Citation: Dalaijargal, L., T. Hayashida (2020), Estimation of surface wave dispersion characteristics
using ambient noise records in Ulaanbaatar region, Synopsis of IISEE-individual study report.

ESTIMATION OF SURFACE WAVE DISPERSIONCHARACTERISTICS USING AMBIENT NOISE
RECORDS IN ULAANBAATAR REGION
Lkhagvadorj DALAIJARGAL1

Supervisor:

Takumi Hayashida2

ABSTRACTWe observed Rayleigh and Love wave dispersion characteristics between 5 s and 20 s using yearlystacked cross-correlation functions (CCFs) of ambient seismic noise data, from the dense seismic
broadband network consisting of 17 stations in the Ulaanbaatar region, Mongolia. The extracted
empirical Green's functions of Rayleigh and Love waves between 136 pairs of stations showed
substantially large signal-to-noise ratios (> 17) in the target periodic range, indicating clear trends ofwave propagation up to ~270 km. We removed P-wave contamination, which exists as precursors, fromthe stacked CCFs using vertical-to-radial (ZR) and radial-to-vertical (RZ) components and succeeded in
obtaining dominant Rayleigh-wave signals. The estimated Rayleigh and Love-wave phase velocities
using the matched-filter frequency-time analysis showed little variation over the period between 6 s and
10 s, showing small subsurface heterogeneity in the Ulaanbaatar region. The estimated Rayleigh wave
phase velocity map for 8 s indicated small (~0.05–0.1 km/s) velocity contrasts between the western andeastern parts, and this suggests the possibility of local-scale seismic velocity heterogeneity in the study
area. The resulting velocity maps correspond well with the estimations derived with a sparse seismicarray. We also found a feature which could extend our Rayleigh wave phase velocity dispersion to amuch longer period, and shorter spacing stations, (which are typically excluded in seismic
interferometry) could be included using the SPAC method. The combined use of seismic interferometry
and SPAC method will make it possible to conduct a much more precise surface-wave tomographic
study in the Ulaanbaatar region.
Keywords: Ambient noise cross-correlation, Ulaanbaatar region, Rayleigh wave, SeismicInterferometry.
1. INTRODUCTION
The Mongolian territory is in the middle of the Central Asian Orogenic Belt (CAOB). The CAOB issituated between the ancient Precambrian Siberian Craton in the north and the Tarim and Sino-Korean
Cratons in the south (Badarch et al., 2002). The seismicity of Mongolia is characterized by the effect of
the India and Eurasian convergence. Compared to the plate boundary regions, seismicity in Mongolia isrelatively low. However, during the last century, large earthquakes (> M8) occurred several times in theWestern part of Mongolia, such as Bolnay M8.4 (1905), Gobi-Altay M8.1 (1957), and Tsetserleg M8.0
(1905). The Ulaanbaatar region, the capital city of Mongolia, the seismic velocity structure has not been
well investigated due to its low seismicity. Nevertheless, Recent receiver function studies with
1
2

Institute of Astronomy and Geophysics, Mongolian Academy of Science (IAG), Ulaanbaatar, Mongolia.
International Institute of Seismology and Earthquake Engineering, Building Research Institute.

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teleseismic events have showed the interesting feature that the low velocity zone is investigated at a
depth of 10-12 km in study area (e.g. Zorin et al., 2003; Baasanbat et al., 2015). In recent years, theambient noise cross-correlation technique has become an effective standard tool to construct seismic
velocity structures on both local and global scales. Our final goal is to estimate the seismic velocity
structure model of the crust and upper mantle using ambient noise records in and around the Ulaanbaatar
region. For this purpose, we conduct seismic interferometric analysis using one-year long ambient noise
records in the Ulaanbaatar region to extract empirical Rayleigh and Love waves Green's functions.
2. DATAWe analyzed one-year continuous data from 17 broad-band stations in and around Ulaanbaatar region(Figure 1) recorded between January 1,
2018 and January 1, 2019. The 16
broadband stations were equipped with
CMG-3ESPC (60 s and 120 s)
broadband seismometers (Guralp Inc.)
whose sensitivity is 2000 V/m/s. Eachsensor is connected to a data logger
CMG-DM24S3EAM (Guralp Inc.), andthe sampling frequency is set as 50 Hz
for all stations. Further to that, we used
the station IU.ULN from theIncorporated Research Institutions for
Seismology (IRIS), which is equippedwith STS-1 (Streckeisen), a verybroadband seismometer with asensitivity of 2358 V/m/s, and recordedwith the data logger Quanterra Q330
with sampling frequency 20Hz. All thepermanent stations are installed in a
concrete vault at a depth of 3 m below
the surface. As for the interstation
distance, station spacing ranges from Figure 1. Map showing seismic station distribution (redtriangles). The black solid lines indicate interstation paths.
27.92 km to 269.42 km, and the total
number of station pairs used in this study
is 136.
3. METHODOLOGY
3.1. Seismic Interferometry
Recently, surface wave tomography using ambient noise has been widely used to estimate the dispersion
of surface waves from regional to global scales. The results from ambient noise tomography agree wellwith traditional surface wave tomography (e.g. Lin et al., 2008; Pan et at., 2011), and the resulting maps
show comparable interpretations in terms of known geological structures such as mountainous ranges
and sedimentary basins. In seismology, interferometry based on the ambient noise cross-correlation
between two receivers is now a standard and effective tool to illuminate subsurface structures such as
crust and upper most mantle of the Earth from the global to regional scale. Seismic interferometry iscommonly referred to as the study of interference phenomena between two receivers to obtain structural
information of a medium. The main utilization of seismic interferometry is a cross correlation analysis.
With a simple mathematical cross-correlation operation, the impulsive response of a medium, known asGreen’s function, can be extracted assuming one station acts as a source and the other as a receiver. In
fact, the noise source distribution is not homogenous. However, under the assumption of the plane wave

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propagation and homogenous noise distribution, waves coming from all the direction are destructed
except for the two stationary points. Then, the resulting cross-correlogram yields Green’s function
between the two receivers. The cross-correlation function (CCF) between a pair of two stations can be
described as follows:∞𝐶[𝑥(𝑡), 𝑦(𝑡)] = ∫ 𝑥(𝜏)𝑦(𝜏 + 𝑡)

(1)

−∞

where, x(t) and y(t) are recorded time series at two receivers, and τ is the lag time.
3.2. Green’s function extraction
The positive and negative lag signals are observed from the CCF. We then use a symmetric signal toobtain the Green’s function. The traditional definition of Green’s function is the impulsive response of
the point force. So, we estimate approximate empirical Green’s function between two receivers 𝐺𝐴𝐵 (𝑡)from the time-averaged cross correlation Eq. (2).
𝑑 𝐶

𝐺𝐴𝐵 (𝑡) = − 𝑑𝑡 [ 𝐴𝐵(𝑡)+𝐶𝐵𝐴 (−𝑡)2] 0≤𝑡<∞(2)

where 𝐶𝐴𝐵 (𝑡) – positive time lag signal, and 𝐶𝐴𝐵 (−𝑡) – negative time lag signal. By folding thesymmetric parts of the CCF, the signal-to-noise ratio is significantly improved, and the effect of thesource distribution's inhomogeneity is greatly reduced. But it alters the signal phase by 𝜋/2, and affects
the phase dispersion measurement.
4. DATA PROCESSING
The data processing procedure consisted of three principal phases: (1) single station data preparation,(2) estimation of Green’s function, and (3) the measurement of dispersion curves. Here we used an inhouse software package developed by T. Hayashida (partially used in Hayashida et al., 2014). The dataprocessing procedure used in this study is mostly based on Bensen et al. (2007). The single station datapreparation is intended to eliminate nonpermanent signals (earthquake and instrumental irregularities)
from continuous ambient noise records for further cross-correlation analysis. To do that, we first
conducted data selections based on two criteria; (a) Daily waveforms must be more than 80 percent(Bensen et al., 2007), and (b) data gaps between each sub traces must be less than 25 s, because wefound that the gaps larger than 25 s generate amplitude jumps on the resulting cross-correlation. Data
gaps < 25 s were filled with artificial zero amplitudes. Then, all the continuous raw signals were divided
into daily sub-traces, and the DC components, liner trend, and the effects of the instruments were
removed. A band-pass filter (5–50 s) were then applied to all daily records. After the filtering, daily
time-series are down-sampled into 2 Hz to save computation time. After instrumental correction,transient earthquake contaminations are removed by running the absolute-mean normalization method(Bensen et al., 2007). Then after, we conducted the spectral whitening within a window of 0.01 Hz in
the frequency domain to flattening the amplitude spectrum at whole frequency range. The mainprocedure, cross-correlation, is performed in the frequency domain between all components ZZ, ZN,
ZE, NZ, NN, NE, EZ, EN, and EE for each station pairs and stacked all daily cross-correlograms. Then,the rotations of horizontal component to radial and transverse directions started just after the stacking tospeed up the process using methods of (Lin et al., 2008, Yamanaka et al., 2010). The phase and groupvelocity measurements were performed using automated procedure frequency-time analysis (FTAN).
We used a global crustal structure model (CRUST 1.0; Laske et al., 2013) to estimate the reference
dispersion curves of surface-wave phase velocity in the study area. To achieve this, we extracted the 1D
crustal model at N47.50º, E106.50º and a program package DISPER80 developed by Prof. Saito (Saito,1988). For the measurements of group and phase velocities of fundamental Rayleigh and Love modes,3

we used a program package aftan1.1 provided by the University of Colorado Boulder(http://ciei.colorado.edu/Products/) for automatic frequency-time analysis (AFTAN) of the CCFs. Afterthe group and phase velocity dispersion measurements, the following selection criteria that was
described by Bensen et al. (2007) were used to gather reliable velocity measurements: First, signal-tonoise ratios higher than 17 were selected at the periods of interest. Second, we selected phase and groupvelocities with interstation distances larger than three wavelengths to satisfy the far-field approximation.
5. RESULTS AND DISCUSSION
The similar symmetric Rayleigh waves with identical arrival times emerged at both positive and negative
correlation lags for the Z–Z, R-R, R-Z, and Z-R components. We obtained a reasonable Rayleigh wave
without P-wave contamination (Figure 2a) by taking difference between cross-correlation tensors Z-R
and R-Z using newly developed method (Takagi et al., 2014). Hereafter, this component is named ZRRZ
for further discussion of dispersion measurement. Moreover, clear Love waves were also observed on
the transverse-transverse (T-T) component (Figure 2b). In general, the observed Love waves with groupvelocity ~3.45 km/s showed faster travel time than those of Rayleigh waves observed with groupvelocity of ~3 km/s. Also, we estimated the signal-to-noise ratio for the yearly stacked cross-correlationsof 136 station pairs by taking the ratio between peak amplitude and RMS noise level of trailing noise.
In all cases, our estimated spectral SNR satisfy the first acceptance criteria for data selection that the
SNR needs to be higher than 17 (Bensen et al., 2007) at the periods of interests between 5 and 20 s.

Figure 2. The observed Rayleigh and Love waves from ambient noise cross-correlation
between 136 station pairs (a) The Rayleigh wave without body wave contamination on
the ZRRZ component ~ 3.0 km/s (b) Love waves on the transverse-transverse (T-T)
component ~ 3.45 km/s. The vertical axis shows the interstation distances, and thehorizontal axis shows the time lag.
Surprisingly, we observed visible signals on cross terms (T-R, R-T, Z-T, and T-Z), but no clear
arrivals were observed. Compared to the diagonal components (Z-Z, T-T, and R-R), these signals werequite weak in most cases. The existence of such signals could be linked to the conversion from Rayleighto-Love or Love-to-Rayleigh waves due to the structural heterogeneity, but this is beyond the scope of
this study.
We also investigated the feature that there is a π/2 phase shift between the ZRRZ and Z-Z
components. Since such phase difference significantly affects the phase velocity measurement, weincorporated a correction factor in the FTAN analysis. Finally, we measured group and phase velocities
using AFTAN on ZRRZ (Rayleigh wave) and TT (Love wave) components for 136 station pairs (Figure
2a and 2c). In this study, the seismic station distribution is typically dense, and station spacing is varied4

from 28 to 270 km.
Thus,
resultingmeasurements wereconstrainedbyinterstation distance,
and totally 31 station
pairs were excluded
in data selection.
We observed aninteresting featurethat a slightly clear
velocity
contrast
emergedattheRayleigh and Lovewave phase velocity
map of 8 s. However,both maps presenteddifferent
characteristics in thewestern and easternparts of the study
area. As a result of
the Rayleigh wave
phase velocity map
Figure 3. (a) Measured Rayleigh wave dispersions for all station pairs, theat 8 s, the paths
reference dispersion curves for both group and phase dispersion curves are
between some station shown as blue and red dashed lines, respectively. The red solid lines denotepairs in the eastern the phase, and blue solid lines denote group dispersion curves. (b) Rayleigh
area
show
a wave phase velocity map of 8s. (c) Love wave dispersion curves, (d) Love
relatively
smallerwave phase velocity map of 8s.
value than those of
the station pairs in the western part of the study area. In contrast to the Rayleigh wave phase velocity
map at 8 s, the Love wave phase velocity map at 8 s presents high velocity in the east and low velocity
in the west.

Figure 4. The comparison between the
SPAC results and seismic interferometry
results.

We investigated the possibility of theestimation of Rayleigh wave phase velocities using
the SPAC method (Aki 1957), and compared theestimated results with those from seismic
interferometry measurements. Generally, the seismic
interferometry uses the main criteria that the distance
between two stations must be longer than three
wavelengths to satisfy the far-field approximation for
selecting the most reliable dispersion measurements.
As mentioned before, most of our resulting dispersion
measurements are excluded based on these criteria
used in seismic interferometry analysis. Alternatively,
the SPAC approach can be applied for station
separation greater than one wavelength and can also
be applied for much longer wavelength measurements(Figure 4).

55. CONCLUSIONS
We observed clear Rayleigh and Love waves from one-year ambient noise cross-correlations using thedense seismic broadband network with 17 seismic stations in the Ulaanbaatar region. The main
conclusions are as follows:
1. The estimated SNRs for all station pairs at periods of 5 and 20 s were higher than the required data
selection criteria. The ambient noise seems to be weak at periods between about 25 and 50 s.
2. A phase shift was found between the ZRRZ and Z-Z components, and we applied a correction factor
to the ZRRZ component in FTAN analysis. After the correction, the resulting Rayleigh wave
dispersion curves for both the Z-Z and ZRRZ components were typically similar, and the groupvelocity dispersion measurements for the ZRRZ component produced relatively stable results thangrouped speed dispersion of the Z-Z component. This indicated that the method of separating theRayleigh wave from the body wave was effective to obtain a stable measurement.
3. Our study was the first case that measured regional Love wave phase and group velocity dispersion
measurements in the study area.
4. The Rayleigh wave phase velocity in this study was very similar to the results for previously studied
ambient noise in the south-central Mongolian region. Our data will help complement phase velocity
maps, obtained with a lack of station coverage in the Ulaanbaatar region.
5. The resulting dispersion measurement from the SPAC analysis presented a significant feature thatthe dispersion measurement can be extended up to longer periods of around 20 to 30 s. Furthermore,this enables us to perform phase velocity dispersion measurements for longer wavelength even for
station pairs with short distances. By combining the results from the seismic interferometry and the
SPAC method, we can complement our dispersion measurement with further tomographic analysis.
ACKNOWLEDGEMENTS
This research was conducted during the individual study period of the training course “Seismology,
Earthquake Engineering and Tsunami Disaster Mitigation” by the Building Research Institute, JICA. I
would like to express my sincere gratitude to my supervisor Dr. Takumi Hayashida for his valuable
support and guidance which helped me to complete my research successfully, and to Dr. Tatsuhiko Harafor his guidance and comment during my individual study. I also would like to thank all the other staff
members at IISEE/BRI and my international friends in TBIC for their support and encouragement during
this training program.
REFERENCES
Aki, K., 1957, Bull. Earthq. Res. Inst., 35, 415-457.
Badarch, G., Cunningham, W.D., and Windley, B.F., 2002, J. Asian Earth Sci., 21, 87-110.
Bensen, G.D., Ritzwoller, M.H., Barmin, M.P., Levshin, A.L ..., 2007, Geophys. J. Int, 169, 1239-1260.
Hayashida, T., Yoshimi, M., and Horikawa, H., 2014, Zishin 2, 66, 127-145.
Laske, G., Masters., G., Ma, Z., and Pasyanos, M., 2013, Geophys, Res. Abstract EGU2013-2658.
Lin, F.C., Moschetti, M.P., and Ritzwoller, M.H., 2008, Geophys. J. Int, 173, 281-298.
Okada, H., 2003, Geophysical Monograph Series No.12, Society of Exploration Geophysicists, Tulsa.
Pan, J.T., Wu Q.-J., Li, Y.-H., Yu, D.-X., Gao, M.-T., Ulziibat, M., and Demberel, S., 2015, Chin. J.
Geophys., 58, 436-450.
Saito, M., 1988, Academic Press, ed. by Doornbos, D.J, 293-319.
Takagi, R., Nakahara, H., Kono, T., and Okada, T., 2014, J. Geophys. Res.: Solid Earth, 2005-2018.
Takeo, A, Nishida, K., Isse, T., Kawakatsu, H., Shiobara, H., Sugioka, H. and Kanazawa, T., 2013, J.
Geophys. Res., 118, 2878-2892.
Ts, Baasanbat, Shibutani, T., 2015, IISEE/BRI, 1-63.
Yamanaka, H., Chimoto, K., Moroi, T., Ikeura, T., Koketsu, K., Sakaue, M., and Nakai, S., 2010,
BUTSURI-TANSA (Geophysical Exploration), 63, 409-425.
Zorin, Y.A., Mordvinova V.V., and Turutanov, E.K., 2002, Tectonophysics, 359, 307-327.

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