Fig. 3. The number of group velocity dispersion curves at different
periods.
We adopted the data processing procedures following the method of Bensen
et al. (2007) and Fang et al. (2009). Data are processed one day at a
time for each station after being decimated to 1 Hz. Other parts
involved instrument response removal, clock synchronization, time-domain
normalization, bandpass filtering (4−50 s period), and spectral
whitening. Following this, the day-long waveform at each station is
correlated with other seismic stations and the daily results are stacked
to produce the final cross-correlation results.
The resulting cross-correlations contain surface wave signals coming
from opposite directions along the path linking the stations. The
cross-correlations are often asymmetrical due to the inhomogeneous
distribution of ambient noise sources. To simplify data analysis and
enhance the signal-to-noise ratio (SNR) of the surface waves, we
separated each cross-correlation into positive and negative lag
components and then added the two components to form the so-called
symmetric component. The following analysis was done on the symmetric
signals exclusively.
We use the CPS (Computer Programs in Seismology) software developed by
Herrmann and manually picked up the group velocity dispersion curve
based on the multiple filtering technology to (Dziewonski et al., 1969;
Levshin et al., 1992,Herrmann, 1973). If there are n stations, then the
empirical Green’s function on n(n-1)/2 paths can be calculated. In order
to ensure reliable results, a quality control of the dispersion curve
was carried out.
An empirical Green’s function is accepted if its signal to noise ratio
is greater than 10 and the inter-station distance is at least 3 times of
wavelength at a given period (Yao et al., 2006; Bensen et al., 2007).
Furthermore, we excluded paths that are shorter than 120 km because of
the lack of adequate condition related to 3 times of the wavelength.
Finally, a total of 1883 dispersion curves of the station pairs meeting
the above requirements were extracted from the 3916 Rayleigh wave
waveform data (Fig. 2). We show in Fig. 3 the number of ray paths used
for surface wave imaging at different periods, and confirm that the
number of rays in most periods is relatively uniform.
Rayleigh wave velocity and S-wave velocity inversion
For the surface wave tomography, a generalized 2-D-linear inversion
procedure developed by Ditmar and Yanovskaya (1987) and Yanovskaya and
Ditmar (1990) was applied to construct the group velocity inversion,
which is a generalization to 2-D inferred from the classical 1-D method
of Backus and Gilbert (1968). In this study, we designed a 0.5° × 0.5°
grid lateral. The damping parameter (α) controls the trade-off between
the fit to the data and the smoothness of the resulting group velocity
maps. We use the value of α = 0.2, which yields relatively smooth maps
with small fit error.
From the Rayleigh wave group velocity obtained by the above inversion
approach, we extracted the dispersion curves of group velocity at each
grid node. We then inverted for the 1-D shear wave velocity structure
under each grid node following the method of Herrmann and Ammon (2004).
The velocities in between the nodes are interpolated linearly. In this
way, a 3-D shear wave velocity structure was constructed (Fig. 4).
The initial model has a constant shear-wave velocity of 4.48 km/s from
the surface to 90 km depth that is divided into 2 km layers. By starting
with an overestimated velocity model, we ensured that no artificial
low-velocity zone or layer boundary was introduced as a consequence of
the nonlinear of the inversion. A fixed Vp/Vs ratio of 1.732 was used
and the density was calculated from the P-wave velocity (Zanjani et al.,
2019).