INVESTIGATION OF SITE RESPONSE IN
KATHMANDU VALLEY USING AFTERSHOCK DATAOF THE 2015 GORKHA EARTHQUAKE, NEPAL
Naresh MAHARJAN
MEE17704

Supervisor:

Toshiaki YOKOI
Takumi HAYASHIDAABSTRACT
We used 16 aftershock recordings of the 2015 Gorkha earthquake (Mw 7.8) at three temporary stations
inside the Kathmandu Valley to derive earthquake H/V spectral ratios (EHVSRs) and compare thosewith microtremor H/V spectral ratios (MHVSRs) at same stations. The predominant frequency peaks of
EHVSRs and MHVSRs are comparable at three continuous observation stations with three-component
accelerometers; NAKRM (0.6 Hz), NANST (0.32 Hz) and NABKT (2.2 Hz) suggesting that
accelerometers can sometimes be used to investigate response of the deep sedimentary basin. We also
derived MHVSRs using microtremor data at 11 temporary sites inside the Kathmandu Valley to obtaindominant frequencies. Next we estimated the thickness of sedimentary layer by calculating theoretical
H/V ratio with simplified four-layered velocity model and compared them with the derived MHVSRs.
The estimations indicate that the thickness of sedimentary layer ranges from 42 to 700 m. We also
investigated amplification and attenuation property of S-waves inside Kathmandu using threeearthquakes; one teleseismic event and two aftershocks of the 2015 Gorkha earthquake. Observed dataclearly showed that the basin amplifies ground motion in the low frequency range (0.1 – 1.0 Hz), while
high-frequency ground motion (>1 Hz) do not rapidly decay in the Kathmandu Valley.
Keywords: H/V spectral ratio, Site Amplification, Kathmandu Valley, Microtremor, Gorkha
Earthquake aftershocks.

1. INTRODUCTION
The Kathmandu Valley is an intermountain basin located in the Central Nepal, Lesser Himalaya. The
basin was a paleo-lake and sediments were deposited from the surrounding mountains and haveestimated the thickness of sedimentary layer more than 600 m (Moribayashi and Mauro, 1980). It has
an EW elongation of 30 km and NS elongation of 25 km with an average altitude of 1340 m. The Gorkha
earthquake (Mw 7.8) occurred on April 25, 2015 took 8979 lives and destroyed 610720 houses in whole
Nepal (1,739 lost their lives and about 13% of buildings were completely damaged in the Kathmandu
Valley). Five strong-motion sensors were deployed inside Kathmandu Valley before the mainshock data
and the data showed the long-period ground motion with dominant period at 4 to 5 s (Bhattarai et al.,2015; Takai et al., 2016; Dhakal et al., 2016). Also some researchers estimated 1D S-wave velocity
structure models using aftershock data inside Kathmandu Valley and found clear velocity contrast
between sedimentary layer and bed rock.

2. METHODOLOGY

Department of Mines & Geology (DMG), Nepal.
International Institute of Seismology and Earthquake Engineering, Building Research Institute, Japan.

12.1. H/V Spectral Ratios
Microtremor is defined as a very weak motion generated by natural phenomena and human activities.
Kanai and Tanaka (1954) discovered that the dominant frequency of microtremor corresponds to theresonant frequency of subsurface soil. Later, Nakamura (1988) proposed that MHVSR could be a
responsible tool to estimate site amplification factor. The similarity and relationship between MHVSR
and EHVSR have been discussed over a long period of time. In this study, we used both earthquake andmicrotremor recordings to identify dominant frequencies of observation sites in Kathmandu Valley andcompare EHVSRs and MHVSRs at the same sites. The spectral ratios are derived as follows.
𝐻
𝑁𝑆 2 (𝑓) + 𝐸𝑊 2 (𝑓)( ) (𝑓) = √𝑉 𝑆
𝑈𝐷 2 (𝑓),(1)where NS, EW represents the amplitude of horizontal components and UD represents the amplitude ofvertical component.
2.2. Theoretical H/V Spectral Ratios
Assuming that the fundamental-mode Rayleigh wave is dominant in the data set, the theoretical H/V
spectral ratio is expressed as follows (Arai and Tokimatsu, 2004).
(𝐻 ⁄𝑉 ) 𝑆 (𝜔) = √𝑃𝐻𝑅 (𝜔)𝑃𝑉𝑅 (𝜔),(2)where PHR and PVR denotes the horizontal and vertical power of the observed surface waves in thefrequency domain. Based on existing estimated 1D velocity structure models in Kathmandu Valley(Bhattarai et al., 2017; Dhakal et al., 2016), we constructed simplified velocity structure models thatconsists of two equivalent-thickness subsurface layers (Vs < 1000m/s) to estimate the depth of basement
layer (Vs = 2200 m/s)

3. DATA

Table 1. Earthquake event list from the IRIS Catalog.

ML Local Depth
A research team of Oregon StateNo.
Time yyyymmdd
Location
Catalog(km)
University (OSU), the United
1
20:04 20150703
27.72S, 84.91E
4.5
3.36
States, installed 47 temporary2
15:29 20150706
27.94S, 85.69E
4.2
10
seismic stations in collaboration
3
1:37
20150801
28.53S, 85.68E
4.0
10with Department of Mines &4
13:02 20150814 27.57N, 86.07E
4.6
10
Geology (DMG) from June 2015 to5
18:11 20150815 27.70N, 85.95E
4.7
10
May 2016 to study the aftershock6
22:23 20150817 27.90N, 86.13E
4.2
10
7
13:17 20150830 27.65N, 85.66E
4.5
10
sequence in Nepal. There were three8
14:52 20150916 27.59N, 86.19E
4.4
10
observation stations with three9
6:32
20150922
27.69S, 85.06E
4.2
7.48
component accelerometers inside
10
10:54 20150929
27.77S, 85.82E
4.1
16.02
the Kathmandu Valley (stations
11
7:33
20151005
27.64S, 86.03E
4.5
10
NAKRM, NANST & NABKT) and12
12:02 20151006 27.77N, 86.15E
4.0
10
16 aftershocks recorded during the13
23:30 20151006 27.67N, 85.98E
4.3
14.04
observation period (4 ≤ ML ≤ 5.2;
14
4:15
20151119 27.85N, 85.68E
5
16.51
see Table 1). We retrieved the15
19:22 20160121 28.02N, 85.05E
4.5
19.01
aftershock event data from the16
16:20 20160205 27.84N, 85.25E
5.2
25.95
website of Incorporated ResearchInstitution for Seismology (IRIS-DMC; https://ds.iris.edu/ds/nodes/dmc/forms/breqfast-request/),2

which were uploaded by International Federation of Digital Seismograph Networks (FDSN);
http://www.fdsn.org/networks/detail/XQ_2015/). Continuous observations were conducted at the threesites and microtremor recordings are also available, so that we retrieved MHVSRs. A Japanese researchteam also conducted single-point microtremor observations with a three-component portableaccelerometer at 11 sites inside the Kathmandu Valley five month after the mainshock (Yamada et al.,2017) and we used a part of the data.

4. RESULTS
4.1. EHVSRs and MHVSRs at
three continuous observation
stations
We used 16 aftershocks (Table 1)
for calculations of EHVSRs. We
retrieved 40.96 s time window
from the S-wave onsets andcalculated the EHVSRs at three
Figure 1. Derived EHVSRs from 16 aftershock events. Thin black
stations NAKRM, NANST andcurves denote the H/V spectral ratios from individual event and
NABKT for all recorded
red curves denote the averaged ratios.
earthquakes. The result detectsthat stations NAKRM and NANST has a dominant peak at 0.59 and 0.29 Hz respectively in the lowfrequency range and station NABKT shows a peak at 2.2 Hz in the high-frequency range (Figure 1).
We also used continuous one-day microtremor data recorded at the same stations in different
seasons. We divided the one-day microtremor data into 1-hour segments and derived MHVSRs together
with noise Power Spectrum Density (PSD) to quantify the seismic background noise (McNamara andBuland, 2004). The MHVSR results indicated that stations NAKRM and NANST had a dominant peak
at 0.6 and 0.27 Hz and station NABKT shows a peak at 2.2 Hz (Figure 2). This implies that
accelerometers can be sometimes used for long-period microtremor survey as a substitute for longperiod seismometers, when the amplitude microtremor islarger (especially in winter season). The comparisons
between peak frequencies of EHVSRs and MHVSRs
show linear relation to all three stations (Figure 3).

Figure 2. Derived MHVSR at NABKT station. This
station shows peak frequency at 2.2 Hz and large
PSD in short period range (< 1s).

Figure 3. Comparison between dominantfrequencies of EHVSR and MHVSR at
stations NANST, NAKRM, NABKT.

4.2. MHVSRs at 11 temporary microtremor observation sites
We used single station microtremor survey data at 11 temporary stations inside the Kathmandu Valley(Yamada et al., 2017) and derived MHVSRs. The derived results showed dominant frequency peak
range from 0.22 to 4.17 Hz (see an example in the Figure 4).

3

4.3. Estimated bedrock depth
The existing velocity models of the
Kathmandu Valley; one simplified
velocity structure model by Bhattarai etal., (2017) and another by Dhakal et al.,(2016) were used to calculate thetheoretical H/V spectral ratio. Comparing
these theoretical H/V spectral ratio withthe microtremor H/V peaks at three
Figure 4. Derived MHVSR at Site10 station. The sitestations from OSU and 11 sites from Jshows the peak 0.28 Hz.
RAPID microtremor study, we found thedifference of bedrock depth between these two reference models (see Figures 5 and 6).

Figure 5. Map showing the depth at each station Figure 6. Map showing the depth at each station
using the simplified model of Bhattarai et al., using the simplified model of Dhakal et al.,(2016).
(2017).

5. DISCUSSIONS
5.1. Seismic wave
Kathmandu Valley

amplification

insideTo investigate the attenuation characteristics ofhigh frequency seismic waves along the
propagation
directionandlow-frequency
amplification inside the basin, we selected three
events; two aftershocks, Gorkha (4.6 ML) andDolakha (4.5 ML) and one distant Afghanistan
earthquake i.e. Hind-Kush (7.5 Mw). NANAT and
NAKRM stations inside the Kathmandu Valley
show high amplification in the lower frequency
range for the distant event (epicentral distance ofabout 1600 km and station-to-station distance isalmost 100 km). The dominant frequencies were0.27 Hz at NANST and 0.57 Hz at NAKRM (Figure7) corresponding well to those of the derived H/Vratios.

4

Figure 7. Observed Fourier spectra at five OSU
stations. Two stations inside the basin(NAKRM, NANST) show large amplificationin the low frequency range (0.1 to 1.0 Hz).

5.2. Seismic wave attenuation inside and outside of Kathmandu basin
Some researchers pointed out that high-frequency component seismic waves are significantly attenuatedor scattered in the basin, while the low frequency components (< 0.1 to 1 Hz) are amplified. In order toprove whether the high-frequency component significantly attenuates or not, we selected two local
earthquakes; Gorkha (mainshock area) and Dolakha (large aftershock area) and checked the attenuationof seismic waves along the propagation direction towards the Kathmandu basin. For this purpose, weestimated the theoretical amplitude spectra and compare them with observed spectra.
The following equation (Boore, 1983) was used to calculate the source effect.
𝑆𝑎 (𝑓) = 𝑅𝜃∅ . 𝑃𝑅𝑇𝐼𝑇𝑁 .

𝜋 𝑀𝑜
.
𝜌 𝑉𝑠3

𝑓2
𝑓 2
1+( )
𝑓𝑐

,

(3)

where RƟø is the radiation pattern, PRTITN is the partitioning factor to convert the energy into two
horizontal components, Mo is the seismic moment, ρ is the density around the source, Vs is the S-wave
velocity around the source and fc is the corner frequency. To obtain seismic moment of two earthquakes,
we used the Moment tensor inversion program developed by Yagi & Nishimura (2011). The program
requires 1D seismic velocity structure model to compute Green’s functions and we tested two structure
models, a regional structure model by Pandey et al. (1995) and widely-used model by Jeffreys andBullen (1958). Since the source depth of the earthquakes were not precisely determined and we searched
the reliable source depths, changing the depth from 5 to 15 Km. The propagation path is expressed as,𝑃(𝑓) =

1
−𝜋𝑓𝑟
exp (
)
𝑟
𝑄(𝑓) 𝑉𝑠,(4)

where r is the hypocentral distance(m), Q(f) is the quality factor which isequal to 100 f 1.16 (Hazarika et al.,2013) & Vs is the S-wave velocity.
Site effect G(f) is set as 2.0
for the hard rock site.
Figure 8 compares observed
and theoretical acceleration spectra
for Dolakha event. Here we canclearly see that the observed spectra
are quite similar to the theoreticalones, when we assumed cornerfrequency at around 4 Hz. Thisestimation well correlate with thevalue proposed in the past research(Timsina et al., 2018). The stations Figure 8. Comparisons between observed and theoretical
NANST & NAKRM show large acceleration spectra for Dolakha event. Stations NANST andamplifications in the lower frequency NAKRM inside the Kathmandu Valley shows large
range, but we cannot identify any amplification in the low frequency range, while there are no
decay of high frequency motions. significant differences in the higher frequencies. Blue and red
This clearly shows that the thick traces indicate theoretical spectra for different corner
sedimentary layers only amplify the frequencies (fc=3Hz and 4Hz, respectively).
input motion from 0.1 Hz to 1.0 Hz and do not significantly attenuate seismic ground motions.

6. CONCLUSIONS

5

The main findings from this study are as follows:i.
Estimated dominant peak frequencies from EHVSRs and MHVSRs corresponds to each otherin the Kathmandu Valley. We identified the peaks at NANST (0.32 Hz), at which the thicksedimentary layer could exists.
ii.
Theoretical H/V ratios show that the sedimentary layer had a thickness ranges from 42 to 700
m in the study sites.
iii.
The velocity spectra derived from Afghanistan event and the amplitude spectra derived from
Dolakha and Gorkha event shows that large seismic amplifications were observed in the lower
frequency range at basin stations (0.1-1.0 Hz).
iv.
The spectra of local event showed that the attenuation of high-frequency seismic waves are notsignificantly attenuated in the Kathmandu basin.

7. RECOMMENDATION
The accurate estimation of bedrock depth using single station microtremor data is difficult. However,the data showed the spatial variation of bedrock depth in the Kathmandu Valley. The combined use of
H/V and other approach, such as microtremor array exploration, receiver function analysis and gravity
survey would help to understand detailed 3D basin structure model.

ACKNOWLEDGEMENTS
I would like to express my sincere gratitude to the supervisors Dr. Toshiaki YOKOI and Dr. Takumi
HAYASHIDA for their tremendous support, valuable suggestion and instruction during my study. I
would like to extend my gratitude to DMG, JICA, IISEE and GRIPS for the opportunity to study thiscourse.

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Website: IRIS, https://ds.iris.edu/ds/nodes/dmc/forms/breqfast-request/
Website: FDSN, http://www.fdsn.org/networks/detail/XQ_2015/6