PRELIMINARY RESULTS FROM A DATA-INTENSIVE STUDY OF CHINA SEISMICITY TO DETECT
SMALL EVENTS AND IMPROVE LOCATION ESTIMATION FOR EVENT CLUSTERS
Richards, P. G., Heck, S., Schaff, D. P., Slinkard, M., and Young, C.
Lamont-Doherty Earth Observatory of Columbia University, New York, USA
Tel. 845-365-8389; Fax 845-365-8150; E-mail: richards@LDEO.columbia.edu
Sandia National Laboratories, Albuquerque, New Mexico, USA
We are beginning to get results from a study of the seismicity of China using a sparse network for which continuous data has been archived since the 1980s. Using cross-correlation methods, first to detect small events whose waveforms are similar to waveforms in a template archive, and second to measure the relative arrival times of signals from neighboring events as recorded at common stations, we find it possible to make estimates of the relative location of events within a cluster with precision at the level of a few hundred meters or even just a few tens of meters, even when the waveform data come from stations at far regional distances (several hundred km, or more). Our approach is part of a broad effort to take advantage of the information contained in large and ever-expanding archives of continuous seismic waveform data, for purposes of improving the characterization of seismic events occurring today.
In this paper, we first summarize a recently-completed study of the seismicity of Kazakhstan and parts of
Central Asia that successfully demonstrated the effectiveness of cross-correlation methods to detect small events. We then go on to report preliminary results in which cross correlation is used additionally to measure the relative arrival times of similar signals, with sub-sample precision, for clusters of events recorded at a sparse network of regional stations. We find such a network enables estimates of relative event locations to be made with much higher quality
(orders of magnitude improvement) than those made via the traditional approach, based typically on a larger number of stations, in which events are studied one-at-a-time.
SUMMARY OF A RECENT STUDY OF THE SEISMICITY OF KAZAKHSTAN AND NEARBY REGIONS
To explore several issues related to routine application of waveform correlation as a detector in broad regional monitoring, we studied the seismicity of a large region of Kazakhstan (35 – 60° N, 45 – 90° E) over a three year period,
2006 to 2008, using just three high-quality stations, namely the two arrays MKAR, BVAR, and the three-component station KURK. This work, described in Slinkard et al . (2014), relied upon the development by Sandia National
Laboratories of a high-performance distributed computing software package known as SeisCorr, which enabled the efficient search of multiple years of continuous data for the occurrence of waveforms similar to those of thousands of template events.
Our SeisCorr software found more than 19,000 detections during the 3 year period using 1938 templates, each of which was based upon a 25-second Lg window, passed in the band between 0.5 to 5 hz, for an event reported in the
Late Event Bulletin (LEB) of the CTBTO’s International Data Centre. Slinkard et al.
(2014) describe the reasons for this choice of template, and the procedure for setting a cross-correlation value for each template that corresponds to a predetermined false alarm rate (chosen to be not more than once per year per template). Figure 1 shows an example of a large cluster of more than 90 detections, made using a template recorded by the 9-component array MKAR.
The success in getting about 19,000 detections from 1938 templates raises the question of how to validate the detected events, i.e. how to be sure that they are indeed additional seismic events and not artifacts of processing.
Slinkard et al . (2014) used three different methods of confirming detections, namely: bulletin confirmation, multistation validation, and high correlation with a template. Roughly 25% of the detections turned out to be in the LEB or in the regional bulletin published by the Kazakhstan National Data Centre. (We note that the KNDC regional bulletin is primarily intended for earthquake studies and therefore excludes mine blasts, so it was to be expected that it did not confirm many of the detections of the type described in Figure 1.) Roughly 70% of the detections at a given station were confirmed by a detection at another station with an origin time that was the same to within a few seconds, and this turned out to be the best method for validating detections. Most of the events with a very high cross-correlation value
(> 0.7) were also confirmed by one of the other two methods.
The computational effort in this study of three years of seismicity in and near Kazakhstan was found to be quite manageable. With about 800 nine-channel waveforms for the MKAR array, about 500 (nine-channel) for the
BVAR array and about 1500 (thee-components) for the KURK station, the cross-correlation search for detections in three years of continuous data took only 2.5 days using a 32 processor network.
To summarize this study of the effectiveness of cross-correlation methods as a detector of small seismic events using a sparse regional network: the set of about 1900 templates were used to detect about ten times as many events over a three-year period; and most of these detections were found to be valid seismic events.
These are encouraging results, and they have led us to take the next steps in application of cross-correlation to broad areas of active seismicity, specifically to obtain improvements in event location.

Figure 1. Here are shown waveforms from a cluster of more than 90 seismic events, recorded in the period 2006 to
2008 at the primary array station MKAR of the International Monitoring System. They were detected via a search of the continuous data using cross-correlation against a template for an event reported in the LEB. On the basis of a
Google map of the source region and the histogram of event times, these events are taken to be mine blasts.
PRELIMINARY INVESTIGATION OF CAPABILITY TO OBTAIN PRECISE RELATIVE LOCATIONS
Several studies have demonstrated the utility of regional seismic signals, from a sparse network, in obtaining precise relative locations of neighboring events. The earliest such work using cross-correlation methods appears to have been in Iceland in the 1980s, as reported by Slunga et al. (1995). Figure 2 shows results reported by Schaff and Richards
(2004) for a series of earthquakes in Liaoning Province, China, for which high-quality relative locations were obtained from a small set of stations at around 500 km distance. Figure 3 shows the seismicity of a part of China for a period of more than seven years beginning in 2006, and here we report some early results on the application of cross-correlation methods to event sub-clusters within the smaller boxed region of dense seismicity west of station BJT.

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Figure 2. At top left is shown the location in Liaoning Province, China, of the Xiuyan sequence of earthquakes, which occurred in 1999; and four far-regional stations used to obtain precise relocations. At bottom left are the Lg-waves as recorded at the MDJ station. On the right are the double-difference relocations, obtained from measuring relative Lg arrivals (via cross correlation), and the mechanism of a nearby event in 2000. Adapted from Schaff & Richards (2004).
Figure 3. The seismicity of part of East Asia is shown for a period of slightly more than seven and a half years, as reported in the Late Event Bulletin of the IDC. The larger box includes many aftershocks of the Wenchuan earthquake of May 2008. We have studied sub-clusters of seismic events in the smaller box, relocating them on the basis of their
Lg-wave signals as recorded on just three stations, ULN, BJT, and XAN, operated jointly by the China Earthquake
Administration and the US Geological Survey.

Thus, for the sub-cluster shown in Figure 4, there are ten events, within an area about ten km across, separated typically by about two km from each other, which is comparable to the linear dimensions of the 95% confidence ellipses. The cross-correlation coefficients (on Lg only) range from 0.25 to 0.58. This precision is better than that of typical regional bulletins based upon tens or even hundreds of stations.
For the sub-cluster shown in Figure 5, there are nine events close together, within an area that is only about one km across. Remarkably, the precision here is at the level of only a few tens of meters. The Lg cross correlation coefficients s range from 0.25 to 0.7 and 60% of them are less than 0.5.
If such precision can be confirmed for other clusters of neighboring events, for example in dense aftershocks of a larger event, then such results have potential utility for the detailed study of earthquake interactions.
10 events; mean semi-axes of 95% error ellipses are a = 0.72 km and b = 1.15 km
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Figure 4. Shows the relative location of ten events clustered in the time period from December 2009 to August 2012 at about 39.0°N, 110.6°E, using the method of Schaff & Richards (2004). These locations, lying within the smaller box shown in Figure 3, are more than 300 km from BJT, 1000 km from ULN, and 600 km from XAN (the three stations at which Lg signals from these events were cross correlated to obtain precise relative arrival times). The mean sigma of the residuals is 0.26 s. The bootstrap errors (bubbles) agree well with the error ellipses.
DISCUSSION
It is truly remarkable that with just three stations at distances of several hundred km it is possible to achieve the precision of a few tens of meters as indicated in Figure 5, for a sub-cluster of events occurring within a region about one km across. The Lg -wave is thought to be composed of shear waves trapped within the crust due to their low phase velocity (which is too low to allow propagation into the mantle as either P or S ). The system of interfering waves comprising Lg is discussed in some detail by Vogfjörd and Langston (1990, 1996), and for a very simple (crude) crustal model the travel time for several different multiples is as shown in Figure 6. Layering within the crust would add more multiples.
Our procedure for estimating relative event location is based currently on the assumption that, for two events at slightly different distances from a common station, the travel-time difference ∆ T associated with the maximum crosscorrelation value can be interpreted as due entirely to a delay ∆ T in the whole Lg wavetrain, so that the difference in distance is just ∆ T ☓ 3.3 km for ∆ T in s and Vs = 3.3 km/s as in Figure 6. Though this may be an adequate assumption when the Lg path from two events to a common station is very similar, as for event pairs in Figure 5, it may be less adequate for purposes of interpreting the cross-correlation measurement ∆ T for events with greater separation. For them, as indicated by the changes in travel time of interfering waves as shown in Figure 6, there is the possibility of a change in waveform that may need to be taken into account in a best interpretation of the ∆ T measurement.
Nevertheless, making the simpler interpretation that ∆ T is due only to a time shift in the whole waveform, we still get much more precise locations (as for example in Figure 4) than can be obtained by the traditional approach of locating events separately (one at a time).

9 events; mean semi-axes of 95% error ellipses are a = 0.03 km and b = 0.04 km
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Figure 5. Similar to Fig. 4 but here for the relative location of nine events clustered in the time period from April 2006 to August 2006 at about 39.2°N, 112.5°E. These locations are about 500 km, 1000 km, and 550 km, from stations BJT,
ULN, XAN, respectively. The mean sigma of the residuals is only 0.012 s.
Figure 6. Travel times as a function of distance D, of shear waves within a homogeneous crust of thickness 40 km having shear velocity Vs = 3.3 km/s, from a source at 10 km depth. Times are with reference to a horizontally-traveling shear wave. After the direct arrival from 10 km depth, the next arrival is SMS (downgoing from the source and reflected at the Moho), followed shortly afterwards by sSMS. The travel times for four later multiples are also shown.

ACKNOWLEDGEMENTS
The first author appreciates collaborations with seismologists in Kazakhstan over more than twenty years. This work was supported by the US Defense Threat Reduction Agency under award number HDTRA1-11-1-00027 to Columbia
University.
REFERENCES
Schaff, D.P., and Richards, P.G. Lg -wave cross correlation and double-difference location: application to the 1999
Xiuyan, China, sequence, Bulletin of the Seismological Society of America , 94: pp867--879, 2004.
Slinkard, M., Schaff, D.P., Mikhailova, N.N., Heck, S., Young, C., and Richards, P.G. Multi-station validation of waveform correlation techniques as applied to broad regional monitoring. Paper accepted for publication,
Bulletin of the Seismological Society of America , 2014.
Slunga, R., Rögnvaldsson, S., Bödvarsson, R. Absolute and relative location of similar events with application to microearthquakes in southern Iceland, Geophysical J. Int.
, 123: pp409—419, 1995.
Vogfjörd, K.S., and Langston, C.A. Analysis of regional events recorded at NORESS, Bulletin of the Seismological
Society of America , 80: pp2016--2031, 1990.
Vogfjörd, K.S., and Langston, C.A. Characteristics of short-period wave propagation in regions of Fennoscandia, with emphasis on Lg , Bulletin of the Seismological Society of America , 86: pp1873--1895, 1996.
PRELIMINARY RESULTS FROM A DATA-INTENSIVE STUDY OF CHINA SEISMICITY TO DETECT
SMALL EVENTS AND IMPROVE LOCATION ESTIMATION FOR EVENT CLUSTERS
Richards, P. G., Heck, S., Schaff, D. P., Slinkard, M., and Young, C.
Lamont-Doherty Earth Observatory of Columbia University, New York, USA
Tel. 845-365-8389; Fax 845-365-8150; E-mail: richards@LDEO.columbia.edu
Sandia National Laboratories, Albuquerque, New Mexico, USA