Bulletin of the Seismological Society of America, Vol. 105, No. 4, pp. 2101–2108, August 2015, doi: 10.1785/0120140252
Synchronizing Intercontinental Seismic Networks Using the 26 s Persistent Localized Microseismic Source by Yingjie Xia, Sidao Ni, Xiangfang Zeng, Jun Xie, Baoshan Wang, and Songyong Yuan
Abstract
Accurate instrument clocks are essential for quantitative studies in seismology. Recent studies demonstrate that ambient seismic noise can be used to detect clock time drift for ocean-bottom seismometers (OBSs) or inland seismometers with internal hardware or software problems, but the short-period (< 20 s) interstation Rayleigh waves extracted from ambient seismic noise are too weak for intercontinental seismic station-pair correlations and thus are less effective for synchronizing sparse global seismic networks. The 26 s persistent localized microseismic source in the Gulf of Guinea radiates strong seismic signals that can be recorded on global stations, thus providing an alternative approach for synchronizing seismometers at large interstation distances. In this study, we test the feasibility of synchronizing seismic stations in Africa, North America, and Europe using the 26 s signals in the noise cross-correlation functions. We find that the clocks at the TAM and OBN stations are relatively stable during the years from 2002 to 2009; an ∼1 s time shift occurred at the CCM station from 2006 to 2009, and the clock drift was not constant. We also detected about 150 s time shifts at the KOWA station in October 2012 and up to 500 s in January 2013. We validate our results via comparison with the ambient noise method by Stehly et al. (2007) and teleseismic P-wave arrival-time residuals. The method proposed in this study can be used to calibrate the OBSs in the north Atlantic by synchronizing their clocks with the precisely calibrated inland seismometers.
Introduction Accurately synchronized clocks in seismic networks are the foundation in seismology for quantitative studies such as earthquake location and travel-time tomography (Rost and Thomas, 2002; Li et al., 2008; Gouédard et al., 2014). Furthermore, studies for detecting crustal velocity variations due to postseismic or volcanic processes require even higher timing precision (e.g., Brenguier et al., 2008; Hobiger et al., 2012). For earthquake early-warning systems, accurate clocks are also very important because the magnitude and location of earthquakes are usually estimated with only a few seconds of P waves (Wu and Kanamori, 2005), and even a 1.0 s error in estimating the origin time of earthquakes is a substantial delay for early-warning purposes. Timing systems in seismometers consist of an internal clock and external synchronization unit (Schneider et al., 1987). The internal clock is mostly based on quartz oscillators that take into account temperature-compensation effects; however, it still suffers from long-time-period clock drift with a linear or nonlinear trend. From the early 1960s to the late 1980s, seismic networks were usually externally synchronized with radio waves broadcast from a central station or a network of stations, for example, the WWVB system or the Omega navigation system (Swanson and Kugel, 1972;
Schneider et al., 1987; Hutton et al., 2010). Since the early 1990s, space navigation systems, such as the Global Positioning System (GPS), have been adopted for most digital seismic networks (Butler et al., 2004). With WWVB and Omega navigation system synchronization, a 1 ms timing accuracy is easily obtained, and microsecond accuracy is obtainable with advanced processing techniques (Schneider et al., 1987). With GPS synchronization, timing accuracy can be up to 50 ns, which is usually sufficient to meet the timing requirements for most seismological observation systems (Pallares et al., 2011). However, the external synchronization systems may fail under two circumstances. The first case is for ocean-bottom seismometer (OBS) networks, where electromagnetic waves from GPS satellites cannot penetrate the deep ocean water. Without external clock synchronization, the timing for the OBS networks rely on internal clocks, and clock error can be calibrated assuming linear time drift (Anchieta et al., 2011). Because of the presence of nonlinear clock drift, the timing error is up to a few seconds (Gouédard et al., 2014). The second case is for rare hardware problems or software bugs inside the seismometer (Gouédard et al., 2014), although GPS external synchronization is available. Stehley et al.
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The distribution of the seismic stations (triangles) and the 26 s source (star). The great circle traces L1 and L2 represent the surface-wave path from the 26 s source to the stations.
(2007) observed a clock error of about 2 s at station GSC, lasting for a few months in 1992, the causes of the clock errors are not yet well understood. A clock shift of about 0.2 s was also observed for station PFO in 1992. A clock error of 1–2 s or less due to internal clock problems is particularly harmful for travel-time tomography studies because the clock error is too subtle to be excluded as observation outliers. For example, Li et al. (2012) excluded travel-time data with residuals of 3 s and larger assuming that large traveltime residuals are due to inaccurate clocks or earthquake locations. Two methods have been proposed to calibrate clock errors for OBS or inland seismic networks. Comparison of the P-wave arrival between OBS stations and nearby inland stations can be used to check OBS clock errors (Anchieta et al., 2011). Coherent and stable Green’s functions retrieved from ambient seismic noise provide an efficient tool for detecting clock errors. This method has been applied to inland stations as well as OBS stations (Stehley et al., 2007; SensSchönfelder, 2008; Gouédard et al., 2014; Hannemann et al., 2014). The ambient-noise-based method can resolve clock error every month with an accuracy of one-tenth of a second with interstation Rayleigh waves with periods of 5–10 s or 10–20 s (Stehley et al., 2007). This method is applicable to a seismic network with a relatively small aperture, such as an OBS network, regional network, or national seismic network. However, this method might not work well for synchronizing stations on different continents (e.g., for stations in North America [CCM] and Africa [TAM]; Fig. 1), because interstation Rayleigh waves with periods of 20 s or less are too weak for very large interstation distances due to attenuation (Fig. 2). In the early 1990s, the Global Seismographic Network (GSN) was very sparse, with a few digital broadband seismic stations on each continent, thus an alternative method is required to synchronize these intercontinental seismic stations.
Fortunately, there are a few persistent localized (PL) microseismic sources on the Earth that continuously radiate strong seismic signals and which are useful for synchronizing seismic networks (the 26 s PL source in the Gulf of Guinea, the 7–15 s PL source on Kyushu Island, and the 26 s PL source near the Vanuatu Islands) (Oliver, 1962; Holcomb, 1980, 1998; Shapiro et al., 2006; Zeng and Ni, 2010, 2014; Xia et al., 2013). The PL microseismic sources are similar to the Omega navigation stations in having fixed locations and a monochromatic signal, narrow frequency band, and strongly radiated signals that can be identified at very distant receivers. Among the PL sources, the 26 s microseismic source in the Gulf of Guinea is particularly suitable for synchronizing intercontinental seismic stations because of its strong signal. The signal can be observed in raw seismograms when this source bursts (Oliver, 1962). The PL signals are much easier to be detected with the interferometry technique (Zeng and Ni, 2011). For example, the 26 s PL signal is strong on the noise cross-correlation functions (NCFs) between the TAM station in Africa and the CCM station in the United States, whereas the interstation Rayleigh waves are very weak (Fig. 2). In this study, we test the feasibility of using the 26 s PL signal to detect clock drift by comparing the monthly intercontinental NCFs with the average NCFs for the years 2002–2013. We first describe the data processing and clock drift detection algorithms, and then validate our method via comparison between other methods. We find the CCM station shows a clock drift of about 2 s across 2006 and the KOWA station’s clock drift is a few hundred seconds in 2013. Finally, we discuss the advantages, drawbacks, and potential uses of our method.
Data and Processing To demonstrate the feasibility of detecting clock drifts for intercontinental seismic networks, we chose two stations in
Synchronizing Intercontinental Seismic Networks Using the 26 s Persistent Localized Microseismic Source
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per second. This process ensures that the seismic data starts on the beginning of each day with a 0.01 s precision. After applying the running absolute average method (Bensen et al., 2007), all the daily NCFs are computed and stacked into the monthly NCFs. When the station pairs and the PL source are not aligned on a great circle path, the PL signal shows up as a precursor to the interstation Rayleigh waves (Shapiro et al., 2006). For the station pair TAM–CCM, interstation Rayleigh waves are barely visible even for NCFs stacked over 10 years, whereas the 26 s PL signal is very clear. Stacking over time periods shorter than one month produces clear 26 s PL signals, because the interstation Rayleigh waves cannot be recovered.
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Figure 2. The observation of the 26 s microseism in the noise cross-correlation functions (NCFs) and spectrogram. (a) The reference NCF stacked from the years 2002–2011, the NCF in 2002, and the NCF in June 2002. The distance between the stations CCM and OBN is about 8554 km. (b) Spectrogram of the CCM station from 2002 to 2008. The NCFs are in the 20–50 s period, and the interstation surface waves are indicated in the boxes. Africa (TAM in the Geoscope network and KOWA in the GSN network), two stations in the United States (CCM and ANMO in the GSN network), and one station in Europe (OBN in the GSN network). TAM, CCM, and OBN are used for detecting clock drifts using the 26 s PL signals. ANMO and CCM are used to detect clock errors with the Stehly et al. (2007) method to validate the detection results with the 26 s PL signals. Clock errors at station KOWA detected with the 26 s PL signals are validated with teleseismic P-wave arrival residues. Continuous broadband vertical-component seismic records at TAM (2002–2013), OBN (2002–2010), CCM (2002– 2010), ANMO (2002–2010), and KOWA (2012–2013) are accessed from the Incorporated Research Institutions for Seismology Data Management Center (IRIS DMC; Fig. 1). All data are interpolated into 100 samples per second using the Wiggins interpolation method (Wiggins, 1976) after removing the instrumental responses. Then the data are segmented into one-day-long time series and resampled back to 20 samples
We detect the travel-time variation of the 26 s precursor by comparing the monthly NCFs with the reference NCFs, which are constructed by stacking over long time periods to enhance the signal-to-noise ratios (SNRs). Figure 2 shows a comparison among the reference NCF, the yearly NCF, and the monthly NCF between the station pair CCM–OBN (Fig. 2). On the NCFs, the 26 s precursors show up as prominent signals, while the interstation surface waves (in the boxes) are very weak (almost nonidentifiable in the monthly NCF). The spectrogram of the continuous vertical-component record at the CCM station from 2002 to 2008 is shown in Figure 2b. The coherent and continuous spectral peak near 26 s (0.038 Hz), which produces the strong 26 s precursor, is clearly visible (Fig. 2a; see also Shapiro et al., 2006). On the NCF, the phase of the 26 s precursor can be represented as φp e−iwT 2 −T 1 ikL2 −L1 ;
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in which i is the imaginary unit, w represents the angular frequency (which is 0:038 Hz × 2π), k represents its wavenumber for the 0.038 Hz Rayleigh wave, T 1 and T 2 are the arrival time from the 26 s source in the Gulf of Guinea to the two stations separately, and L1 and L2 represent the minor arc great circle distances from the 26 s source to the stations. If instrumental clock drift Δt occurs between the station pairs, then the phase becomes Δφp e−iwT 2 −T 1 ΔtikL2 −L1 :
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By comparing equations (1) and (2), the phase difference caused by the time shift is Δφp e−iwΔt :
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This can be used to detect clock drift between station pairs. The phase difference is measured by cross correlating the monthly NCFs with the reference NCF, which is similar to the Stehly et al. (2007) method. Meanwhile, due to the data gap and variable strength of the 26 s precursor, the SNR of the 26 s precursor on the NCFs varies for different months. Here, the SNR is defined as
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ma ; σa
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in which ma is the maximum amplitude of the monthly NCF and σ a is the mean square error for waveforms other than the 26 s precursor, defined as sP PT tp −tw 2 2 tp tw at −T at σa 5 : 2T − 2tw In equation (5), at is the amplitude of the monthly NCF at time t; −T and T represent the beginning and ending time of the NCFs, which are chosen to be larger than the interstation Rayleigh-wave travel time; tp is the time of the maximum amplitude; and tw is the half-window width. Wavetrains in the window are excluded to eliminate the impact of the 26 s precursor when computing the variance σ a of the noise in the NCFs. The definition in equation (5) prevents the SNR from becoming altered when the time windows are long enough to cover the entire wavetrain of the 26 s, which spans about 2000 s on the NCFs (Fig. 2a). We choose a 2400 s timewindow width (tw 1200 s) in this study, assuming that the waveforms outside the window are pure noises without contribution from the 26 s signal. The SNR of the 26 s precursor does not change substantially with longer tw. To obtain reliable measurements, we only choose the monthly NCFs of SNRs larger than 5.0 to conduct clock drift detection, whereas a much larger SNR (> 10) yields fewer points of measurement and a lower SNR leads to larger inaccuracy of clock drift.
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Time Shifts in the CCM and KOWA Station NCFs from the years 2002–2005 are stacked to obtain the reference NCF for each station pair, assuming there were no clock drifts in those four years. We correlate the monthly NCFs with their reference NCF in the period from 25 to 28 s to detect the possible clock drifts in each month (Fig. 3). For the station pair TAM–OBN, the time drifts are small (a few tenths of a second), with only a few exceptional months due to the low SNR of the 26 s precursor (Fig. 3a). However, for the two station pairs CCM–OBN (Fig. 3b) and CCM–TAM (Fig. 3c), obvious time shifts (about 2 s) occurred after 2006, persisting with decreasing clock drift from 2006 to 2009, suggesting that the clock at the CCM station was problematic during this time span. To verify that the clock at CCM had timing errors after 2006, we adopted the Stehly et al. (2007) method for the station pair ANMO–CCM. The station ANMO is managed by the Albuquerque Seismological Laboratory, which has expertise in maintaining U.S. and global seismic networks; and, therefore, the clock at ANMO is supposedly stable and accurate. High SNR interstation Rayleigh waves are retrieved in the monthly NCFs between the station pair ANMO–CCM (about 1400 km apart; Fig. 4). The reference NCF is also stacked for the years 2002–2005 (Fig. 4a) and then correlated with the monthly NCFs in the 10–20 s (Fig. 4b) and 5–10 s periods
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Time shifts detected by the 26 s synchronizing clock method between station pair (a) TAM–OBN, (b) CCM–OBN, and (c) CCM–TAM. The distances of the three station pairs are about 4412, 8554, and 9043 km, respectively. The reference NCF is stacked from the years 2002–2005 between each station pair.
(Fig. 4c), respectively. The black dots in Figure 4b,c are the measurements of the clock drift for the interstation surface waves on the negative side of the NCFs, and the red dots are for the positive side. The scattering in the clock drift measurements is less for the 5–10 s band as compared to the 10–20 s frequency band, consistent with Stehly et al. (2007). Clock drifts measured for both sides of the NCFs clearly show obvious time shifts after 2006. Moreover, the range of the clock drift at station CCM, measured from ambient seismic noise, is consistent with those measured with the 26 s PL signal, confirming the effectiveness of determining clock drifts with the 26 PL signal. In addition to detecting clock drifts as minor as 1–2 s, the 26 s PL signal can also be used to resolve much more severe clock errors. To demonstrate its capability in detecting large clock drifts, we study the NCFs between KOWA and TAM, on which the travel time of the 26 s precursor has obvious shifts in 2012 and 2013 (Fig. 5). KOWA is located in western Africa, and the quality of the clock timing and waveform data transmission could be compromised by the limited infrastructure near the stations. In Figure 5a, the interstation
Synchronizing Intercontinental Seismic Networks Using the 26 s Persistent Localized Microseismic Source CCM−ANMO (1404 km)
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Time shifts detected using the ambient noise synchronizing clock method between station pair CCM–ANMO. (a) The reference NCF in the 5–20 s period stacked from the year 2002 to 2005. The distance between the station pair is about 1404 km. The interstation Rayleigh waves are indicated in the boxes. Time shifts detected (b) in the 10–20 s period and (c) in the 5–10 s period. Black and red dots represent the time shifts detected in the negative and positive side of the interstation Rayleigh waves.
Rayleigh waves are also observable on the monthly NCFs due to the relatively short interstation distance (about 1350 km). However, they are relatively weak in the period from 20 to 50 s, and the 26 s PL signal is much stronger. The reference NCF is stacked from January 2012 to July 2012 for the 25–28 s frequency band. By correlating the monthly NCFs with the reference NCF, we find that there were about 150 s of time shift between the station pair after October 2012, which even increased to about 500 s in 2013 (Fig. 5b). However, we are not sure whether it is possible to retrieve the exact clock drift with this method because of possible cycle skipping problems (the measured time shift could be wrong by multiples of 26 s). For example, the time shift between August and September 2012 is probably caused by cycle creeping (Fig. 5b). Cycle skipping is an inherent problem for monochromatic (single frequency) waves, and therefore, the Stehly et al. (2007) method does not suffer from the problem of cycle skipping because of the broadband spectrum of the ambient seismic noise.
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Time shifts detected between the station pair KOWA– TAM in 2012 and 2013. (a) The comparison of the monthly NCFs in the 20–50 s period in (top) February 2012 and (bottom) February 2013. The distance between the station pair is about 1361 km. The reference NCF is stacked from January 2012 to July 2012 and the interstation Rayleigh waves are indicated in the boxes. (b) Time shifts detected using the 26 s synchronizing clock method.
To verify the large clock error at station KOWA, we used P-wave arrivals of three strong teleseismic events (M w ∼ 7). The result shows that the theoretical and observed P-wave arrival time match well for the earthquake on 25 March 2012 (Fig. 6a), whereas there is about a 150 s offset for the earthquake on 30 September 2012, which increases to about 500 s for the earthquake on 30 January 2013. These results confirm that substantial time shifts indeed occurred at the KOWA station in 2012 and 2013, with the amount of timing error being consistent with that measured with the 26 s PL signal.
Discussion In this study, clock drifts in seismometers are detected by correlating the 26 s precursor between the monthly NCFs and the reference NCF. Therefore, more precise clock drifts can be resolved with more accurate reference NCFs. It has been demonstrated that NCFs stacked over longer time periods are usually more stable and have higher SNR (Yang and Ritzwoller, 2008). However, reference NCFs stacked over too long a time period may cause bias if clock error occurs during the stacking time window. The NCFs will be different before and after the clock error occurs, and
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The measurements of teleseismic P-wave arrivals at the KOWA station for the three earthquakes (a) 22:37:06, 25 March 2012, Mw 7.1; (b) 16:31:35, 30 September 2012, M w 7.2; and (c) 20:15:43, 30 January 2013, M w 6.8. The waveform is filtered from 0.005 to 0.4 Hz and is synchronized by the initial time at 0 s. T denotes the theoretical P-wave arrival time calculated by the travel-time measurement software TTimes, whereas the real P-wave arrival times are indicated by the arrows.
therefore stacking of the different NCFs would produce an inaccurate reference NCF. Figure 7 shows the time shifts detected with the reference NCFs stacked from the years 2002–2009 (note that clock error occurred after 2006 at the CCM station). When compared with Figure 3, the timing error is observed to be almost the same for the station pair TAM–OBN (Fig. 7a), which is expected because the clocks at OBN and TAM were stable. However, there are overall time shifts for the station pair CCM–OBN (Fig. 7b) and CCM–TAM (Fig. 7c) caused by the biased reference NCFs. Theoretically, a reference NCF should be obtained by stacking the daily NCF over days without clock error. However, it is difficult to know beforehand on which days the clocks are accurate. This dilemma is also an issue for the method by Stehly et al. (2007) and can be solved through trial and error; that is, the first version of the reference NCFs can be obtained via stacking of all the daily NCFs, and the crude clock errors can then be measured. If appreciable clock errors are found on certain days, the daily NCFs on those days are then removed from the reference NCFs, and then more accurate clock drifts are stacked. In fact, the 26 s PL signals may be used to detect clock drifts for both intercontinental and regional station pairs. For
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Figure 7. Time shifts detected using the 26 s synchronizing clock method between station pairs (a) TAM–OBN, (b) CCM– OBN, and (c) CCM–TAM. This figure is the same as Figure 3 except all the reference NCFs are stacked from the year 2002 to 2009. regional station pairs, both the 26 s PL signal and interstation Rayleigh waves are observable, and a combination of both signals would improve the detection of clock drifts because clock errors affect all the waves on NCFs in a time-translation manner. For intercontinental station pairs, interstation Rayleigh waves become weak, and only the 26 s precursor plays a major role in the detection. Thus, the method in this article can be applied in regions where a strong 26 s PL signal is observed, such as in African, European, North American, and north Atlantic regions. However, for stations in South America, the 26 s PL signal is very weak (Oliver, 1962; Shapiro et al., 2006), and this method is not expected to work well. Moreover, the detection precision of the method using the 26 s PL signal changes due to temporal variations of the 26 s precursor and local noise level at the respective stations. Figure 8 shows the temporal variation of the SNR of the 26 s PL signal on the monthly NCFs between the station pairs TAM–OBN, CCM–OBN, and CCM–TAM. It demonstrates that the SNR depends on the number of days when waveform data is available. The SNR shows seasonal variation and is relatively low in the local winter of the northern hemisphere. The insufficient 26 s PL signal in the low SNR NCFs leads to
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Signal-to-noise ratios (SNRs) of the monthly NCFs between the three station pairs (a) TAM–OBN, (b) CCM–OBN, and (c) CCM–TAM. The red lines represent the SNR, and the black lines represent the number of daily NCFs in each month.
lower precision of clock drift measurements and could even result in a malfunction of the 26 s detection method. Thus, the SNR should be considered when determining a real clock error occurrence using the 26 s detection method. In our cases, because all the chosen stations have a good record of the 26 s PL signal, the detection precision of our measurements achieves a few tenths of a second. As compared to the ambient seismic methods (Stehly et al., 2007; Gouédard et al., 2014), detection of clock error with the 26 s PL signal suffers from cycle skipping due to the very narrow band of the PL signal (which could lead to measurements accurate up to multiples of 26 s). Therefore, extra constraints are needed from other data sets. Teleseismic P-wave arrivals are usually less than 10 s, as compared to theoretical predictions with 1D reference Earth models (such as the IASP91 model; Zhao et al., 2013); thus, teleseismic P-wave arrivals can be used to resolve the number of cycles (26 s) skipped. However, waveform records with such large clock errors are rarely adopted in seismological studies. Rather, there is not much need to account for the skipped cycles at all. The drift of interstation Rayleigh waves could be caused by changes of source distribution, physical changes in the medium, and instrumental clock error. Normally, changes in source distribution independently affect the positive and
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negative correlation time (Stehly et al., 2007). In this study, it has to be assumed that the 26 s PL source does not change spatially for this method to work. Though the nature of the 26 s PL source is not understood yet, the PL source in Kyushu Island is believed to be associated with hydrothermal vents of the Aso volcano (Zeng and Ni, 2011). Therefore, the 26 s PL source is probably also fixed in location (Xia et al., 2013). However, further studies are needed to investigate the enigmatic origin of the 26 s PL source and its spatial fixity (Shapiro et al., 2006). Although temporal seismic velocity variation has been observed from active sources or ambient noise studies (Li et al., 1998; Brenguier et al., 2008), the physical change of the medium is believed to occur mostly in the shallow crust of the region near the earthquake rupture zone, with very subtle amplitude (typically < 0:1%). Therefore, physical change of the medium may only produce minor time shift in surface-wave travel time near periods of 26 s, which samples almost the entire crust and should not be much affected by temporal velocity change in the shallow crust.
Conclusion In this study, we demonstrate that the 26 s microseismic signal can be used to detect instrumental clock drift. By correlating the 26 s precursor in the monthly NCFs with those in the reference NCFs, we find that there is about a 2 s time shift at the CCM station between 2006 and 2009 and that the clock drifts fluctuated during this period. We also detected about 150 s of time shifts in the KOWA station from September 2012, increasing to up to 500 s in 2013. These results are verified using the ambient-noise-based method and teleseismic P-wave arrival residuals. This method can be combined with the Stehly et al (2007) ambient noise method to detect time shifts for regional station pairs. The method can be applied to intercontinental station pairs in African, European, North American, and north Atlantic regions but not for stations in South America. This method should be useful when stations are sparsely distributed (such as GSN in the early 1990s). This method can also be used to calibrate the OBS in the north Atlantic by synchronizing them with precisely calibrated ground stations. This method may not be effective in real-time detection of instrumental clock drift less than 1.0 s, because the error of the method is a few tenths of a second (Fig. 3). Only with a few months’ measurement, the clock drift could be confirmed with confidence after statistical analysis. Therefore, the method is only useful for analyzing clock accuracy of historical seismic-waveform data. More accurate tools are needed for real-time assessment of clock errors.
Data and Resources Seismic waveform data used in this study were obtained from the Incorporated Research Institutions for Seismology Data Management Center (IRIS DMC).
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Y. Xia, S. Ni, X. Zeng, J. Xie, B. Wang, and S. Yuan
Acknowledgments The authors are grateful to the Incorporated Research Institutions for Seismology Data Management Center (IRIS DMC) for providing continuous waveform data. Figures were created with Generic Mapping Tools (Wessel and Smith, 1991). The study is supported by Chinese Academy of Sciences (CAS) (KZCX2-EW-121), MOST 973 program (2014CB845901), Hubei Province Science Foundation of Innovation, and CAS/State Administration of Foreign Experts Affairs (SAFEA) International Partnership Program for Creative Research Teams (Grant Number KZZDEW-TZ-05).
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State Key Laboratory of Geodesy and Earth’s Dynamics Institute of Geodesy and Geophysics Wuhan 430077, China
[email protected] (Y.X., S.N.)
Mengcheng National Geophysical Observatory University of Science and Technology of China Hefei 230026, China (J.X.)
CAS Key Laboratory of Computational Geodynamics University of Chinese Academy of Sciences Beijing 100049, China (X.Z.)
Key Laboratory of Seismic Observation and Geophysics Imaging Institute of Geophysics China Earthquake Administration Beijing 100081, China (B.W., S.Y.) Manuscript received 21 August 2014; Published Online 21 July 2015
