Physics of the Earth and Planetary Interiors 175 (2009) 8–16

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Physics of the Earth and Planetary Interiors
journal homepage: www.elsevier.com/locate/pepi

GRiD MT (grid-based real-time determination of moment tensors) monitoring
the long-period seismic waveﬁeld
Hiroshi Tsuruoka ∗ , Hitoshi Kawakatsu, Taku Urabe
The Earthquake Research Institute, The University of Tokyo 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japana r t i c l e

i n f oArticle history:
Received 20 August 2007
Accepted 29 February 2008
PACS:
91.30.Bi
Keywords:
Seismic sources
Automated real-time system
Moment tensor inversion

a b s t r a c t
We have developed and implemented a new grid-based earthquake analysis system that continuously
monitors long-period seismic waveﬁeld of 20–50 s recorded by broadband seismometers. The new analysis system automatically and simultaneously determines the origin time, location and seismic moment
tensor of seismic events within 3 min of their occurrence. This system has been in operation since 2003
at the Earthquake Research Institute (http://wwweic.eri.u-tokyo.ac.jp/GRiD MT/), and the locations andorigin times are usually obtained within 3 s and 20 km away from the earthquake catalog values determined by the Japan Meteorological Agency (JMA). In addition, moment tensor solutions are comparable
to the network solutions manually obtained. This new system should enable us to monitor long-period
seismic waveﬁeld continuously further to help identifying long-period (or low-frequency) events which
are undetectable by the conventional monitoring of short-period seismic waveﬁelds.
© 2009 Elsevier B.V. All rights reserved.

1. Introduction
Until now, many automated waveform data processing systems have been developed that use waveform inversion techniques
to determine earthquake source mechanisms (i.e., CMT solutions). For earthquakes with moment magnitude larger than 5,
moment tensors are routinely determined by several institutes
using long-period waveforms observed by the worldwide seismic networks (Dziewonski et al., 1981; Sipkin, 1994; Kawakatsu,
1995).
For regional earthquakes, there are several attempts to estimate
moment tensors using regional broadband networks. For example,
Thio and Kanamori (1995) analyzed moment tensors of local earthquakes using TERRA-scope regional broadband surface waveforms.
Dreger and Helmberger (1993) developed a broadband body wave
inversion to estimate regional earthquakes using three-component
sparse network data. Moment tensor of regional earthquakes invarious countries and areas are now determined using this algorithm. In Japan, using the Japanese regional broadband seismic
network (called F-net) Fukuyama et al. (1998) determined routinely moment tensors of small earthquakes (M > 3.5). Bernardi etal. (2004) determined moment tensor of earthquakes occurred in
the European-Mediterranean region.

∗ Corresponding author.
E-mail address: tsuru@eri.u-tokyo.ac.jp (H. Tsuruoka).
0031-9201/$ – see front matter © 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.pepi.2008.02.014

As all of these systems are started after receiving information
about earthquake occurrences, they basically process data that isalready saved on hard disks, rather than a real-time data ﬂow ofwaveforms. For this reason, it is difﬁcult to reduce the time lag
between the earthquake occurrence and the determination of theearthquake mechanism, as these solution systems must wait until
they receive information concerning the earthquake’s origin timeand location.
To circumvent this situation, Kawakatsu (1998) suggested a
possibility of monitoring the long-period waveﬁeld in real-timeusing a grid-based search algorithm. Tajima et al. (2002) haveapplied this approach to Berkeley Digital Seismic Network (BDSN)
data with a 0.25◦ mesh of grid points. Recently, Ito et al. (2006,2007) employed a similar technique that lead them to reportvery-low-frequency earthquakes occurring in the transition zoneof the subducting plate interface along the Nankai subduction
zone in southwest Japan. Auger et al. (2006) presented a comprehensive processing tool for the real-time analysis of moment
tensors to monitor very long-period activity of the Stromboli volcano using a 62-node Linux cluster. Although these approaches
do not use prior earthquake information, they use the waveform
data stored on hard disks, rather than the actual real-time ﬂow ofdata.
In this study, we introduce a new analysis system that uses
waveform data in memory and distributed over a computer network to continuously monitor the long-period seismic waveﬁeld.
Kanamori et al. (1999) pointed out that such a continuous monitoring of waveforms enhances the reliability of the system because ofminimization of the workload.

H. Tsuruoka et al. / Physics of the Earth and Planetary Interiors 175 (2009) 8–16

2. Method

9

To search for the best solution, in the present approach, insteadof the quantity estimated in (4), a variance reduction (VR)⎡2.1. Inversion algorithm
A moment tensor inversion is considered for cases where the origin time and the source location are given. The observation equationof such an inversion is provided below, with s denoting the source
and k the station:
Gisk (t)msi = dk (t)

(1)

⎤

⎢
⎥
1 − res⎥ × 100(%)
VR = ⎢  ⎣⎦2(dk (t)) dt(5)k

has been used in the actual monitoring. VR is an indicator of the ﬁt
between the observed waveform and the theoretical waveform.

where Gisk (t) is the theoretical waveform (Green’s function) for ith

component of the moment tensor, and dk (t)the observed waveform, and the summation convention is assumed for subscripts.
This equation is generalized for the three components of groundmotion at station k. msi refers to the ith component of the moment
tensor at a source s. The corresponding normal equation is:Asji msi = bsj
where



Asji =

(2)



Ask ,
k ji

bsj =



bsk ,
k j

Ask
=
ji



Gjsk (t)Gisk (t)dt,

and= Gjsk (t)dk (t)dt. Hereafter, the superscript for the source, s,bsk
jis omitted from equations when not confusing. The least squares
solution of the moment tensor is then given by:m̂ = A−1 b(3)

In this equation, A is given once a structural model is speciﬁed as
it can be provided solely by the Green’s functions. So, we precompute the A matrix and therefore A−1 for each source-station pair.
The inversion solution can therefore be obtained if b can be estimated from the Green’s function and the observed waveform. Thisshows that the procedure for estimating a moment tensor when asource location is given is almost equivalent with the calculating
the cross-correlation between Green’s functions and data. That is
the most time-consuming processing. In addition, the prediction
error of the observed waveforms for the obtained moment tensor
is also easily calculated:
res =




2.2. Real-time analysis system
2.2.1. Outline of the system
Our system consists of programs that perform the following
tasks: (1) receiving of data (recvt, order), (2) ﬁltering and conversion of data from the shared memory into the standard output(shmdump), (3) moment tensor inversion (rmtinv), (4) detectionof an earthquake (EventDetect.pl), (5) visualization (mtplot, shmx,mtx) and dissemination programs (e-mailing, www). All these programs work together to form the complete system. Programs (1)and (2) were prepared using those from the WIN system (Urabe,1994). The WIN system continuously collects and distributes realtime waveform data among nation-wide stations and data centers
in Japan. The WIN format, which is based on time-stamped variablelength data packets, is used for both transmission and storageof waveform data. This system also has various processing package, such as the hypocenter determination and the picking of P
and S arrivals. Programs (3)–(5) were developed in the current
study. Since all of these programs have been developed for clearlydeﬁned modular functionality, the system is capable of ﬂexibly
accommodating changes and modiﬁcations. Fig. 1 shows how these
programs are interrelated. The programs have also been developed to facilitate ofﬂine processing of data as well as real-timeprocessing.

2dk (t) dt− bt A−1 b(4)k

Thus, for a given “virtual” source s, both the moment tensor (3)and the residual (4) can be easily estimated. This allows us toperform a grid-search type approach to ﬁnd a moment tensor solution and a location of a virtual source which is most consistent(i.e., gives the least prediction error) with the observed waveﬁeld.
Kawakatsu (1998) suggested the possibility to do such a grid-searchin real-time every 1 s to monitor the regional long-period seismic
waveﬁeld.

2.2.2. System conﬁguration
As this earthquake analysis system requires a high level ofcomputational performance and computers with large memory
resources, we used PCs with the speciﬁcations shown below for
the actual monitoring: CPU: Intel Xeon 3.06 GHz; Memory: 1 GB
PC2100 DDR SDRAM. For both the system and memory, bandwidthis 4.2 GB/s. A system with insufﬁcient memory bandwidth, such as a
Pentium III (1.06 GB/s), is not capable of real-time processing. Eachof the PCs is connected to a broadcast segment to which data fromthe satellite or terrestrial is distributed, so the PCs can easily shareseismic waveform data.

Fig. 1. Outline of the real-time earthquake analysis system used in this study.

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H. Tsuruoka et al. / Physics of the Earth and Planetary Interiors 175 (2009) 8–16
Table 1
Velocity structure used for Green’s function.
Thickness (km)P velocity (km/s)

S velocity (km/s)

Density

Qp

Qs

600
600
600
600
600

300
300
300
300
300

3

(kg/m )
3
15
15
67
∞

Fig. 2. Earthquake detection algorithm. An event is detected at 140 (s) in this case.

5.5
6.0
6.7
7.8
8.0

3.14
3.55
3.83
4.46
4.57

2300
2400
2800
3200
3300

records, which are used directly to reduce processing time rather
than integrating to displacement as commonly done.

2.3.1. Period range
In ordinary moment tensor inversions, the frequency band used
for analysis may be modiﬁed to adjust for the earthquake magnitude. Our system, however, employs a ﬁxed frequency band
because it performs continuous real-time processing. Considering
the degree of seismic activity in the monitored area, long-period
waveﬁelds of 20–50 s are used in this study. A recursive digital ﬁlter (Saito, 1978) is used as the band-pass ﬁlter for the waveform, assuch a digital ﬁlter works well with real-time processing. The broadband seismometers installed at Japanese stations produce velocity

2.3.2. Record length, station number and grid size
The record length, number of stations, and number of virtual
sources need to be set in such a way that enables real-time processing. Considering that F-net determines a solution at three stations
close to the hypocenter (Fukuyama et al., 1998), it is consideredherein that the monitoring area is where real-time processing ispossible at three stations. For a seismic wave that travels 400 km
distance, P, S, surface, and other major waves arrive at stations
within 2 min. For this reason, the record length used by the analysis is set for 2 min. As re-sampling of data is performed every
second, the system collects 120 data points in total. As we used
long-period waveforms (20–50 s), there is no need for high resolution in determining the hypocenter. Thus, virtual sources are

Fig. 3. Locations of stations (ﬁlled triangle) and virtual sources (plus). Three monitoring regions (R0010, R0015, R0100) are showed.

Fig. 4. Monitoring results from January 2004 to December 2006. The focal mechanisms are lower hemisphere projection and are scale by the moment magnitude.

2.3. Monitoring details

H. Tsuruoka et al. / Physics of the Earth and Planetary Interiors 175 (2009) 8–16

located every 0.1◦ horizontally and every 9 km vertically (depth),
which are both signiﬁcantly less than the minimum wave length ofthe data used. Under these conditions, our tests show that realtime data processing is possible for up to approximately 7000
virtual sources. A single PC is therefore able to cover an area of
2.4◦ × 2.4◦ × 90 km ( = 25 × 25 × 11 = 6875 virtual sources,) in maximum. However improved computer performance in the future will
enable more than three stations to be used for monitoring or morethan 7000 virtual sources to be processed.
2.4. Earthquake detection algorithm
As this system does not depend on a preliminary report of theseismic event occurrence, the system itself is required to detects
the earthquake. The system monitors the VR of every m̂ which isdetermined every second, but the actual event detection is issued
based on the maximum value of VRs for speciﬁed time windows
whose length is w (Fig. 2). If the maximum value of VR within a
speciﬁed time window (Zj for jth time window) is larger than boththose of the previous time window (Zj−1 ) and the next time window (Zj+1 ), then an event is detected; i.e., in a case where Zj meetsthe condition below, this system decides that an earthquake has
occurred:Zj−1 ≤ Zj ≥ Zj+1 ,Zj ≥ VR0(6)

where VR0 is the event detection threshold, which is set to 65.0.
Fig. 2 shows how an earthquake is detected. As w is set to 20 s, the11system recognizes an earthquake occurrence on average 30 s after
VR takes a maximum value.
3. GRiD MT
We have implemented the real-time monitoring system basedon the procedures described above to monitor the seismicity of
the Paciﬁc Ocean side of the northeast Japan, and have been operating the system since April 2003. The analysis system is termed
GRiD MT (grid-based real-time determination of moment tensors),
and real-time solutions have been available at the following URL:
http://wwweic.eri.u-tokyo.ac.jp/GRiD MT/. As the web site displays
5 areas monitored, we discussed 3 areas monitored in this paper.
Because we do real-time monitoring with ﬁxed three stations, but
the other 2 areas is unstable in waveform data acquisition with ﬁxed
three stations from 2004 to 2006.
3.1. Monitoring area
All the broadband stations used in the present study are shownin Fig. 3. All the data is collected and distributed in real-time using asatellite telemetry system (Takano et al., 2001), which was recently
changed to a terrestrial telemetry system. In terms of the area
to be monitored, there is a restriction on the number of virtual
sources that enables computation of (3) and (5) within a periodof 1 s. Three different monitoring areas are deﬁned, each containing 25 × 25 × 11 = 6875 virtual sources, as shown in Fig. 3. In the

Table 2Detected event list larger than Mw = 5.0.
No.

Origin time

LON

LAT

DEP

MAG

Mechanism

VR

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40

2004/01/23,18:01:33
2004/02/04,15:08:27
2004/03/11,11:34:56
2004/04/04,08:01:59
2004/08/10,15:13:29
2004/08/21,05:33:02
2004/08/21,06:19:54
2004/09/01,11:49:27
2004/10/06,23:40:44
2004/10/17,02:19:17
2004/10/17,03:54:42
2004/11/02,22:04:24
2004/12/19,14:22:45
2004/12/29,22:58:45
2005/01/01,05:13:47
2005/02/16,04:46:37
2005/04/04,02:57:09
2005/04/11,07:22:20
2005/05/27,17:07:22
2005/06/20,01:15:20
2005/07/23,16:35:00
2005/08/08,00:06:45
2005/10/12,13:30:12
2005/10/19,20:44:44
2005/10/22,22:12:47
2005/10/24,18:34:48
2005/12/02,22:13:05
2005/12/05,03:03:12
2005/12/05,07:20:20
2005/12/17,03:32:13
2006/01/18,23:25:23
2006/02/01,20:35:54
2006/02/03,13:37:33
2006/02/03,15:10:02
2006/03/12,07:06:43
2006/03/13,13:06:10
2006/03/13,13:15:31
2006/07/01,08:28:09
2006/07/06,02:08:39
2006/09/07,03:06:30

141.1000
141.9000
141.2000
141.4000
142.3000
141.9000
141.9000
141.9000
140.0000
141.7000141.6000
142.9000
141.8000
142.5000
141.1000
139.9000
141.9000
140.6000
140.8000
140.6000
140.1000141.6000
142.1000
141.2000
141.2000
142.5000
142.6000
143.0000
142.9000
142.4000
142.4000
140.1000
141.9000
141.9000
142.6000
141.9000
141.9000
142.4000
142.6000
141.3000

37.3000
40.1000
36.3000
36.3000
39.7000
34.9000
34.9000
36.9000
36.1000
36.1000
36.2000
38.8000
36.1000
38.3000
36.7000
36.0000
37.4000
35.8000
38.6000
35.8000
35.6000
36.3000
37.8000
36.4000
37.1000
38.3000
37.9000
40.2000
37.9000
38.3000
37.8000
35.7000
36.0000
36.0000
40.1000
35.9000
35.9000
38.3000
40.2000
35.5000

59.0
68.0
41.0
41.0
50.0
23.0
23.0
41.0
59.0
32.0
32.0
5.0
32.0
41.0
95.0
50.0
41.0
50.0
86.0
50.0
59.0
32.0
41.0
41.0
50.0
41.0
41.0
41.0
32.0
41.0
23.0
86.0
32.0
32.0
41.0
32.0
32.0
41.0
41.0
50.0

5.2
5.2
5.3
5.9
5.6
5.3
5.1
5.7
5.6
5.4
5.7
5.1
5.1
5.6
5.0
5.3
5.1
5.9
5.0
5.6
5.8
5.4
5.1
6.2
5.5
5.0
6.5
5.0
5.2
6.0
5.4
5.0
5.8
5.3
5.0
5.1
5.0
5.2
5.2
5.2

(206,69,87)(356,49,90)(15,68,79)(28,71,92)(15,74,98)(173,62,60)(180,58,67)(29,69,93)(4,67,88)(26,55,85)(31,63,91)(74,88,141)(34,56,97)(15,67,90)(195,81,−87)(37,73,77)(64,50,95)(5,77,95)(206,88,70)(16,77,93)(15,62,102)(26,60,90)(21,68,89)(29,74,99)(24,72,86)(13,63,86)(20,68,88)(37,66,105)(30,50,98)(12,66,86)(209,66,100)(231,83,−14)(25,51,85)(23,49,80)(26,74,92)(33,53,90)(32,50,89)(11,64,85)(26,71,101)(27,71,97)

91.81
85.13
90.69
91.41
91.45
72.62
70.51
90.75
75.30
89.51
90.78
86.13
89.83
95.17
79.32
78.12
87.90
79.96
87.01
78.53
76.27
91.31
95.25
90.33
92.52
94.26
95.05
93.44
91.69
94.29
82.66
76.75
87.82
87.71
93.82
85.89
86.76
94.47
93.07
77.83

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H. Tsuruoka et al. / Physics of the Earth and Planetary Interiors 175 (2009) 8–16

actual monitoring, small parts of these three areas are overlapped.
The details of each area are given below:• R0010: 140.8–143.2E, 35.8–38.2N, 5–95 km depth (stations used:
N.KSKF, N.YMZF, E.TSK)• R0015: 139.5–141.9E, 34.1–36.5N, 5–95 km depth (stations used:
N.YMZF, N.ONSF, N.JIZF)• R0100: 140.8–143.2E, 37.8–40.2N, 5–95 km depth (stations used:
N.TMRF, N.GJMF, N.KSKF)

4. Results and performance
GRiD MT has been monitoring the area R0010 starting from
April, 2003, Fig. 4 shows the monitoring results obtained from January 2004 to December 2006. In all, 550 earthquakes are detected,
including some overlaps. Table 2 lists the determined source parameters of selected events with moment magnitude larger than 5.0,
and Fig. 5 shows an example for an earthquake, Mw = 6.2.
4.1. Comparison with JMA catalog

3.2. Green’s functions and moment tensors
For computation of the Green’s function, eight-component seismograms are computed in advance, namely TSS , TDS , RSS , RDS , RDD ,ZSS , ZDS , and ZDD (for the notation of these terms, see Herrmann
and Wang, 1985), for depths at every 9 km from 5 to 95 km andfor epicentral distances at every 5 km from 30 to 500 km, covering the ﬁrst 300 s from the origin time (which is counted as 0 s).
We use Saikia’s (Saikia, 1994) frequency wave number integration
method. The obtained components were then saved onto a disk. The
velocity structure used to compute the Green’s function is the sameas the one used by the Japanese F-net moment tensor inversion(Fukuyama et al., 1998) as listed Table 1.
The current GRiD MT solves only for the deviatoric components
of a moment tensor, and the 8-component synthetic seismograms
stored on the disk are converted to Green’s functions corresponding
to the 5 deviatoric components which are stored in the memory.

As GRiD MT does not rely upon a preliminary report of theearthquake information, it is important to check the location andorigin time with catalogues determined by dense short-period seismic networks. We compare GRiD MT results with the earthquake
catalog data provided by the Japan Meteorological Agency (JMA).
Fig. 6 shows the differences between the locations obtained by GRiD
MT and those derived from the JMA catalogue. The horizontal gridsize for the virtual sources is 0.1◦ , and the results show horizontal
differences that are generally less than two grids (averaged differences and standard deviations are 19.4 ± 11.9 km). There appears
to be a systematic bias in the estimate of the epicentral longitude;
earthquakes are located further offshore. We attribute this to theinadequacy of the used structural model. The seismic velocity inthe frontal arc region is likely to be slower than the main island arc
part. The usage of a faster reference model should result in mislocation further offshore. The vertical (depth) differences are also

Fig. 5. Example of the monitoring result for an earthquake 2005/Oct/19 20:44:44 Mw = 6.2. (a) The focal mechanism determined by GRiD MT. The solid lines and dashedlines represent the observed and synthetic waveforms, respectively. (b) Monitoring outputs of variance reduction and magnitude(=Mw ). Raw waveforms (left) and waveforms
ﬁltered in the 20–50 mHz band (right). (c) Snap shots of moment tensors and variance reduction. Time in each window are listed in the left bottom. Box shows the best source
location. The size of focal mechanism are scaled by Mw × (0.5 + 0.5 × (VR/100)).

H. Tsuruoka et al. / Physics of the Earth and Planetary Interiors 175 (2009) 8–16

13

Fig. 6. Horizontal and depth differences of the GRiD MT locations and the Japan Meteorological Agency (JMA) catalogues. The difference pairs of longitude and latitude areplotted (top). There appears a systematic bias in the longitude difference. Histogram of the longitude difference (bottom left), latitude difference (bottom center) and depth
difference (bottom right).

within two vertical grid points. The usage of a more appropriate
structural model or a 3-D model would reduce these mislocations.
Fig. 7 shows differences of origin time of GRiD MT and JMA
catalogue. The differences of origin times are 3 s or less (averaged
differences and standard deviations are −0.3 ± 3.2 s). As GRiD MT

is performed in a frequency band of 20–50 s, these results are considered to be of high precision.
Fig. 8 compares computed magnitudes with those from the catalog data. Although both are in agreement in general, differences andvariations are greater and scattered for smaller earthquakes. The
maximum differences in magnitude between the two methods are
0.6 (averaged differences and standard deviations are 0.00 ± 0.27).
It is well known that the magnitude estimated by JMA tend to belarger than the corresponding moment magnitudes for large earthquakes (Takemura, 1990). Although the average difference is 0.00,
for relatively large events (Mj > 4.5), Mj is about 0.1 larger on the
average than Mw of GRiD MT.
All in all, we conclude that GRiD MT obtains origin time andlocations with a high level of precision both in time and space.
4.2. Comparison with F-net solutions

Fig. 7. Origin time of differences of GRiD MT and JMA catalogues.

The broadband seismic network F-net operated by the National
Research Institute for Earth Science and Disaster Prevention (NIED)
provided on line their moment tensor solutions. Although the F-net
solutions assume a preliminary epicenter provided by JMA, they
do estimate the depth of earthquakes. We compare GRiD MT solutions with the manually determined F-net solutions available on
the Internet. Fig. 9 shows a comparison of the obtained moment
magnitudes. The magnitudes obtained by the two methods havedifferences of approximately 0.1 or less (averaged differences andstandard deviations are 0.03 ± 0.08), and are very similar. Differences in depth estimated by the two methods are shown in Fig. 10;
again the similarity is strong, with differences being less than 2
vertical grid points. With respect to depth, the R0010 and R0100
monitoring areas results tend to be shallower than the corresponding F-net results. We attribute this trend to the fact that F-net
solutions use the assumed epicenters obtained by JMA.

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H. Tsuruoka et al. / Physics of the Earth and Planetary Interiors 175 (2009) 8–16

Fig. 8. Correlation of the magnitude of GRiD MT and JMA catalogues (left). Histogram of the difference in magnitude by GRiD MT and JMA catalogues (right).

Fig. 9. Correlation of the moment magnitude of GRiD MT and F-net catalogues by NIED (left). Histogram of the difference in moment magnitude by GRiD MT and F-net
catalogues (right).

We next compare earthquake mechanisms. In comparing mechanism solutions, we use an index of similarity of two moment
tensors introduced by Kuge and Kawakatsu (1993). This index is the
cross-correlation of P-wave radiation patterns. The index value is 1

if the two mechanisms are identical, and −1 if they are completely
different. The index value is not affected by the size of the earthquake. The comparison results are shown in Fig. 11. The similarity
is 0.8 or higher for most of the data, indicating that the obtained
mechanisms are very similar.
4.3. Threshold of the VR

Fig. 10. Histogram of the depth differences of GRiD MT and F-net catalogues.

The VR threshold is set at VR = 65.0 for this study. This setting enables the detection of earthquakes with a magnitude oflarger than approximately 4.5, as deﬁned by JMA magnitude (notethat Mj is larger than Mw by about 0.2). To detect smaller earthquakes, we need to lower the threshold value; however, a lower
threshold value results in more false detections. Fig. 12 shows therelation between VR threshold and false alarm rate. With the VR
threshold at 65.0, 26.4% of the detected cases by GRiD MT are false
detections. We have examined the possibility that some of these
events are actually real “unusual” events, but could not identify
any. The presence of unusual (e.g., very-low-frequency) earthquakes are not established along the Paciﬁc coast of northeasternJapan.
Although a larger VR threshold reduces the number of false
detections, it also results in the fewer earthquakes detections. Also
VR can become as high as 50 due to teleseismic earthquakes. To
detect as many earthquakes as possible while reducing the numberof false detections, GRiD MT might need to cooperate with shortperiod signals. The use of a larger number of stations might solve
this problem of teleseismic events.

H. Tsuruoka et al. / Physics of the Earth and Planetary Interiors 175 (2009) 8–16

Fig. 11. Histogram of the mechanism resemblance of GRiD MT and F-net catalogues.
Resemblance is the cross-correlation of P-wave radiation patterns. The index valueis 1 if the two mechanisms are identical, and −1 if they are completely different.

Fig. 12. Probability that a signal is a false earthquake. With the VR threshold at 65.0,
26.4% of the detected cases are false detections.

In Fig. 13, the vertical axis indicates the ratio of total numberof earthquakes (with magnitudes ≥M) determined by JMA to that
detected by GRiD MT, and the horizontal axis refers to the JMA magnitude. The three lines show cases that correspond to three different
VR thresholds, 50 (), 65 (o), and 70 (+). With the threshold set at
65, the value chosen for our monitoring, GRiD MT detects 90% ofearthquakes with magnitude ≥4.5. For the remaining 10% of events,
the long-period waveﬁeld was found to be contaminated by surface
waves of large teleseismic earthquakes. Detection rate decreased forearthquakes with magnitude larger than 6.
5. Discussion
We established a functioning streaming moment tensor methodthat works well with our initial deﬁnition of operating parameters. However, there are areas that can be improved, and which

15

Fig. 13. Detection rate of events for JMA catalogue. The vertical axis indicates theratio of total number of earthquakes with magnitudes ≥M determined by JMA tothat detected by GRiD MT, and the horizontal axis refers to the JMA magnitude. The
three lines show cases that correspond to three different VR thresholds, 50 (), 65(o), and 70 (+).

are the focus of future works such as how to deal with missing
data, establishing multiple distributed PC processing, adjusting themonitoring period range to account for larger earthquakes, and forspecialized monitoring of long-period events.
In terms of missing data, the most important problem to consider at this point is how to deal with missing waveform data. In
such a case, our system is unable to correctly detect an earthquake.
As this problem arises from the fact that data is currently being usedfrom three ﬁxed stations, we need to consider building and employing a new algorithm that monitors three stations out of more wheredata is collected.
In terms of Multiple-PC processing, to expand the coverage area
(e.g., entire Japan), multiple PCs will be needed in distributed processing. The analysis results from all PCs would then need to be
integrated effectively and correctly. In the present study, two or
more different monitoring areas partially overlap each other, andmultiple sets of information are detected for same earthquakes. Thisoverlapping of information must be avoided.
In terms of monitoring period range, the current system is moreor-less successful in monitoring the period range of 20–50 s. The
system may not be able to properly detect an earthquake larger
in size, as the assumption of a point source does not hold true forsuch a large earthquake. The monitoring period range would need
to be adjusted depending on actual monitoring purposes. For larger
earthquakes, it will be necessary to use longer period and far station
data (Fukuyama and Dreger, 2000).
In terms of monitoring “long-period” events, seismology has
now experienced plenty examples of “long-period” seismic events(e.g., Kanamori and Given, 1982; Kawakatsu et al., 1994; Ekström
et al., 2003) which may not be detected using conventional monitoring of short-period seismic networks. Recently, Ito et al. (2007)
added new such an example by employing a grid-based approachto discover ultra-low frequency earthquakes in the deeper portionof the seismogenic zone beneath southwest Japan, where majorinterplate earthquakes are expected in the near future. With GRiD
MT monitoring a long-period waveﬁeld, such “long-period” events
can be also regularly monitored. Although in our current system,
the isotropic moment tensor and the single force component are
not monitored, such inclusion is possible and is planned in the

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H. Tsuruoka et al. / Physics of the Earth and Planetary Interiors 175 (2009) 8–16

near future which should further expand our ability to detect ‘longperiod” events in real-time.
6. Conclusions
The GRiD MT described in this paper utilizes real-time broadband seismic waveform records collected and distributed by asatellite or terrestrial telemetry system. It continuously monitors
long-period waveﬁelds of 20–50 s period to automatically determine earthquake origin times and locations as well as mechanisms(moment tensor solutions). GRiD MT is the ﬁrst of its kind in theworld, and after three years of monitoring we reached the following conclusions: (1) GRiD MT detects earthquakes and determines
the source parameters with a high level of precision and complete
automation within 3 min of the earthquake occurrence. (2) The origin time and locations obtained using our system are very similar
to those of JMA catalogue. (3) The mechanism and moment magnitude obtained by our system are very similar to the corresponding
F-net solutions determined by NIED.
The above conclusions suggest that GRiD MT can be highly effective in the further development of new seismic analysis systems
that are able to monitor long-period earthquakes that none of
the conventional short-period waveﬁeld monitoring systems areable to detect. Our monitoring results also prove the feasibilityof continuously monitoring of the long-period seismic waveﬁeld.
Incorporation of more accurate structural models (e.g., 3-D), and/or
the usage of different period data, should result in more accurate
description of the earthquake activity ﬁeld in real-time.
Acknowledgments
We thank Prof. Doug Dreger and an anonymous reviewer who
provided helpful review comments that substantially improved
the manuscript. We used the F-net data provided by the National
Research Institute for Earth Science and Disaster Prevention. We
also used FKRPROG, developed by Saikia, for the computation of
the Green’s function.
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