al. (2009) for
the Himalayan arc earthquakes and local event,
respectively. Because of the substantially increased
number of transfer functions now available, this
work provides much more extensive coverage than
earlier studies and enables us to plot contours of
spectral acceleration.
Author's personal copy
J Seismol
Fig. 2 Simplified geological
map of Delhi (modified after
GSI 1963). Earthquake
symbol in Delhi represents
the epicentre of local
earthquake, Mw 04.1, used as
EGF. Yellow squares
represent permanent SM
instruments network in Delhi
where two earthquakes got
recorded. Green squares as
well as yellow squares
represent points where
transfer functions are
estimated. Rectangle A–D
represents the study area.
NDI, situated on hard rock,
is taken as the reference site
for synthesis
2 Time history of postulated earthquakes
at the reference site of NDI
As mentioned above, we generated the time histories
for the postulated earthquakes (Mw 07.5, 8.0, and 8.5)
in the central seismic gap of the Himalayan arc (Fig. 1)
and a local earthquake (Mw 05.5) in Delhi (Fig. 2)
using the same methods as employed by Singh et al.
(2002) and Bansal et al. (2009), respectively. For the
Himalayan arc earthquakes, Singh et al. (2002) used
an EGF summation scheme proposed by Ordaz et al.
(1995). This scheme obeys the ω2-source scaling at all
frequencies. The method requires specification of only
the seismic moment (M0) and the stress drops (Δσ) of
both the EGF and the target event. The seismogram of
the Chamoli earthquake (Mw 06.5), located in the
Himalayan arc (Fig. 1) and recorded at NDI, was used
as the EGF. From spectral analysis, Δσ of the
Author's personal copy
J Seismol
Table 1 Significant earthquakes
in and around Delhi which were
felt in the city (modified after
Bansal et al. 2009)
All distances are taken from
IMD ridge (NDI)
Date
Latitiude
(°N)
Longitude
(°E)
Magnitude
Region
Distance from
Delhi (km)
15 Jul 1720
28.37
77.10
6.5
Delhi
27
01 Sep 1803
27.50
77.70
6.8
Mathura
129
16 Jan 1842
27.00
78.00
5.0
Near Mathura
192
10 Oct 1956
28.15
77.67
6.7
Near Bulandshahar
65
27 Aug 1960
28.20
77.40
6.0
Near Faridabad
46
15 Aug 1966
28.67
78.93
5.8
Near Moradabad
167
20 Oct 1991
30.75
78.86
6.6
Uttarkashi
305
29 Mar 1999
30.49
79.29
6.5
Chamoli
280
26 Nov 2007
28.68
77.20
4.2
Delhi Metropolitan
15
earthquake was estimated at 60 bar. In the synthesis of
ground motion at NDI, Δσ for both the EGF and the
target events were assumed to be the same, namely 60
bar. Singh et al. (2002) also used a stochastic method
assuming a point source (Boore 1983) and a finite
source (Beresnev and Atkinson 1997, 1998, 1999,
2001). In the application of the point-source stochastic
method, Δσ was also fixed to 60 bar. The finite-source
method requires a free parameter, called the strength
factor, which controls the high-frequency radiation. A
“standard earthquake” was assumed and, following the
suggestion of Beresnev and Atkinson (1997,1998), this
factor was set at 1.0. Other parameters required in the
stochastic simulation are discussed in Singh et al.
(2002). For Mw ≤7.5, the ground-motion predictions at
NDI from the EGF and the two stochastic methods were
similar (Singh et al. 2002). This estimation of ground
motion is based on the assumptions that the source
follows ω2 scaling in the far field irrespective of the
size of the earthquake and that the point source approximation is valid (i.e., the source dimension and the
wavelength of interest are smaller than the distance to
the observation). Since for Mw >7.5 the point-source
approximation is grossly violated, for postulated large/
great earthquake in the Himalayan arc, we chose the
time histories generated from the finite-source stochastic
model. In finite-source calculations, the fault was approximated by a rectangle (assuming that the width (W)
of the fault along Himalayan belt does not exceed
80 km) situated between MBT and MCT, centered at
30° N and 79.2° E (Fig. 1). The center of the fault is at a
depth of 16 km.
The strike and the dip of the fault have been taken
as 300° N and 7°, respectively. Simulations were
performed for three hypocenter locations: the northeast
edge of the fault, the center of the fault, and the center of
the downdip edge of the fault (points A, B, and C in
Fig. 1b).With width fixed at 80 km, the length of the
fault was computed from the relation M0log (LW)+4.0,
where L and W are length and width in kilometers (Wyss
1979; Singh et al. 1980). The expected L×W for Mw 0
7.5, 8.0, and 8.5 are 56×56, 125×80, and 400×80 km,
respectively. The simulated horizontal traces at NDI for
Mw 07.5, 8.0, and 8.5 are shown in Fig. 3a. The
corresponding velocity time histories for Mw 07.5, 8.0,
and 8.5 are shown in Fig. 4.
The accelerograms at NDI of a postulated Mw 05.5
in Delhi were synthesized using the EGF technique
mentioned above. We used the recording at NDI of the
local earthquake from 25 Nov 2007 (28.57° N and
77.1° E; depth030.0 km; Mw 04.1) as the EGF and
used Δσ0130 bars for both the EGF and the target
events. Figure 3b illustrates a synthesized N–S accelerogram of the postulated earthquake.
3 Transfer functions
Transfer functions at 55 different sites in Delhi with
respect to NDI were established from 13 earthquakes
(regional as well as local) using the standard spectral
ratio method (Borcherdt 1970). Most of these data
were collected by Wadia Institute of Himalayan
Geology during a period of 3 months during which
broad band seismometers in different locations in
Delhi were deployed and recorded 11 earthquake
events. Using these data, transfer functions with respect to NDI were generated at 39 locations in Delhi
(Mittal 2011). Transfer functions at the remaining 16
locations out of 55 were established using records of
Author's personal copy
J Seismol
Fig. 3 a The synthesized
acceleration time history at
NDI for Mw 07.5, 8.0, and
8.5 earthquake in Himalaya.
b Synthesized acceleration
time history for Mw 05.5
local earthquake (note the
scale difference in y-axis)
earthquakes of 27 Nov 2007 and 24 Feb 2010 recorded
by SM accelerographs installed by IIT Roorkee, CBRI,
Roorkee, and IMD, Delhi (Mittal 2011).
Fig. 4 The synthesized velocity time history at NDI for Mw 0
7.5, 8.0, and 8.5 earthquake in Himalaya
If the reference site has a non-negligible site
response, then the spectral ratios become relative
site-response estimates or site amplification factor.
In the present study, the Ridge observatory New
Delhi (NDI) is considered to be the reference site.
The Ridge Seismological Observatory in Delhi was
established in 1960 by the IMD. In 1963, it was
upgraded and became part of the World-Wide
Seismological Station Network (Bansal et al.
2009).The results show significant variations in
amplification factor from one place to another.
Higher amplification of the order of 30 is observed
at both banks of Yamuna river. At these sites,
amplification of the order of 15–25 is observed
at frequencies ranging between 0.5 to 2 Hz, indicating the presence of thick recent alluvium. At
sites falling toward West of Delhi, a moderate
amplification of the order of 10 to 15 is observed.
The sites having amplification above 10 are
grouped as soft soil sites. However, at some sites
much less amplification is observed.The amplication at these sites is found to range between 3 and
8 at a predominant frequency of 3 Hz, and higher
at stations falling on or near quartzite formations,
indicating the presence of very thin soil cover.
These sites are referred as rock sites or stiff soil
sites. Figure 5 shows the transfer function estimated at two different sites.
Author's personal copy
J Seismol
Fig. 5 Median Transfer
function with±standard
deviation. a Soft soil site; b
hard rock site. Transfer
function at 55 sites in Delhi
with respect to NDI were
established from 13
earthquakes using standard
spectral ratio
4 Prediction of ground motion from future
earthquakes
After the synthesis of acceleration time histories at the
reference site (NDI), the ground motions at 55 sites in
Delhi were estimated by using the transfer functions of
those sites based on the study by Mittal (2011). The
transfer functions were estimated using the spectral
ratio of the motion on a sediment site to the motion
on rock site, the latter considered as the reference one.
Thirteen recorded earthquakes at different locations in
Delhi were analyzed for estimation of site effects. An
alternative to conventional site response analysis is
random vibration theory (RVT) approach. RVT-based
site response is an extension of stochastic groundmotion simulation procedures developed by seismologists to predict peak ground-motion parameters as a
function of earthquake magnitude and site-to-source
distance and has been successfully applied in predicting various earthquake ground-motion parameters
(Hanks and McGuire 1981; Boore 1983; Toro and
McGuire 1987; Ordaz et al. 1988; Singh et al. 1989).
In this method, the ground motion is modeled as a
band-limited, finite-duration white Gaussian noise.
The spectrum of the ground motion is related to the
root mean square (RMS) amplitude in the time domain
through the Parseval's theorem. The expected peak
amplitude is obtained from the RMS amplitude using
equations of RVT (Cartwright and Longuet-Higgins
1956). For an ω2-source model, the source spectra of
an earthquake is completely specified by its seismic
moment and the stress parameter. RVT analysis can
provide an estimate of the site response without the
need to choose any input time histories for analysis.
The method has previously been applied for ground-
motion prediction in India by Singh et al. (1999, 2002,
2003, 2007) and Bansal et al. (2009).
We use RVT to estimate ground motions in Delhi
during future postulated earthquakes in the region.
The sources in Delhi region are assumed to follow
the ω2-source scaling in the far-field and the pointsource approximation is valid. We estimate the horizontal response spectra (Sa) with 5 % damping at
different sites in Delhi during various postulated
earthquakes.
Estimated Sa at all sites known to have soft soil are
grouped together and compared with Indian Standard
code IS 2002spectra for soft soil with zero period
acceleration of 0.24 g (type III) (Fig. 6a, d).
Similarly, estimated Sa at all sites known to have stiff
soil are grouped together and compared with IS1893
(Part 1) (2002) spectra of type I (Fig. 7a, d). IS 1893–
2002 is the Indian standard code for earthquakeresistant design of structures and this code provides
response spectra with unity zero-period acceleration
(ZPA) for three types of soil. This spectrum is required
to be multiplied by a factor which depends upon
maximum credible earthquake/design earthquake,
seismic zone, and reduction factor. In our case, we
have taken a ZPA of 0.24 g which correspond to the
maximum credible earthquake for seismic zone IV
having importance factor and reduction factor of unity.
Figure 6 shows that the zone IV response spectrum
is exceeded at several frequencies on all soft sites for
Mw 08.0 and 8.5 earthquakes from Himalayas while
for Mw 07.5 earthquake from the Himalayas and as for
the Mw 05.5 earthquake near Delhi it is almost in
agreement (except at some frequencies). For all the
rock sites, the zone IV response spectrum is conservative as evident from Fig. 7a, d.
Author's personal copy
J Seismol
Fig. 6 Comparison of
response spectra at soft soil
sites from Mw 07.5, 8.0, and
8.5 earthquake in Himalaya
and Mw 05.5 earthquake
near Delhi with design
response spectra for zone IV
of the Indian seismic zoning
map (IS1893: IS1893
Part 1 2002) valid for Delhi.
(Note: y-axis scale varies
in each plot)
a
b
7.5 magnitude
c
8.0 magnitude
d
8.5 magnitude
These results are combined to draw Sa maps of
Delhi corresponding to 0.3 and 1 s periods (5 % damping) as shown in Figs. 8, 9, 10, and 11. The spectral
acceleration maps are strongly influenced by the shallow geological and soil conditions as indicated by the
contours. When considering the effects of geological
conditions on the ground motion and response spectra,
the widely accepted method of reflecting these effects
is to classify the recording stations according to the
shear-wave velocity (VS) profiles of their subsoil in
the upper 30 m (Boore et al. 1997). But for most
stations in Delhi, reliable VS values and detailed site
descriptions are not available. For that reason, we
estimated qualitatively the site classification for those
stations by analogy with information for similar geologic materials and the estimated site effects. The type
of geologic material underlying each recording site
was obtained from various local geologic maps available for Delhi and the amplification estimated using
SSR. Based on these qualitative data, we used a
5.5 magnitude
general classification of site geology that we could
apply uniformly. Three pockets of high acceleration
values are seen due to both regional and local earthquakes. These pockets coincide with the boundaries of
different geological formations and hence are indicative that the high spectral acceleration may be associated with the contact of soil/geological formations
rather than the formation itself. The change in spectral
acceleration may be attributed to different factors such
as impedance contrast (where waves increase in amplitude to carry same amount of energy when they
pass from hard rock to soft soil), resonance (matching
of signal frequency with fundamental frequency or
higher harmonics of the soil layer), trapping of waves
due to damping, etc. From the spectral maps, the
following is observed:
a) Pockets of potentially high seismic hazard are
present in southern and eastern outskirts of Delhi
city.
Author's personal copy
J Seismol
Fig. 7 Comparison of
response spectra at hard rock
(soil) sites from Mw 07.5,
8.0, and 8.5 earthquake in
Himalaya and Mw 05.5
earthquake near Delhi with
design response spectra for
zone IV of the Indian
seismic zoning map
(IS1893: IS1893 Part 1
2002) valid for Delhi
a
b
7.5 magnitude
c
8.0 magnitude
d
8.5 magnitude
b) The high spectral acceleration in the eastern region is mainly due to thick Yamuna alluvium, but
high spectral accelerations observed in southern
Delhi may be due to contact between Aravalli
quartzite and older quaternary alluvium.
c) At high frequencies (0.3 s), the levels of spectral
acceleration in Delhi are similar for M w 08.0
earthquake in Himalayas and M w 05.5 earthquakes near Delhi. However, at low frequency,
for a near field earthquake, the level of Sa is not
as large as that due to Mw 08.0 earthquake in
Himalaya.
5 Summary and conclusions
The fundamental in seismic hazard is to predict the
ground motion at a given site from future possible
earthquakes. The ideal solution to this problem could
5.5 magnitude
be to use a wide database of recorded strong ground
motion records and to group those accelerograms that
have similar source, path, and site effects. However,
such database will not be generally available for most
sites. Delhi in India is one mega city where such
studies are of prime importance due to its high population and vicinity to earthquake prone Himalayas. Seismic
instrumentation in Delhi has been relatively sparse, however, 20 SM instruments by IIT Roorkee in recent time
(Kumar et al. 2012) are providing good data set. As a
consequence, the knowledge of local and regional seismicity, geotechnical properties and local site effects
remains poor. In this work, an attempt has been made
to understand and quantify seismic hazard of different
parts of Delhi from local and regional earthquakes.
A procedure has been applied for seismic hazard estimation making use of (1) synthesized
strong ground motion based on recorded earthquakes at a reference site, (2) transfer functions
Author's personal copy
J Seismol
Fig. 8 Sa (in galloons)
contour maps of Delhi for
Mw 07.5 earthquake (closest
distance, ∼250–300 km)
from Himalayas at different
periods, showing change in
spectral acceleration from
one place to another. All
studies are confined to the
rectangle A–D in Fig. 2
0.3 Sec
(with respect to the reference site) at 55 sites in
Delhi, (3) and finally RVT to estimate response
spectrum at each site. With this method, response
spectra for each site were estimated for magnitudes
7, 7.5, and 8 earthquakes originating from
Himalayas and magnitude 5.5 earthquake originating near Delhi. This work revealed relatively
1.0 Sec
higher vulnerability zones in Delhi in small pockets due to local site conditions and prevailing
topography. These pockets seem to coincide with
the contacts of (a) Aravalli quartzite and recent
Yamuna alluvium (towards the East), (b) Aravalli
quartzite and older quaternary alluvium (towards
the South), and (c) older quaternary alluvium and
Fig. 9 Sa (in galloons)
contour maps of Delhi for
Mw 08.0 earthquake (closest
distance, ∼250–300 km)
from Himalayas at different
periods, showing change in
spectral acceleration from
one place to another
0.3 Sec
1.0 Sec
Author's personal copy
J Seismol
Fig. 10 S a (in galloons)
contour maps of Delhi for
Mw 08.5 earthquake (closest
distance, ∼250–300 km)
from Himalayas at different
periods, showing change in
spectral acceleration from
one place to another
0.3 Sec
recent Yamuna alluvium (towards the North).
Based on this study, we can say that within
Delhi, ground motion will have substantial variation from place to place and site-specific estimation of ground motion must be used for each site.
We estimated ground motion at different sites using actual earthquake recordings (for synthesis at
reference site as well as transfer functions) and
therefore this estimation is expected to be more
1.0 Sec
reliable in comparison to analytical approaches in
which several assumptions of model and material
properties are used.
Delhi, with a population of more than 10 million inhabitants is a fast growing city. Study of
seismic hazard and preparation of contour maps of
ground motion amplitude will provide an effective
solution for city planning and input to earthquake
resistant design of structures in Delhi. Such maps
Fig. 11 S a (in galloons)
contour maps of Delhi for
Mw 05.5 earthquake near
Delhi at different periods,
showing change in spectral
acceleration from one place
to another
0.3 sec
1.0 sec
Author's personal copy
J Seismol
will provide general guidelines for integrated planning of cities and in sitting new structures that are
more suited to an area, along with information on
the relative damage potential of the existing structures in a region.
Our studies are based on limited data and,
hence, are necessarily preliminary. Since most of
the transfer functions used herein are first-order
approximations determined from local earthquakes,
these need to be validated in the future using more
earthquake data. Also, multiple readings would be
desirable at all sites. Detailed studies need to be
undertaken for soil investigation at all the sites.
This will help in determining the local site effects
more accurately for assessment of amplification of
ground motion. We also recommend establishing a
dense seismic network in and around Delhi to
generate the database so as to verify and improve
the site specific studies for prediction of ground
motion.
Acknowledgments The authors are profusely thankful to
Prof. S.K. Singh, Instituto de Geofisica, Universidad, Nacional
Autonoma de Mexico (UNAM) for providing the codes of his
program and helping us throughout this work. He deserved to be
a coauthor of this MS, but we exclude him on his insistence. We
are also very thankful to editor-in-charge, for his valuable revisions and comments, which helped us to clarify and improve
this paper. Comments from all anonymous reviewers helped in
improving manuscript. The author (HM) thankfully acknowledges Dr. Arjun Kumar, IIT Roorkee for his support.
References
Bansal BK, Singh SK, Dharmaraju R, Pacheco JF, Ordaz M,
Dattatrayam RS, Suresh G (2009) Source study of two
small earthquakes of Delhi, India, and estimation of ground
motion from future moderate, local events. J Seismol
13:89–105
Beresnev IA, Atkinson G (1997) Modelling finite fault radiation
from the ωn spectrum. Bull Seism Soc Am 87:67–84
Beresnev IA, Atkinson G (1998) FINSIM—a FORTRAN program for simulating stochastic acceleration time histories
from finite faults. Seism Res Lett 69:27–32
Beresnev IA, Atkinson G (1999) Generic finite-fault model for
ground motion prediction in eastern North America. Bull
Seism Soc Am 89:608–625
Beresnev IA, Atkinson G (2001) Subevent structure of large
earthquakes—a ground-motion perspective. Geophys Res
Lett 28:53–56
Boore DM (1983) Stochastic simulation of high-frequency
ground motions based on seismological models of the
radiated spectra. Bull Seism Soc Am 73:1865–1884
Boore DM, Joyner WB, Fumal TE (1997) Equations for estimating horizontal response spectra and peak acceleration
from Western North American earthquakes: a summary of
recent work. Seismol Res Lett 68(1):128–153
Borcherdt RD (1970) Effects of local geology on ground motion
near San Francisco Bay. Bull Seism Soc Am 60:29–61
Cartwright DE, Longuet-Higgins MS (1956) The statistical distribution of the maxima of a random function. Proc Roy
Soc London Ser A237:212–232
GSI (1963) Tectonic and Geological Maps of India. Geological
Survey of India, Calcutta.
Hanks TC, Mcguire RK (1981) The character of high-frequency
strong ground motion. Bull Seism Soc Am 71:2071–2095
Herrmann RB (1985) An extension of random vibration theory
estimates of strong ground motion at large distances. Bull
Seism Soc Am 75:1447–1453
IS1893 (Part 1) (2002) Indian standard criteria for earthquake
resistant design of structures, part 1, general provisions and
buildings (Fifth Revision). Bureau of Indian Standards,
New Delhi 110002, 42 pp
Iyengar RN (2000) Seismic status of Delhi megacity. Curr Sci
78(5):568–574
Iyengar RN, Ghosh S (2004) Microzonation of earthquake hazard in greater Delhi area. Curr Sci 87:1193–1202
Kamble VP, Chaudhary HM (1979) Recent seismic activity in
Delhi and neighborhood. Mausam 30:305–312
Kayal JR (2007) Recent large earthquakes in India: seismotectonic perspectives. IAGR Memoir No 10:189–199
Khattri KN (1999) An evaluation of earthquakes hazard and risk
in northern India. Himal Geol 20:1–46
Khattri KN, Rogers AM, Perkins DM, Algermissen ST (1984)
A seismic hazard map of India and adjacent areas. Tectonophysics 108:93–134
Krishna J (1992) Seismic zoning maps of India. Curr Sci 62:17–23
Kumar A, Mittal H, Sachdeva R, Kumar A (2012) Indian Strong
Motion Instrumentation Network. Seismol Res Lett 83
(1):59–66
Mittal H (2011). Estimation of ground motion in Delhi. Ph.D.
thesis, Indian Institute of Technology, Roorkee.
Mohanty WK, Walling MY, Nath SK, Pal I (2007) First order
seismic zonation of Delhi, India using geographical information system (GIS). Nat Hazard 40:245–260
Mukhopadhyay S, Pandey Y, Dharmaraju R, Chauhan PKS,
Singh P, Dev A (2002) Seismic microzonation of Delhi
for ground-shaking site effects. Curr Sci 87:877–881
Mundepi AK, Galiana-Merino JJ, Kamal LC (2010) Soil characteristics and site effect assessment in the city of Delhi
(India) using H/V and f–k methods. Soil Dyn Earthq Eng
30:591–599
Nath SK, Sengupta P, Srivastav SK, Bhattacharya SN,
Dattatrayam RS, Prakash R, Gupta HV (2003) Estimation of S-wave site response in and around Delhi
region from weak motion data. Proc Indian Acad Sci
(Earth Plant Sci) 112:441–463
Nath SK, Vyas M, Pal I, Sengupta P (2005) A seismic hazard
scenario in the Sikkim Himalaya from seismotectonics,
spectral amplification, source parameterization and spectral
attenuation laws using strong motion seismometry. J Geophsy Res-Sol Ea 110(B1):1–24
Oldham T (1883) A catalogue of Indian earthquakes. Mem Geol
Surv India Geol Surv India 19:163–215
Author's personal copy
J Seismol
Ordaz M, Arboleda J, Singh SK (1995) A scheme of random
summation of an empirical Green’s function to estimate
ground motions from future large earthquakes. Bull Seism
Soc Am 85:1635–1647
Ordaz M, Singh SK, Reinoso E, Lermo J, Espinoza JM,
Dominguez T (1988) Estimation of response spectra in
the lake bed zone of the Valley of Mexico. Earthquake
Spectra 4:815–834
Parvez I, Vaccari F, Panza GF (2004) Site-specific microzonation study in Delhi metropolitan city by 2-D modeling of
SH and P-SV waves. Pure Appl Geophys 161:1165–1185
Parvez IA, Vaccari F, Panza GF (2006) Influence of source
distance on site-effect in Delhi city. Curr Sci 91:827–
835
Seeber L, Armbruster JG (1981) Great detachment earthquakes
along the Himalayan arc and long-term forecasting, in
earthquake prediction: an international review, vol 4,
Maurice Ewing Series. American Geophysical Union,
Washington, DC, pp 259–277
Sharma ML (1998) Attenuation relationship for estimation of
peak ground horizontal acceleration using data from
strong-motion arrays in India. Bull Seism Soc Am 88
(4):1063–1069
Sharma ML, Douglas J, Bungum H, Kotadia J (2009) Groundmotion prediction equations based on data from the Himalayan and Zagros Regions. J Earthquake Eng 13:1191–
1210
Sharma ML, Wason HR, Dimri R (2003) Seismic zonation of
the Delhi region for bedrock ground motion. Pure Appl
Geophys 160:2381–2398
Singh SK, Bazan E, Esteva L (1980) Expected earthquake
magnitude from a fault. Bull Seism Soc Am 70:903–914
Singh SK, Ordaz M, Dattatrayam RS, Gupta HK (1999) A
spectral analysis of the 21 May 1997, Jabalpur, India,
earthquake (Mw 05.8) and estimation of ground motion
from future earthquakes in the Indian shield region. Bull
Seism Soc Am 89:1620–1630
Singh SK, Mohanty WK, Bansal BK, Roonwal GS (2002)
Ground motion in Delhi from future large/great earthquakes in the central seismic gap of the Himalayan arc.
Bull Seism Soc Am 92:555–569
Singh SK, Pimprikar SD, Bansal BK, Pacheco JF, Dattatrayam
RS, Suresh G (2007) An analysis of the Mw 4.7 Jabalpur,
India, earthquake of 16 October 2000: toward ground
motion estimation in the region from future events. Bull
Seism Soc Am 97(5):1475–1485
Singh SK, Ordaz M, Anderson JG, Rodriguez M, Quaas R,
Mena E, Ottaviani M, Almora D (1989) Analysis of nearsource strong-motion recordings along the Mexican subduction zone. Bull Seism Soc Am 79:1697–1717
Singh SK, Kumar A, Suresh G, Ordaz M, Pacheco JF, Sharma
ML, Bansal BK, Dattatrayam RS, Reinoso E (2010) Delhi
earthquake of 25 November 2007 (Mw 4.1): implications
for seismic hazard. Curr Sci 99:939–947
Singh SK, Bansal BK, Bhattacharya SN, Pacheco JF,
Dattatrayam RS, Ordaz M, Suresh G, Kamal, Hough SE
(2003) Estimation of ground motion for Bhuj (26th January
2001. Mw 07.6) and for future earthquakes in India. Bull
Seism Soc Am 93(1):353–370
Toro GR, McGuire RK (1987) An investigation into earthquake
ground motion characteristics in the eastern North America.
Bull Seism Soc Am 77:468–489
Wyss M (1979) Estimating maximum expectable magnitude of
earthquakes from fault dimensions. Geology 7:336–340