1. science
2. geology
3. seismology
4. earthquakes

J Seismol
DOI 10.1007/s10950-015-9494-z
ORIGINAL ARTICLE
Effects of seasonal changes in ambient noise sources
on monitoring temporal variations in crustal properties
Meng Gong & Yang Shen & Hongyi Li & Xinfu Li &
Jinsheng Jia
Received: 30 December 2013 / Accepted: 30 March 2015
# Springer Science+Business Media Dordrecht 2015
Abstract Continuous data recorded at 39 broadband
stations near the Longmen Shan Fault operated by the
China Earthquake Administration from 1 January 2008
to 30 September 2010 are used to study temporal variability in direct surface wave arrivals extracted from
ambient noise. We use a cross-correlation technique to
compute Empirical green functions (EGFs) for all available station pairs at the frequency range of 0.1 to 0.5Hz.
Delay times are measured by cross-correlating reference
empirical green functions and moving 60-day stacks of
EGFs. By comparing the temporal changes with and
without the correction for seasonal variations, our results
show that for some station pairs temporal variations were
strongly affected by the seasonal variation. After correction for seasonal variations, we measure a 0.5-% maximum velocity drop after the 2008 Ms8.0 earthquake in
Sichuan, China. We find that the Sichuan Basin exhibited
a larger relative velocity drop than the Tibetan plateau
area. Our results suggest that correction for seasonal
variation is an important procedure for monitoring temporal variations in crustal properties using the direct
arrival surface waves extracted from ambient noise.
M. Gong : H. Li : X. Li
Key Laboratory of Geo-detection, Ministry of Education,
China University of Geosciences, Beijing 100083, China
Keywords Ambient noise . Empirical green function .
Cross-correlations . Surface waves . Season variations .
Temporal variation
H. Li
e-mail: littlefour_lhy@yahoo.com
X. Li
e-mail: xinfulee@163.com
1 Introduction
M. Gong : Y. Shen
Graduate School of Oceanography, University of Rhode
Island, Narragansett 02882 RI, USA
Empirical green functions (EGFs) can be extracted from
cross-correlations of ambient noise and coda waves
between station pairs with sufficiently long records
(e.g., Campillo and Paul 2003; Shapiro and Campillo
2004; Sabra et al. 2005). EGFs have been used to image
crustal and upper-mantle velocity structure at various
scales (e.g., Sabra et al. 2005; Yao et al. 2006, 2008;
Yang et al. 2007; Cho et al. 2007; Lin et al. 2007, 2008;
Bensen et al. 2007, 2008; Li et al. 2009, 2010, 2012).
EGFs have also been used to monitor temporal variations in crustal properties. For example, Brenguier et al.
(2008a) observed seismic velocity drops along the San
Andreas Fault after the 2003 San Simeon and 2004
Y. Shen
e-mail: yshen@uri.edu
J. Jia
Shanxi Lanyan Coalbed Methane Co. Ltd., Jincheng
048000 Shanxi, China
e-mail: jiajinsheng_1986@163.com
M. Gong (*)
Seismological Bureau of Hebei Province,
Shijiazhuang 050000, China
e-mail: mrgongm@gmail.com
J Seismol
Parkfield earthquakes. Brenguier et al. (2008b) also found
a decrease of seismic velocity on the order of 0.05 % a few
weeks before the Piton de la Fournaise volcano eruptions.
Wegler and Sens-Schönfelder (2007) found a 0.6-% drop
in seismic velocity after the Mid-Niigata earthquake.
Compared to naturally repeating earthquakes, the use of
EGFs is more repeatable and avoids the uncertainty in
earthquake source locations and origin times. It is also
more economical than controlled repeated sources. For
these reasons, the technique of passive monitoring with
ambient noise is becoming a useful tool to detect temporal
variations of the earth structure.
On 12 May 2008, an Ms8.0 earthquake ruptured the
Longmen Shan Fault in Sichuan, China (hereinafter the
Wenchuan earthquake). It was the most destructive
earthquake since 1980s in China in terms of human
and property losses. There have been several related
studies that focus on the earthquake source mechanism
(Zhang et al. 2008; Royden et al. 2008; Lei et al. 2009),
crustal structure (Wang et al. 2009; Wu et al. 2009),
crustal rupture process (Wang et al. 2008; Zhang et al.
2009; Xu et al. 2009), and crustal movement (Jiang et al.
2009). Using ambient noise cross-correlations, Liu and
Huang (2010), Cheng et al. (2010) and Chen et al.
(2010) studied the temporal variations of the velocity
structure near the Longmen Shan Fault. Liu and Huang
(2010) used 3 years of continuous data from 2007 to
2009 at frequencies of 0.1 to 0.5 Hz. Cheng et al. (2010)
used 100-day continuous records around the date of the
main shock at frequencies of 0.04 to 0.1 Hz. Both
studies found an approximately 0.4-% maximum seismic velocity drop after the Wenchuan earthquake. Chen
et al. (2010) used the data from January 1, 2007 to the
end of 2008 at periods of 1 to 3 s and found a seismic
velocity drop of 0.08 % after the earthquake. These
studies did not consider the effects of temporal variations in the distribution of ambient noise sources on
ambient noise cross-correlations, which are known to
be important depending on the application (e.g.,
Harmon et al. 2010; Froment et al. 2010). At the level
of a fraction of a percent velocity change, these source
effects become important and have to be separated from
the true, in situ wave speed changes.
Passive monitoring with ambient noise to date has
relied primarily on the coda of surface waves. By definition, the coda is a scattered wave less affected by
variation in noise source than the direct surface wave
arrival, although it has been shown that the coda wave is
affected by rainfalls during the monsoon season
(Froment et al. 2013; Obermann et al. 2014).
Compared to the coda, the direct surface waves are
sensitive to structures in a defined and relatively narrow
band between a station pair and thus essential for accurately locating and tomographic imaging of the temporal
variations in crustal properties.
In this paper, we use cross-correlations of ambient
noise recorded by the China Earthquake Administration
from January 2008 to September 2010, a much longer
recording period after the main shock than in the previous studies, to determine the temporal seismic velocity
variation in the Longmen Shan fault zone and adjacent
areas. Compared to Liu and Huang (2010), we use direct
arrival surface wave instead of coda wave, and we use
substantially more stations (39 in this study versus 17)
over a much broader area to obtain baseline measurements from stations far away from the fault zone, which
are presumably less affected or unaffected by the fault
rupture. We find that in some station pairs the apparent
temporal changes in ambient noise cross-correlations
are strongly influenced by seasonal variations. After
correction for seasonal variations, we measure a 0.5-%
maximum relative velocity drop after the 2008 Ms8.0
earthquake in Sichuan, China.
2 Data and methods
The vertical component continuous data from 39 broadband stations near the Longmen Shan Fault (Fig. 1)
operated by the China Earthquake Administration between 1 January 2008 and 30 September 2010 are used
in this study. Seismograms are first cut into daily segments. After the removal of the mean, trend, and instrument response, a 0.5-Hz low-pass filter is applied. To
reduce the effect of earthquakes and instrumental irregularities on cross-correlations, we normalized the
seismograms with a time–frequency normalization
method (Ekström et al. 2009; Shen et al. 2012) and
deleted time segments that contain earthquakes with
Ms≥ 3.5 according to the earthquake catalogs from the
China Earthquake Network Center. We computed daily
cross-correlations for all station pairs to obtain EGFs.
For each station pair, we stack daily cross-correlations
before the main shock from 1 January to 30 April 2008
and about 1 year after the main shock from 1 May 2009
to 30 September 2010 to construct our reference empirical green function (REGF). To detect temporal variations, we stack cross-correlations in a 60-day moving
J Seismol
Fig. 1 Topographic relief of the Longmen Shan Fault area. Black dashed lines denote the faults; triangles are stations used in this study. The
epicenter of the Ms8.0 earthquake is marked by a star, and circles denote the aftershocks (Ms≥ 3.5)
window and denote the resulting stack (EGF60) to the
center of the moving window in our date sequence. If the
number of daily records in the moving window is less
than 30, EGF60 is not computed. In order to measure a
fraction of a percent velocity change, we interpolated the
REGFs and EGF60 from five samples per second to 50
samples per second to measure the relative time-shifts at
the frequency range of 0.1 to 0.5 Hz.
If the relative velocity change in the medium is
isotropic and homogeneous, it can be determined
from a linear regression of the relative delay time
Δτ/τ between the reference and windowed EGFs,
where Δτ is the relative delay at the time lag of
cross-correlation τ (Brenguier et al. 2008a, b; Liu
and Huang 2010). Because this method uses the
coda, the measurement reflects an average over a
large area determined by the length of the coda
instead of the immediate area around the direct path
between the station pairs. Moreover, if the time
window used in the measurement, τ, starts before
the main surface wave arrival (e.g., Liu and Huang
2010), the waveform may contain teleseismic and
local P and S body waves (Roux et al. 2005; Zhang
et al. 2010; Zhan and Clayton 2010), making it
difficult to associate the measurement with a single
wave type and path.
J Seismol
In this study, we explore the benefits of direct surface
wave arrivals and the factors that may affect their stability. We use the direct surface (Rayleigh) wave defined
by a range of group velocities (2–4 km/s) to measure the
relative temporal variation in the seismic velocity. This
range is substantially wider than the likely range of
group velocities in the region at 2–10-s periods. In this
period range, Rayleigh waves are sensitive to velocities
in the upper and middle crust (approximately upper 15km depth). In general, the EGF60 and REGF for the
same station pair are very similar to each other as shown
in Fig. 2. Relative delay times are measured from the
cross-correlations of daily EGF60 and REGF in the time
window for the direct Rayleigh wave for both the positive (causal) and negative (acausal) time lags. If the
correlation coefficient of EGF60 and REGF is less than
0.6 or the EGF60 signal–noise ratio (SNR) is less than 4,
the delay time is not computed. The SNR is defined as
the maximum amplitude of signal divided by the standard deviation in the noise time window (Fig. 2a). The
bootstrap method (Efron and Gong 1983) is used to
assess the uncertainties of the delay time measurements
at a 60-day interval. The uncertainty is highest after the
main shock, possibly due to intense aftershock activities
and the removal of time segments that contain the aftershocks. In this paper, a measurement is valid if the
standard deviation of the delay measured by the bootstrap method is smaller than 0.06 s.
Fig. 2 a REGF (solid line) and
one EGF60 centered on 20 May
2008 (dashed line) for the station
pair PWU-JMG; vertical solid
lines indicate the signal and noise
time windows used. b Enlarged a
time lags from −60 to 60 s
3 Result
3.1 Clock-shift and seasonal variation
A change in the physical property of the medium should
cause the same earlier or later travel time in both the
causal and acausal time lags. But uneven and varied
spatial distribution of noise sources and the instrumental
clock errors may affect the EGFs and make the measurements on the positive and negative time lags asymmetric (Stehly et al. 2007; Zhan and Clayton 2010). As
discussed in Stehly et al. (2007), the travel time variation
δτ(t) measured from a surface wave by crosscorrelations can be written as:
δτ ðt Þ ¼ Dðt Þ þ φðt Þ þ εðt Þ
ð1Þ
In this equation, δτ(t) denotes the variation of surface
wave travel time measured either on the positive or on
the negative part. D(t) is the time delay caused by the
instrument clock errors. φ(t) is the time-shift due to
changes in the medium. ε(t) is the time-shift due to
changes in the spatial distribution of the source. D is
an even function, whereas φ(t) is an odd function. By
taking the even and odd part of Eq. (1), we obtain:
δτ ðt Þ þ δτ ð−t Þ
εðt Þ þ εð−t Þ
¼ D ðt Þ þ
2
2
ð2Þ
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This equation allows us to evaluate the relative drift
of the station clock D(t), under the assumption that D is
ð−t Þ
large compared to εðtÞþε
. Following Eqs. (1) and (2),
2
we can correct instrumental errors and get the measured
time delay fluctuations δτ*(t) by:
εðt Þ þ εð−t Þ
δτ * ðt Þ ¼ ½Dðt Þ þ φðt Þ þ εðt Þ− Dðt Þ þ
2
ð3Þ
δτ * ðt Þ ¼ φðt Þ þ
εðt Þ−εð−t Þ
2
ð4Þ
Figure 3 shows the measured temporal variations of
the apparent velocity for three station pairs and their
path locations. We first measure the delay times on the
positive (δτ(t)) and negative (δτ(−t)) time lags, respectively (e.g., Fig. 3a—i and ii) and then use Eq. (2) to
compute the time-shift which includes the effects of
instrument clock shift (Fig. 3a—iii) and Eq. (3) to
correct the anti-symmetry component of the measurements to get the corrected delay time including the timeshift due to changes in the medium and the time-shift
due to the seasonal variations as shown in Eq. (4). We
use this delay time divided by the arrival time of the
direct arrival surface wave to compute the relative velocity change, including the effects of seasonal variations in percent (Fig. 3a—iv).
After the correction for the anti-symmetry component, two of the station pairs in Fig. 3 (GZA-EMS and
HSH-YGD) show temporal variations that have a clearly identifiable yearly oscillation (Fig. 3a—iv, b—iv).
The apparent velocity begins to increase in January
and reach to peak in July and then decreases back to a
trough in the next January. For path GZA-EMS, the
positive time lag of the correlation corresponds to waves
traveling from GZA to EMS, while the negative part
corresponds to waves traveling from EMS to GZA. The
positive time lag is sensitive to sources in the northwestern direction and exhibits larger fluctuations (Fig. 3a—i)
than the negative time lag (Fig. 3a—ii), which is sensitive to sources coming from the southeastern direction.
The same features can be found for the path HSH-YGD.
However, station pair XJI-MDS exhibits a relatively
smaller and different type of seasonal fluctuation.
These periodic fluctuations were likely caused by
seasonal variation in the noise source located in distant
regions and local rainfalls (Froment et al. 2013;
Obermann et al. 2014). They are of similar order of
magnitude as the reported velocity drops after the earthquake. To correct for seasonal variation, we average the
relative delay times in 2009 and 2010 by the same date
of the year for each station pair to obtain the average
yearly seasonal variation, and then we assume that this
average yearly season variation is representative for the
whole study period (from 1 January 2008 to 30
September 2010) as shown in Fig. 3a—v, with the
assumption that the deviation from the average seasonal
variations in the 60-day moving window of a particular
year is of second order in magnitude. Finally, the seasonal variation was subtracted from our relative velocity
changes with the anti-asymmetry correction to get the
final result (Fig. 3—vi).
Figure 4 shows additional examples of the temporal
velocity changes, with and without correction for seasonal variation and their path locations. Most of the
station pairs show seasonal variation. However, station
pairs with similar path orientations may exhibit a different seasonal variability. For example, station pairs XJIMDS, HSH-JJS, and YZP-HMS are all nearly orthogonal to the Longmen Shan Fault (Fig. 4a), but YZP-EMS
(Fig. 4j) exhibits a stronger seasonal variation than
others. On the contrary, REG-RTA (Fig. 4b), WDTWXT (Fig. 4d), and YGD-EMS (Fig. 4g) are all subparallel to the Longmen Shan Fault and show similar
seasonal variations. ZJG-BZH are located in Sichuan
Basin, and XJI-YZP are located in the Tibetan Plateau
(Fig. 4a). The paths of these two station pairs are almost
parallel to each other, but ZJG-BZH (Fig. 4m) exhibits
the strongest seasonal variation. These observations
suggest that in addition to the influence of remote noise
sources, local crustal scattering and local seasonal noise
sources (e.g., rainfalls and rivers) may also play an
important role in seasonal variation, although the exact
causes and mechanisms of the apparent local influences
remain unknown.
3.2 Velocity change after the main shock
As shown in Fig. 4, most of the station pairs located near
or across the Longmen Shan Fault exhibit a velocity
drop after the main shock (Fig. 4d–f). In particular,
WDT-WXT (Fig. 4d), which is located northwest of
the Longmen Shan Fault, had an approximately 0.5-%
maximum relative velocity drop. Station pairs located in
the plateau area exhibit no obvious velocity change
immediately after the main shock as seen at station pairs
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Fig. 3 Examples of removal seasonal variation and clock errors
from measured temporal velocity change for the paths GZA-EMS,
HSH-YGD, XJI-MDS, and their locations. a–c The heavy vertical
lines denote the time of the Wenchuan earthquake. i and ii The
temporal variations for the positive and negative time lags, respectively; iii the anti-symmetry component of the measurements
includes the effects of instrumental clock-shift; iv the relative
velocity temporal variation after correction for the anti-symmetry
component; v the seasonal variation obtained from iv; vi the final
result after correction for seasonal variation; negative values indicate a velocity decrease relative to the reference. d Locations and
paths of station pairs used in a–c. d Black dashed lines denote the
faults; the epicenter of the main Wenchuan earthquake is marked
by a black star
REG-SPA and REG-RTA (Fig. 4b, c). In the basin area,
we measured sharp velocity drops after the main shock
at station pairs YGD-EMS (Fig. 4g), YZP-EMS
(Fig. 4j), and YZP-HMS (Fig. 4i). Station pair ZJG-
BZH (Fig. 4m) do not show a significant relative velocity change after the main shock.
We use the average velocity change 60 days after the
Wenchuan earthquake from all station pairs to analyze
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Fig. 4 Examples of relative velocity changes for station pairs and
their location distributions. Negative values indicate a velocity
decrease relative to the reference. a Locations and paths of station
pairs used in b–m; red lines denote the Longmenshan faults. The
epicenter of the main Wenchuan earthquake is marked by a red
star. b–m The red and black lines denote the results with and
without seasonal variation correction, respectively. The black vertical line denotes the time of the Ms8.0 Wenchuan earthquake. The
vertical blue lines are the standard deviation estimated from bootstrap at 60-day interval
the spatial distribution of the temporal variation in seismic velocity. Figure 5 shows the relative velocity changes, with and without the correction for seasonal variation. Prior to the correction for seasonal variation
(Fig. 5a), station pairs DBT-REG, GZA-SMI, and
YZP-MDS have average relative velocity changes of
up to approximately −0.4 %. The nearby station pairs
exhibit a complicated pattern; some paths exhibit a
velocity drop, while others show a velocity increase.
After correction for seasonal variation (Fig. 5b), DBTREG, GZA-YZP, and nearby station pairs exhibit consistent relative velocity changes, while the relative velocity change of YZP-MDS decreases from approximately −0.4 % to less than −0.1 %. As shown in
Fig. 5b, even after correction for the seasonal variation,
there are still some paths that exhibit velocity increase
after the main shock, which were inconsistent with their
surrounding paths (e.g., RTA-HSH and SPA-ZJG).
These contradictory observations may be caused by
the uncertainties of our measurements and the effects
of seasonal variations that are not completely removed.
Since the fluctuation of the measured velocity from
January 2010 is up to ±0.2 % (Fig. 4), only the average
velocity changes larger than 0.2 % are considered to
have been affected by the earthquake.
After correction for seasonal variation (Fig. 5b), most
of the station pairs near the Longmen Shan fault exhibit
a drop in the average relative velocity after the
Wenchuan earthquake. Station pairs located in the northern part of the Longmen Shan Fault have larger relative
velocity drops than the southern part, and the velocity
drop in the Sichuan Basin is on average larger than in
the Tibetan plateau area, respectively. Similar
observations were also documented in the studies of
Liu and Huang (2010) and Chen et al. (2010) from
measurements of coda waves. Liu and Huang (2010)
measured a maximum relative velocity drop of 0.4 % in
the station pair AXI-PWU, and Chen et al. (2010) found
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Fig. 5 Spatial distribution of the relative velocity variation averaged over 60 days after the Wenchuan without (a) and with (b)
correction for seasonal variation. Negative values indicate a velocity decrease relative to the reference. The epicenter of the
Wenchuan earthquake is marked by a black star; the red dots
denote the aftershocks, and black dashed lines denote the faults.
Triangles are stations used in this study. The color of the lines
connecting the station pairs corresponds to the relative velocity
changes, with dark blue and dark red representing the maximum
velocity increase and drop, respectively
a maximum velocity drop of 0.08 % just after the main
shock in the Longmen Shan fault region. Cheng et al.
(2010) found that seismic wave velocities drop by as
much as ∼0.4 % in the northwest side of the Longmen
Shan Fault. Wang et al. (2008) showed that Yingxiu and
Beichuan counties had the largest strike-slip motions
after the Wenchuan earthquake. Stations YZP and
PWU are located within these two counties. Most of
the station pairs around PWU and YZP in our study
(Fig. 5b) exhibit more than 0.2 % relative velocity drop
after the main shock, and for station pairs AXI-PWU,
QCH-PWU, and WDT-WXT, the relative velocity drops
reach 0.4 %. Thus, our measurements using the direct
surface arrivals are generally consistent with the coda
measurements in the previous studies, and these velocity
drops can be attributed to the damage of the upper and
mid crust during the main shock, as suggested by Liu
and Huang (2010) and Cheng et al. (2010).
Longmen Shan fault area. We find that the apparent
temporal changes in ambient noise cross-correlations
may be strongly influenced by seasonal variations.
Comparison of station pairs having similar path orientations reveals complexity in the seasonal variations,
suggesting that the seasonal variability may include both
local and remote influences, although the sources and
mechanisms of the local influence remain unknown.
After correction of seasonal variations, we measure an approximately 0.5-% maximum relative velocity drop after the main shock. Station pairs located near the Longmen Shan fault exhibit obvious
velocity drops after the Wenchuan earthquake, and
the velocity drop in the northern part of Longmen
Shan Fault is larger than the southern region. The
relative velocity drop in the Sichuan Basin is on
average larger than that in the Tibetan plateau. Our
results suggest that correcting seasonal variations
determined from multiple years of data leads to a
geographically more consistent pattern in the apparent velocity changes after the earthquake. Correction
for seasonal variations is therefore an important
procedure when we apply the ambient noise technique to monitor temporal variations in crustal properties with direct arrival surface waves. An important implication of this study is that direct surface
4 Conclusions
We demonstrate that direct surface waves extracted from
ambient noise also provide viable measurements to
monitor the temporal variations in the crustal velocity
structure before and after the Ms8.0 main shock near the
J Seismol
arrivals, corrected for seasonal variations, can be
used to monitor seismic wave temporal change in
crustal properties.
4.1 Data and resources
Waveform data for this study are provided by the Data
Management Centre of China National Seismic
Network at Institute of Geophysics, China Earthquake
Administration (Zheng et al. 2009).
Acknowledgments This research was supported by Open Fund
(No. GDL1202) of Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of Education, National
Science Foundation of China under Grant 41174050, the Program
for New Century Excellent Talents in University (NCET), the
Fundamental Research Funds for the Central Universities, and
the US National Science Foundation under Grant 0738779.
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