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Geophysical Journal International
Geophys. J. Int. (2013) doi: 10.1093/gji/ggt371
GJI Seismology
Towards real-time regional earthquake simulation I: real-time
moment tensor monitoring (RMT) for regional events in Taiwan
Shiann-Jong Lee,1Wen-Tzong Liang,1Hui-Wen Cheng,2Feng-Shan Tu,1
Kuo-Fong Ma,2Hiroshi Tsuruoka,3Hitoshi Kawakatsu,3Bor-Shouh Huang1
and Chun-Chi Liu1
1Institute of Earth Sciences, Academia Sinica, Taipei 11529, Taiwan. E-mail: sjlee@earth.sinica.edu.tw
2Institute of Geophysics, National Central University, Jhongli, Taoyuan 32001,Taiwan
3Earthquake Research Institute, University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 1130032, Japan
Accepted 2013 September 13. Received 2013 September 12; in original form 2013 June 17
SUMMARY
We have developed a real-time moment tensor monitoring system (RMT) which takes ad-
vantage of a grid-based moment tensor inversion technique and real-time broad-band seismic
recordings to automatically monitor earthquake activities in the vicinity of Taiwan. The cen-
troid moment tensor (CMT) inversion technique and a grid search scheme are applied to obtain
the information of earthquake source parameters, including the event origin time, hypocentral
location, moment magnitude and focal mechanism. All of these source parameters can be
determined simultaneously within 117 s after the occurrence of an earthquake. The moni-
toring area involves the entire Taiwan Island and the offshore region, which covers the area
of 119.3◦E to 123.0◦E and 21.0◦N to 26.0◦N, with a depth from 6 to 136 km. A 3-D grid
system is implemented in the monitoring area with a uniform horizontal interval of 0.1◦and
a vertical interval of 10 km. The inversion procedure is based on a 1-D Green’s function
database calculated by the frequency–wavenumber (fk) method. We compare our results with
the Central Weather Bureau (CWB) catalogue data for earthquakes occurred between 2010
and 2012. The average differences between event origin time and hypocentral location are
less than 2 s and 10 km, respectively. The focal mechanisms determined by RMT are also
comparable with the Broadband Array in Taiwan for Seismology (BATS) CMT solutions.
These results indicate that the RMT system is realizable and efficient to monitor local seismic
activities. In addition, the time needed to obtain all the point source parameters is reduced
substantially compared to routine earthquake reports. By connecting RMT with a real-time
online earthquake simulation (ROS) system, all the source parameters will be forwarded to
the ROS to make the real-time earthquake simulation feasible. The RMT has operated offline
(2010–2011) and online (since January 2012 to present) at the Institute of Earth Sciences
(IES), Academia Sinica (http://rmt.earth.sinica.edu.tw). The long-term goal of this system
is to provide real-time source information for rapid seismic hazard assessment during large
earthquakes.
Key words: Time-series analysis; Inverse theory; Earthquake source observations; Seismic
monitoring and test-ban treaty verification; Computational seismology.
1 INTRODUCTION
The centroid moment tensor (CMT) inversion is one of the com-
mon ways to retrieve point source information for moderate-to-
large earthquakes. The CMT is also a very good representation of
the fault rupture mechanism during an earthquake. The definition
of CMT was first proposed by Backus & Mulcahy (1976). Suc-
cessful development of CMT inversion technique has enabled the
automated determination procedure to examine earthquakes world-
wide, for example, the Havard CMT (Dziewonski et al. 1981), the
USGS CMT (Sipkin 1982), the ERI AUTOCMT (Kawakatsu 1995)
and the Global CMT project (http://www.globalcmt.org/; Ekstr¨
om
et al. 2012). There are also several studies which use regional wave-
form data to analyse CMT of local events. For example, Dreger &
Helmberger (1993) used the broad-band waveforms to invert the
moment tensor and focal mechanism of regional earthquakes in
C
The Authors 2013. Published by Oxford University Press on behalf of The Royal Astronomical Society. 1
Geophysical Journal International Advance Access published October 16, 2013
at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from at Academia SinicaLife Science Library on October 16, 2013http://gji.oxfordjournals.org/Downloaded from
2S.-J. Lee et al.
the United States; Bernardi et al. (2004) applied the moment ten-
sor inversion for earthquakes which occurred in the European–
Mediterranean region; Fukuyama et al. (1998) used regional broad-
band seismic network data (F-net) to analyse source parameter
information in Japan.
In late 1992, the Institute of Earth Sciences (IES), Academia
Sinica, initiated a project to establish the Broadband Array in Tai-
wan for Seismology (BATS), which has paved the way for seis-
motectonic studies in the Taiwan region (Kao et al. 1998; Kao &
Jian 2001). Over the past decade, the moment tensor inversion for
local earthquakes has become a routine that is performed by the
BATS data centre (Kao et al. 2002; Liang et al. 2003; Liang et al.
2004). The CMT solutions and BATS waveform data set are avail-
able online at the BATS website (http//bats.earth.sinica.edu.tw). A
modified moment tensor inversion procedure from BATS is incorpo-
rated with the Central Weather Bureau (CWB) broad-band seismic
network from which the corresponding CMT solution is reported
for moderate-to-large earthquakes in the Taiwan region since 2005
(http://cwbsnbb.cwb.gov.tw).
Both of these routine CMT solutions require some lag time to
receive the CWB rapid earthquake report (event origin time, location
and local magnitude) before data preparation and performing the
moment tensor inversion analysis. Usually the CWB can detect
earthquakes occur in the vicinity of Taiwan within 45 s. All the
source information are confirmed and then released to the public
in 3–5 min. Considering the network communication time that is
required for distributing the earthquake information from CWB
to local server, the entire latency before further to implement the
moment tensor inversion analysis can be longer. This is also a
common problem in most of the automatic CMT inversion systems
in the world, for example, the near–real-time MT in Greece (AUTH-
solutions, Roumelioti et al. 2008) and the automatic regional MT
in the European–Mediterranean region (Bernardi et al. 2004).
To solve this problem, Kawakatsu (1998) suggested a possibility
of monitoring the long-period wavefield in real time using a grid-
based search algorithm. Based upon this algorithm, Tsuruoka et al.
(2009a) developed and implemented a new grid-based earthquake
analysis system (GRiD MT, http://wwweic.eri.u-tokyo.ac.jp/GRiD
MT/) that continuously monitors earthquake activity using broad-
band seismograms. This new analysis system automatically and
simultaneously determines the origin time, location and seismic
moment tensor of seismic events within 3 min of their occurrence
offshore of northeastern Tohoku. However, there are areas that can
be improved upon with the GRiD MT, including (1) how to deal
with missing data, (2) extend the monitoring area and (3) improve
performance by multiple-PC processing (Tsuruoka et al. 2009a).
Tajima et al. (2002) developed a similar automatic system (AMT),
with 0.25◦in grid space and real-time waveforms shifted forward
with ∼20-s time interval, to simultaneously determine the centroid
source location and seismic moment tensor for regional earthquakes
that occurred in northern California. Due to the restraint of system
performance, the resolutions in both event origin time and hypocen-
tral location are required to be improved to serve the purpose of
monitoring seismic events in real time. Lee et al. (2010) applied
the grid-based moment tensor inversion technique by using a 3-D
Green’s functions database to analyse the detailed source parame-
ters, including both the hypocentral location and focal mechanism,
of a local earthquake that occurred near the Taipei Basin on 2004
October 23. By the use of W-phase and/or high-rate GPS data, the
grid-based CMT inversion approach can also be applied to the large
events (Duputel et al. 2011; Melgar et al. 2011). Guilhem & Dreger
(2011) implemented the GRiD MT method for the Mendocino
Triple Junction region, and suggested modifying the approach for
characterizing large earthquakes (i.e. M>7).
In this study, we develop a real-time moment tensor monitor-
ing system, in short called RMT (http://rmt.earth.sinica.edu.tw/), to
monitor seismic activity in the vicinity of Taiwan. Unlike the stan-
dard moment tensor inversion procedure, the RMT system searches
the entire model space every 2 s, thus does not require aprioriinfor-
mation on an earthquake’s origin time and hypocentral coordinates
to invert the source moment tensor. The RMT provides all the point
source information automatically in real time, including event oc-
currence time, hypocentral location, moment magnitude, moment
tensor and focal mechanism simultaneously once the earthquake is
detected. The basic concept of this system is similar to the GRiD MT
system recently operated in Japan (Tsuruoka et al. 2009a). We make
several improvements to utilize the real-time BATS data flow and
to deal with the missing data problem. This system uses six BATS
stations in Taiwan. When one or two stations have missing data
problems, the remaining stations can still provide good azimuthal
coverage. Furthermore, a parallel computing technique is applied to
improve system performance, which is crucial to realize real-time
earthquake monitoring at the regional scale. The high seismicity rate
in Taiwan and high-quality broad-band continuous waveform data
provide us a good opportunity to develop these inversion techniques
and test the reliability of this real-time earthquake monitoring sys-
tem. In this paper, the developments and details of RMT system
are presented in Section 2. Offline (2010–2011) and online (since
2012 January to present) results are shown and analysed in Section
3. In Section 4, we provide comprehensive discussions on system
performance and future developments.
2 RMT DATA AND METHOD
2.1 System flowchart
The flowchart of the RMT system is shown in Fig. 1. First, the
BATS database server broadcasts real-time broad-band waveforms
through internet via ‘Seedlink’ (software developed by GEOFON
Program of GFZ Potsdam, Hanka et al. 2000). The RMT server
acquires the raw data by using ‘slinktool’ that is maintained by In-
corporated Research Institutions for Seismology (IRIS); the data
format is converted and processed on the RMT server through
‘getRTsac’ script (Fig. 1). Then, a parallel program named ‘Parallel
RMT’, which is the kernel of this monitoring system to perform the
moment tensor inversion and search for the best-fit solution through
all virtual source points (see Fig. 2). The inversion and grid search
Figure 1. The flowchart of real-time moment tensor monitoring system.
Towards real-time earthquake simulation 3
Figure 2. Distribution of virtual source gridpoints and the six BATS stations used in this study. Black crosses show the surface projection of 21 154 3-D
distributed gridpoints, which covers the region from 119.3◦E to 123.0◦E, 21.0◦N to 26.0◦N and the depth from 6 to 136km. Blue triangles are the locations of
the six BATS stations used.
result is evaluated by an event-detecting procedure (EDP). If no
event is detected, the system will go back to ‘getRTsac’ to retrieve
new data. Once the earthquake is detected by the system, the RMT
will announce alarms via the webpage and provide a summary of
event information automatically within 2 min after the occurrence
of an earthquake. All of these monitoring results will be shown on
the RMT webpage in real time.
2.2 Data processing
For continuous data flow processing, the BATS database server
broadcasts real-time broad-band data in seed format via ‘Seedlink’.
The RMT server fetches the raw data and performs a series of
data-processing procedures, including converting data format from
seed to SAC (Seismic Analysis Code), time synchronization, cut-
ting the time window, applying a bandpass filter and resampling
the waveform to one point per second. The time window used in
the RMT is 100 s; this time window is long enough to include
the first arrival and/or later phases when the seismic wave propa-
gates from offshore to BATS stations on the island. We bandpass
filtered the continuous broad-band seismogram for the frequency
between 0.02 and 0.1 Hz. For this long period, low-frequency band
signal, the details of localized path and structure effects can be
discounted.
2.3 Inversion method
In this study, the moment tensor inversion problem is formulated in
a linear form, Ax =b,whereAis the matrix of Green’s functions, b
is the observed data vector and xis the solution vector of six moment
tensor elements (Aki & Richards 1980). We use the singular value
decomposition technique to solve this linear system of equation for
the six moment tensor elements. The misfit between observed data
and synthetics is assessed based on the least-square waveform misfit
function (Mellman et al. 1975)
Ei=1−T
0[fi(t)gi(t)] dt
T
0fi(t)2dtT
0gi(t)2dt
,(1)
where fi(t)andgi(t) are observed and synthetic seismograms, re-
spectively. Eiis 0 if it is a perfect fit. The advantage of this formula
is that it is more sensitive to the correlation of waveforms than the
absolute amplitudes (Wallace et al. 1981). Thus, knowing apriori
structural details becomes less critical.
To search for the best-fit solution in all virtual gridpoints, instead
of the quantity estimated Ei, a misfit reduction (MR)
MR =(1 −Ei)×100% (2)
is used in the actual monitoring system. Because it depends on Ei,
MR is an indicator of the fit between the observed waveform and
4S.-J. Lee et al.
the synthetic waveform. The MR value of a perfect fit in waveform
will be 100.
In addition, we considered a variance reduction (VR)
VR =1−T
0[fi(t)−gi(t)]2dt
T
0fi(t)2dt×100% (3)
as the second indicator to evaluate the waveform fitting. From the
online monitoring and offline tests, we find that the VR is too
sensitive to the absolute amplitudes, especially when a large phase
dominates the seismogram. This will cause erroneous judgment
when searching the best-fit solution. Thus, the VR is only used as
a reference and has not been considered to evaluate the inversion
result in the RMT system.
2.4 Virtual source grid and station settings
The RMT monitors earthquake activity in Taiwan, which covers
the region from 119.3◦E to 123◦E, from 21.0◦N to 26.0◦Nand
in depth from 6 to 136 km. A 3-D distributed grid is set in this
monitoring area with grid size of 0.1◦×0.1◦in horizontal and
10 km in depth (see Fig. 2). The total number of virtual sources in
this 3-D grid system is 21 154. These gridpoints are subdivided into
several parts in the ‘Parallel RMT’ program as discussed in the next
section. The distributions of BATS stations used in the RMT are
shown in Fig. 2. We use six BATS stations in the RMT system, five
stations located in Taiwan Island and one sited on the Penghu Island.
From north to south are YHNB, SSLB, PHUB, YULB MASB and
TWKB. The broad-band stations used in this study are equipped
with STS-2 (PHUB, YHNB), STS-1 (MASB) and Trillium-240
(SSLB, TWKB, YULB) seismometers and Q330HR dataloggers.
The lower corner frequencies are 8.33 mHz (120 s), 4.17 mHz
(240 s) and 2.78 mHz (360 s) for STS-2, Trillium-240 and STS-1,
respectively. All of these stations provide good data quality and
high signal-to-noise (S/N) ratio of real-time continuous broad-band
recordings. However, because PHUB and TWKB are sited close
to the coastal area, the background noise, which is mainly caused
by large sea waves and tides, could contaminate the seismic signal.
Thus, a smaller waveform weighting (0.5) is given for the horizontal
components (E and N) of these two stations in the moment tensor
inversion procedure. The seismograms recorded by other stations
are given a full weighting (1.0) in all three components.
Each gridpoint to station pair has six 1-D Green’s functions
corresponding to six moment tensor elements. The 1-D Green’s
function is calculated using the frequency–wavenumber (fk) method
of Zhu & Rivera (2001) based on a 1-D Taiwan average velocity
model proposed by CWB (Table 1; Chen & Shin 1998). A Green’s
Tab l e 1 . 1-D velocity model used for Green’s functions.
Thickness (km) Vp(km s−1)Vs(km s−1) Density (kg m−3)QpQs
2.0 3.50 1.97 2200 600 300
2.0 4.44 2.57 2400 600 300
5.0 5.25 3.03 2600 600 300
4.0 6.05 3.46 2600 600 300
4.0 6.36 3.66 2700 600 300
8.0 6.66 3.85 2700 600 300
5.0 7.14 4.10 2700 600 300
5.0 7.43 4.27 2800 600 300
15.0 7.71 4.41 2800 600 300
20.0 7.96 4.63 3000 600 300
20.0 8.10 4.63 3100 600 300
200.0 8.23 4.73 3300 600 300
functions database is stored in the hard disk. This database is read
only once from hard disk when the ‘Parallel RMT’ program starts to
work. Then all the Green’s functions are stored and taken from the
memory when performing the moment tensor inversion. By doing
so, the time used to read the Green’s functions on each virtual source
during grid search will be greatly reduced.
2.5 Parallel RMT
An important aspect of the RMT system is the combination of the
moment tensor inversion with grid search through a 3-D virtual
source grid to evaluate the best-fit solution in the monitoring area.
With this approach, not only the CMT solution but also the event
occurrence time, location and magnitude can be obtained simulta-
neously. It is expected that using more virtual gridpoints can help
to increase the resolution of hypocentral locations and enlarge the
monitoring area. However, an increase in the number of virtual
source points would lead to a large expansion of grid search time,
making the evaluation of a best-fit solution difficult to perform in
real time. To resolve this problem, we develop a parallel real-time
moment tensor inversion program (‘Parallel RMT’), which essen-
tially divides the 3-D gridpoints into present computing nodes; each
node deals with only the divided part of 3-D grid, and thus increases
the number of total virtual source points and promotes program
performance. Message Passing Interface (MPI, Gropp et al. 1996)
is applied as the communicant between the computing nodes in
the parallel computing process. Using the ‘Parallel RMT’ program
within a 32 CPU cores cluster, the RMT system can complete one
grid search throughout the monitoring area of every 2 s in real
time.
2.6 Event detecting
The example of an earthquake successfully detected by the RMT
system is shown in Fig. 3. A continuous real-time monitoring result
for Taiwan can be seen in the link: http://rmt.earth.sinica.edu.tw/
rmt_demo.htm. An earthquake is detected when: (1) the MR is
larger than the threshold (MR =60), and (2) a peak MR appears (at
tseconds) that is no larger than the MR found previously and after
20 s (t±20 s).
3 RMT MONITORING RESULTS
The RMT system has been utilized online for more than 1 yr since
2012 January to present. Offline RMT analysis was also carried out
for events listed in the CWB earthquake catalogue from 2010 to
2011. All the earthquake parameters for magnitude ≥4 determined
by RMT are shown in Table 2. The total number of detected earth-
quakes is 302; their epicentres and focal mechanisms are shown in
Fig. 4. About 60 per cent of these events are located offshore east of
Taiwan. The smallest magnitude is Mw2.8 and only two events are
larger than ML6 (as retrieved from the CWB earthquake catalogue)
occurred during this time period. These two events are the 2010
Jiashian earthquake (ML6.4; No. 7 in Table 2) and 2012 Wutai
earthquake (ML6.4; No. 93 in Table 2). Both of these two events
are located in southern Taiwan.
In order to evaluate the system performance of RMT, we compare
the source parameters (event time, location and magnitude) taken
from CWB earthquake catalogue with those determined by RMT.
Fig. 5 shows the difference of RMT and CWB source parameters
in location. In the horizontal component (see Figs 5a, b and d), the
Towards real-time earthquake simulation 5
Figure 3. Example of an earthquake successfully detected by the RMT system (the continuous real-time monitoring result can be seen in the link:
http://rmt.earth.sinica.edu.tw/rmt_demo.htm). The left panel shows the comparison between the real-time BATS data (black lines) and the synthetic waveforms
(red lines). The maximum ground velocity is shown at the end of each waveform. The continuous misfit reduction (MR) of the best-fit solution is presented
in the upper right panel. The variance reduction (VR) is also presented in the background and shown as a thin white line. The lower right panel shows the
surface projection of the largest MR values in all depths in the monitoring area (MR map). The epicentre of the detected event is shown by a red solid star and
highlighted with a blue open square in the MR map.
differences are mostly between ±0.1◦with the mean value close
to 0.0◦in both longitude and latitude. For the vertical component
(or hypocentral depth), the major differences are between 0 and
10 km, with similar percentage in 0 and +10 km (Fig. 5c). Since the
resolution of the RMT in horizontal and vertical are 0.1◦and 10 km,
respectively (the interval of virtual source grid), the results of the
hypocentral location determined by RMT are comparable to the
CWB earthquake catalogue. The difference in the event occurrence
time between CWB and RMT reports is usually between ±2s(see
Fig. 6). Again, this falls into the temporal resolution of RMT system,
which updates the monitoring result every 2 s.
Fig. 7 shows the comparison between the Mwdetermined by
RMT, MLobtained from CWB earthquake catalogue and Mwtaken
from BATS CMT reports. Clear linear relationships can be ob-
served between Mw(RMT) versus ML(CWB) and Mw(RMT) versus
Mw(BATS; see Fig. 7a). Usually, the CWB MLhas larger magnitude
compared to the Mwtaken from RMT. The maximum difference
in magnitude between the two catalogues is −1.0; the averaged
differences and standard deviations are −0.45 ±0.21 (Fig. 7b).
This description becomes slightly larger when the magnitude in-
creased. Conversely, the BATS Mwusually has a smaller magnitude
compared to Mwtaken from RMT. However, the difference is not
so obvious. It is less than 0.3 magnitude unit on average. Again, the
description becomes slightly larger when the magnitude increased.
In all, the moment magnitude Mwdetermined by RMT is usually
between the local magnitude MLtaken from CWB earthquake cat-
alogue and the moment magnitude Mwfrom BATS CMT reports.
The RMT system analyses the six moment tensor elements in a
full inversion; the isotropic and compensated linear vector dipoles
(CLVDs) components are then removed to determine the focal
mechanism (strike, dip and rake) of the event. In order to evalu-
ate the quality of focal mechanism determined by RMT, we use the
Kagan angle (Kagan 1991) to see the difference of focal mecha-
nism (only double-couple part) between BATS CMT and RMT. The
Kagan angle is a measure of the difference between the orientations
of two seismic moment tensors. Kubo et al. (2002) indicates that two
focal mechanisms are similar when the Kagan angle is less than 30◦.
On contrary, a Kagan angle of 120◦means that the focal mechanisms
are significantly different. The result of Kagan angle from the com-
parison between BATS CMT and RMT is shown in Fig. 8. Most of
the rotated angles are smaller than 50◦(with about 63 per cent <30◦
and 72 per cent <40◦), which indicates that the focal mechanism
determined from BATS CMT and RMT are very close.
Note that the azimuthal coverage of events located offshore on
the north or south of the Taiwan Island is poor because most of the
BATS stations are located on the island. Fortunately, there are few
earthquakes occurred in these areas. During our analysis time period
(from 2010 to 2012), no earthquake had been detected offshore on
northern Taiwan and only one earthquake occurred in southern
offshore. The same azimuthal coverage problem also happened on
BATS CMT since BATS use the same broad-band station array,
which is mostly located on the island. Thus, the difference of Kagan
angles between BATS CMT and RMT is not referred to the station
azimuthal coverage.
6S.-J. Lee et al.
Tab l e 2 . Event list for the earthquake determined by the RMT with magnitude Mw≥4.0.
No. Date (yyyy/mm/dd) Time (hh:min:ss) Time shift (s) Long. (◦E) Lat. (◦N) Depth (km) Strike (◦)Dip(
◦)Rake(
◦)MwMR (per cent)
1 2010/01/01 17:34:46 0 121.6 23.8 36 5 63 80 4.4 82.3
2 2010/01/04 19:27:37 +1 121.8 24.2 46 214 79 162 4.7 86.8
3 2010/01/13 10:14:48 −3 121.6 24.0 26 53 28 91 4.0 75.2
4 2010/01/19 06:09:27 +1 121.6 23.8 36 346 76 45 4.7 78.3
5 2010/02/22 05:21:05 −7 123.0 24.0 26 241 59 59 5.1 72.0
6 2010/02/26 01:07:58 −2 122.9 23.6 46 43 74 154 5.3 54.4
7 2010/03/04 00:18:54 +4 120.7 23.1 26 162 77 74 6.1 77.0
8 2010/03/04 07:07:14 +2 122.1 23.1 16 35 88 147 4.2 68.0
9 2010/03/04 08:08:00 0 120.7 23.1 16 177 89 84 3.8 63.0
10 2010/03/04 08:16:16 +2 120.7 23.1 16 170 87 79 4.8 67.1
11 2010/03/08 09:26:23 −7 120.4 23.3 16 339 89 −60 4.3 71.9
12 2010/03/09 05:52:20 −2 121.0 24.0 16 348 70 6 4.0 70.4
13 2010/03/18 09:01:36 −1 122.0 24.4 36 145 34 60 4.2 67.5
14 2010/03/18 16:54:00 −1 122.0 24.4 36 138 32 50 4.1 68.9
15 2010/03/26 23:07:54 0 121.7 24.1 56 235 82 −32 4.4 85.2
16 2010/04/09 11:49:54 0 122.0 24.8 106 126 59 127 4.3 72.8
17 2010/04/11 04:57:31 −3 122.1 23.2 26 2 68 55 4.9 69.0
18 2010/04/13 14:29:29 −3 121.3 23.1 16 186 72 −31 4.5 75.6
19 2010/04/13 20:49:08 −2 121.3 23.1 16 188 78 −29 4.7 75.2
20 2010/04/23 01:49:48 −1 122.4 24.6 86 70 33 135 4.3 67.2
21 2010/05/02 18:00:14 +1 120.6 23.0 16 351 89 −85 4.3 70.0
22 2010/06/14 17:17:46 0 121.6 24.1 26 30 76 91 4.4 77.9
23 2010/06/15 00:31:18 +1 121.6 24.1 26 33 73 95 5.3 77.2
24 2010/06/18 01:08:44 0 121.7 24.2 16 163 15 −13 4.4 72.0
25 2010/06/25 04:28:05 −1 122.1 24.5 56 219 61 146 3.9 71.5
26 2010/06/26 18:05:52 −3 121.6 24.8 76 9 61 163 4.6 84.8
27 2010/07/02 19:11:32 +4 120.8 23.0 16 238 10 −19 4.3 62.1
28 2010/07/08 19:43:38 0 122.0 24.4 26 152 70 −74 4.9 70.8
29 2010/07/09 00:41:20 +2 122.6 24.6 96 215 39 17 4.9 63.7
30 2010/07/17 09:04:17 +1 121.6 23.5 46 30 66 73 4.5 72.5
31 2010/07/18 13:03:25 −3 122.6 24.0 36 295 16 136 5.0 63.6
32 2010/07/25 03:52:10 −1 120.7 22.9 26 198 7 0 5.0 76.9
33 2010/07/31 14:49:06 +1 120.6 23.0 16 171 74 81 4.2 71.9
34 2010/08/30 08:45:09 −2 120.9 25.5 6 21 81 −60 4.7 73.9
35 2010/09/11 22:41:07 +2 121.7 24.4 46 30 88 117 4.1 82.0
36 2010/09/15 08:47:23 −1 121.4 23.2 16 7 60 47 4.1 80.7
37 2010/09/19 05:24:04 0 122.0 24.4 36 113 39 −5 4.1 57.8
38 2010/09/20 03:17:08 0 121.6 23.7 36 3 66 60 4.5 66.3
39 2010/09/28 13:10:13 0 121.6 24.1 26 45 68 58 4.2 78.3
40 2010/09/28 17:33:53 −8 121.8 24.1 16 233 30 −64 4.5 73.5
41 2010/09/30 19:56:00 0 121.9 24.9 86 216 47 44 4.1 67.1
42 2010/10/02 19:23:12 0 121.7 24.4 26 78 64 −25 4.5 68.7
43 2010/11/08 13:01:21 0 120.4 23.3 16 303 42 53 4.5 73.8
44 2010/11/12 13:08:50 −4 122.7 24.1 36 59 72 80 5.1 69.3
45 2010/11/12 15:39:01 0 120.5 22.2 36 209 58 −21 4.8 73.0
46 2010/11/16 03:00:35 −1 121.7 24.1 26 26 63 66 4.1 70.8
47 2010/11/21 12:31:46 +1 121.6 23.9 46 13 81 67 5.5 77.3
48 2010/11/26 15:13:45 +1 121.7 24.2 26 30 80 68 4.5 79.0
49 2010/12/06 02:14:12 −1 121.4 23.7 16 134 20 76 4.3 70.9
50 2010/12/07 02:46:15 0 121.4 23.0 36 136 78 −30 4.4 79.8
51 2011/01/31 20:53:16 +1 121.7 24.2 26 195 88 −68 4.5 78.2
52 2011/02/01 08:16:32 −6 121.8 24.2 16 260 21 −16 5.0 77.7
53 2011/02/05 12:46:32 −3 122.2 24.6 66 66 16 120 4.0 69.0
54 2011/02/07 05:55:02 −2 121.7 24.1 26 77 51 124 4.3 75.0
55 2011/03/16 13:12:17 −1 120.7 22.6 36 222 33 −62 4.2 82.6
56 2011/03/18 11:12:24 +1 121.7 24.2 16 220 77 −80 4.0 72.7
57 2011/03/20 08:00:51 −5 121.7 22.4 16 211 68 119 5.0 64.4
58 2011/03/26 07:19:12 −2 121.9 25.0 96 112 81 71 4.1 54.1
59 2011/03/30 04:22:32 −2 121.5 23.9 16 72 35 112 4.2 73.5
60 2011/05/03 15:52:34 −2 121.8 23.9 46 86 21 162 4.7 76.9
61 2011/05/06 21:31:51 −3 121.5 23.2 26 346 49 42 4.4 81.1
62 2011/05/22 01:34:12 −1 121.8 24.2 16 206 58 −34 4.9 61.2
63 2011/05/22 21:07:16 −2 121.1 23.9 26 30 36 97 4.3 87.5
64 2011/05/24 18:32:05 0 121.5 23.4 36 169 86 −37 4.0 73.9
65 2011/06/10 01:25:22 −1 121.6 23.6 36 351 61 55 4.6 77.2
Towards real-time earthquake simulation 7
Tab l e 2 (Continued.)
No. Date (yyyy/mm/dd) Time (hh:min:ss) Time shift (s) Long. (◦E) Lat. (◦N) Depth (km) Strike (◦)Dip(
◦)Rake(
◦)MwMR (per cent)
66 2011/06/19 10:18:55 −1 121.9 24.7 76 55 51 128 4.4 86.5
67 2011/06/26 00:31:22 +1 122.0 24.3 36 21 72 120 4.2 73.6
68 2011/07/03 12:30:44 −5 122.1 24.8 76 70 35 152 4.3 83.2
69 2011/07/06 05:22:58 +2 121.7 24.3 26 44 72 88 4.3 82.7
70 2011/07/06 11:59:00 0 121.8 24.3 26 50 61 114 4.4 82.0
71 2011/07/07 15:52:20 +1 121.7 24.3 26 43 73 99 4.2 78.9
72 2011/07/12 11:17:11 +1 121.5 23.5 26 18 49 100 4.7 82.7
73 2011/07/17 18:45:24 −1 121.6 23.8 26 358 68 65 4.2 76.0
74 2011/07/19 14:01:10 −2 121.6 24.0 26 74 42 128 4.3 76.7
75 2011/08/10 19:25:55 0 121.7 23.4 26 236 50 134 4.1 82.3
76 2011/08/11 22:07:33 −1 121.6 23.8 36 355 73 58 4.3 80.4
77 2011/08/25 09:35:14 −1 121.9 23.1 16 5 25 46 4.1 72.2
78 2011/08/31 11:22:32 +9 120.8 21.9 16 150 41 62 4.3 74.2
79 2011/09/09 03:26:59 +1 120.9 22.3 16 175 72 18 4.4 69.8
80 2011/09/09 05:52:12 +1 120.9 22.3 16 79 69 164 4.0 66.9
81 2011/09/21 22:18:33 0 121.7 24.1 26 38 59 72 4.6 81.5
82 2011/09/22 01:00:59 0 121.6 23.8 36 3 67 70 4.3 81.0
83 2011/10/09 15:54:26 −2 121.0 22.5 26 103 51 132 4.3 73.8
84 2011/10/31 15:17:04 0 122.0 24.9 76 119 76 110 4.1 63.1
85 2011/12/02 00:55:24 0 121.3 23.0 16 28 46 65 4.4 76.4
86 2011/12/04 10:13:04 −1 122.1 24.7 66 76 46 149 4.3 78.7
87 2011/12/15 00:35:42 −1 121.8 23.7 36 231 64 117 4.3 72.5
88 2012/01/04 06:59:57 −9 121.7 23.9 16 275 31 −17 4.1 62.9
89 2012/01/21 00:50:02 −4 122.5 24.2 26 53 71 99 4.4 66.9
90 2012/02/04 02:54:23 +1 122.7 24.7 96 213 75 −12 4.7 61.5
91 2012/02/05 16:25:11 +1 121.0 22.5 26 138 41 −29 4.5 67.8
92 2012/02/06 03:48:28 0 121.0 22.4 26 36 87 93 4.2 72.4
93 2012/02/26 02:35:00 +1 120.7 22.7 36 179 49 87 5.8 78.9
94 2012/03/04 17:52:15 0 122.7 24.1 46 115 50 3 4.4 69.8
95 2012/03/21 23:44:38 −1 121.6 23.2 46 235 61 128 4.4 74.5
96 2012/04/07 19:59:18 −1 121.7 24.1 26 357 66 47 4.2 78.6
97 2012/04/12 10:39:25 0 120.4 23.4 16 15 32 70 4.0 64.0
98 2012/04/19 01:58:09 −1 121.7 24.1 26 11 77 56 4.9 81.5
99 2012/04/27 14:48:36 −1 121.8 23.8 36 42 36 85 4.2 72.5
100 2012/04/27 21:08:18 0 120.7 22.7 26 167 67 87 3.9 88.0
101 2012/05/30 07:25:42 −2 121.0 23.2 16 259 89 179 4.2 75.5
102 2012/06/05 09:56:30 −3 122.4 24.3 36 277 18 133 4.7 65.0
103 2012/06/06 01:08:35 +9 121.5 22.6 26 180 36 −69 5.6 66.9
104 2012/06/09 21:00:18 +5 122.1 24.4 66 191 57 52 5.8 67.4
105 2012/06/09 21:54:21 +1 122.2 24.5 66 176 68 47 4.3 65.4
106 2012/06/10 06:23:29 −1 121.7 23.9 36 355 71 69 4.2 80.3
107 2012/06/13 08:22:20 +1 121.3 24.7 6 14 52 −20 4.2 89.5
108 2012/06/14 16:15:13 +1 121.5 23.7 6 1 65 63 4.8 68.2
109 2012/06/14 19:30:56 0 121.5 23.7 16 33 89 51 4.5 78.5
110 2012/07/04 20:05:47 −4 121.0 21.5 26 45 72 110 4.5 66.5
111 2012/08/14 10:55:43 0 121.5 24.1 26 87 61 143 4.4 77.1
112 2012/08/16 17:41:00 0 121.5 24.1 26 89 54 152 4.0 74.0
113 2012/08/31 12:11:40 0 120.9 24.7 6 94 56 168 4.0 80.7
114 2012/09/04 20:00:18 +2 121.0 22.2 26 63 83 121 5.1 68.6
115 2012/09/05 03:45:30 −4 122.6 24.0 36 65 74 83 4.5 65.0
116 2012/09/24 21:57:33 +2 121.2 22.9 16 59 32 62 4.1 78.0
117 2012/10/13 08:27:51 −1 121.5 24.1 26 6 36 81 3.8 66.9
118 2012/10/20 08:49:39 0 122.5 24.4 76 192 79 84 4.3 63.5
119 2012/10/25 10:31:18 0 120.4 22.5 36 15 49 −75 5.0 72.6
120 2012/11/05 13:40:30 −2 121.5 23.8 16 95 59 148 4.2 79.8
121 2012/11/20 17:09:02 −6 121.7 22.4 16 6 27 68 4.8 64.8
122 2012/11/21 22:25:26 −3 122.5 24.0 26 279 17 118 4.6 64.9
123 2012/11/29 03:12:53 +2 121.3 22.7 76 140 76 106 4.3 78.7
124 2012/12/02 17:45:37 0 121.5 24.0 16 71 66 139 4.6 69.4
125 2012/12/02 19:43:31 −2 121.6 23.9 16 37 29 81 4.3 72.6
126 2012/12/11 10:32:04 +1 121.6 24.1 26 19 84 39 4.2 73.4
127 2012/12/24 03:17:52 +1 121.3 22.5 66 217 19 −6 4.4 62.1
128 2012/12/26 02:06:38 −3 122.6 23.9 36 65 80 79 4.6 62.2
129 2012/12/30 16:03:26 −1 120.9 23.5 6 338 79 −9 4.6 75.5
8S.-J. Lee et al.
Figure 4. The RMT analysis results. A total number of 302 earthquakes determined from RMT online monitoring (2012) and offline analysis (2010–2011) is
shown. Beach balls are their focal mechanisms; different hypocentral depths are presented with varied colours.
4 DISCUSSION
4.1 Time and location
It is worth noting that the earthquake location and focal mecha-
nism determined by RMT can be different from CWB earthquake
catalogue and BATS CMT reports. Because the RMT determined
the centroid based on the best-fit location of full waveform moment
tensor inversion results while the CWB report is determined based
on the traveltime of first arrivals, which is sensitive to revealing
the initial rupture point. The initial rupture and the centroid might
have a shift in both time and space, especially for large earthquakes.
Furthermore, the system biases of two methods could also cause dif-
ferences of event time and location. These differences will further
influence the inversion result of seismic moment tensor. We find in
some specific cases that the event time, location and focal mecha-
nism of northeast offshore earthquakes can sometimes have an ob-
vious difference with respect to that reported in the CWB catalogue
and BATS CMT solutions (see Fig. 9). To evaluate the robustness
of RMT, we perform forward 3-D wave propagation simulations
for these specific events and compare the synthetic waveforms de-
termined from the source parameters provided by RMT and BATS
CMT solutions. The simulations are based upon spectral-element
method with a fully 3-D tomography model (Lee et al. 2013). Re-
sults show that the synthetics determined from RMT fit the observed
waveforms better in both timing and amplitude compared to the
BATS CMT solutions. This could be due to the differences in event
time and location taken from CWB earthquake report that further
influence the CMT inversion result of BATS.
Reliable determination of source parameters for offshore earth-
quakes east of Taiwan is an important seismological task because
more than 60 per cent of the earthquakes in Taiwan occur in this
area. Since the moment tensor inversion is based on the concept
of earthquake centroid, the RMT utilizes the best-fit gridpoint (the
centroid location) to determine the moment tensor and focal mech-
anism that could be more reliable, especially for large earthquakes
because their nucleation and centroid locations are usually differ-
ent. Once there are more inland and offshore events determined by
RMT, a comprehensive analysis of seismic moment tensors and their
implications to Taiwan tectonic setting will be addressed in detail.
Towards real-time earthquake simulation 9
Figure 5. Comparison of source parameters between RMT analysis results and CWB earthquake catalogue: (a) histogram of the difference in longitude, (b)
histogram of the difference in latitude, (c) histogram of the difference in hypocentral depth and (d) difference pairs in horizontal (longitude and latitude).
4.2 Station redundancy
Because the moment tensor inversion analysis utilizes full wave-
forms from all three components, it can be done with only a few
stations (Dreger & Helmberger 1993). For example, the GRiD MT
system in Japan uses three inland stations (per grid) to monitor
earthquakes offshore, east of the Tohoku area. However, when a
missing data problem occurs, the GRiD MT is unable to accurately
detect an earthquake with just one station (Tsuruoka et al. 2009a).
Unlike the GRiD MT, the RMT system uses six BATS stations in
Taiwan. When one or two stations have missing data problems, the
remaining stations can still provide good azimuth coverage. The
traveltime and radiation information can be retrieved from other
stations with full waveforms in all three components. This implies
that the use of six BATS stations itself can be regarded as a re-
dundant seismic network in the RMT system. For example, the
SSLB station was down during the 2012 December 11 earthquake
(Mw4.2). It still worked well to find the correct source parameters
using the remaining five stations (see Fig. 10). In this case, the
system was inverting flat seismograms for the missing data.
4.3 Teleseismic events
The RMT system monitors earthquake activity by using long-period
waveform between 10 and 50 s. This period could be overlapping
with teleseismic events which are dominated by long-period wave-
forms, and thus result in an erroneous judgment as a local event in
10 S.-J. Lee et al.
Figure 6. Histogram of the difference in event time between RMT analysis
results and CWB earthquake catalogue.
the RMT. However, after online monitoring for more than 1 yr, we
find that the RMT system can deal with this problem well. This is
because the first teleseismic arrival reaches the local seismic net-
work at almost the same time that none of the Green’s functions
in all virtual source points can explain the observed arrival time
pattern. Furthermore, even though the later phases of a teleseismic
event usually have large amplitudes, their phases and arrival times
at each station are incoherent (similar to the low-frequency random
noise) that cannot be explained by Green’s functions. Thus, the tele-
seismic waveform cannot increase the value of MR (always <60)
to result in false detection. An example of the response of the RMT
system during 2012 April 11 off the west coast of northern Sumatra
event (Mw8.6) is shown in Fig. 11.
For an event that occurs in a regional distance like in Japan, the
maximum MR is usually less than 60. Only a few cases show MR
between 60 and 65. The increase of MR (>60) of the regional earth-
quakes is due to the first arrival (Pwave). Unlike the teleseismic
event that the first arrival reaches BATS stations at almost the same
time, the regional event may have an arrival recorded by one station
earlier. In this case, the inversion will try to fit this specific phase
with local Green’s functions and sometimes the MR can increase
significantly due to a good fitting on that specific waveform. This
kind of false detection can be easily eliminated by increasing the
MR threshold (MR =65) or adding another constraint in the sys-
tem, that is, considering the individual waveform misfit on each
seismogram. In addition, the W-phase can provide very good infor-
mation for the point source moment tensor inversion (Kanamori &
Rivera 2008; Hayes et al. 2009). The W-phase has incorporated into
GRiD MT system in Japan to obtain the MT in real time (Tsuruoka
et al. 2009b). Similar use of the W-phase to monitor regional events
close to Taiwan (i.e. in the Ryukyu and Manila trenches) will be
considered in the RMT system in future.
4.4 Monitoring performance
Using high-quality broad-band data from BATS, small earthquakes
(M<4) can also be detected by RMT. Fig. 12 shows the relationship
between moment magnitude (Mw) and MR of the events determined
by RMT. It shows that smaller earthquakes usually have a lower MR,
Figure 7. Comparison between the moment magnitude of RMT, local mag-
nitude of CWB and moment magnitude of BATS CMT catalogues: (a) Cor-
relation of the magnitudes determined from different catalogues. Red solid
circles show the magnitude correlation of Mw(RMT) and ML(CWB). Blue
solid circles are the magnitude correlation of Mw(RMT) and Mw(BATS).
(b) Histogram of the differences in magnitude determined from RMT, CWB
and BATS catalogues.
especially when Mwis lower than 3.7. When considering a threshold
of MR =60 to evaluate the occurrence of an earthquake in the
region, we find that 82.7 percent of the total events can be detected,
including 19.5 per cent of the events that have Mwlower than 3.7.
If we target only Mw≥3.7 ear thquakes, 93.2 per cent events can
be caught by the RMT system. The missed 6.8 per cent of the 3.7+
earthquakes are mainly caused by: (1) deep events which induce
weak shaking at ground surface, (2) low S/N ratio due to noisy
data, which are usually resulted from bad weather condition, such
as torrential rain or typhoon, especially for the stations close to the
coastal area and (3) the contamination of large teleseismic event.
The amplitude of waveform of the local small-to-moderate event
is relatively weak, which is embedded in the large surface waves
originated from big teleseismic earthquake. It is expected that a
smaller MR threshold can help to detect more small earthquakes.
Towards real-time earthquake simulation 11
Figure 8. Histogram of the Kagan angle determined from the comparison
between BATS CMT and RMT.
However, the number of false detections will be increased due to
low S/N ratio of small earthquakes. Since the purpose of RMT
focuses on providing the source parameters for moderate-to-large
earthquakes (M≥4), the use of MR =60 as the threshold is
appropriate for monitoring target events.
Nevertheless, there are several false alarms that occurred during
the online monitoring time period (from 2012 to present). Most
of the false alarms are due to regional events which can be easily
eliminated by increasing the MR threshold (MR =65) or adding
another constraint in the system as discussed in the previous sec-
tion. A few false alarms are due to station problem, such as the
regular checking or testing of seismometer. However, some uniden-
tified false alarms are also found which might be caused from the
anomalous ground shakings resulted from large landslides (on the
land as well as seabed), low-frequency events or others.
Concerning the performance of timing, the recent RMT system
provides all point source parameters within 117 s once the earth-
quake occurs. The waveform time window used is 100 s long, and
the system takes 15 s for time synchronization. The times needed in
these two parts are fixed and cannot be reduced. It takes another 2 s
for data processing, inversions, grid search and plotting the results
to show on webpage. Thus, the RMT updates the monitoring infor-
mation every 2 s and the total system latency is 117 s (or less than
2 min). Our analysis results indicate that 2 s updated rate is good
enough to obtain the precise source parameters in the RMT system.
Of course, if this time interval can be reduced, such that an update
is provided every second or less, the monitoring performance will
be improved further. By the use of 32 MPI threads in the ‘Parallel
RMT’ program, the inversion and grid search through all 21 154
virtual sources take less than 0.5 s in one iteration. However, in order
to provide the real-time monitoring result on the webpage [plotting
the monitoring summary via GMT (Generic Mapping Tools) and
then converting into PNG (Portable Network Graphics) format], it
needs more than 1 s to accomplish. This part works under one MPI
thread depending only on the performance of a single CPU core that
cannot be parallelized. According to Moore’s law (Moore 1965), the
number of transistors on integrated circuits doubles approximately
every 2 yr. We can expect that when the single thread computing
performance is improved in the next generation CPU, this problem
will be resolved straightforwardly.
4.5 Future studies
The recent RMT system utilizes 1-D Green’s functions based on a
1-D Taiwan average velocity model (Chen & Shin 1998). In most of
the cases, the 1-D Green’s functions can work sufficiently well for
the low-frequency period between 10 and 50 s in Taiwan. However,
we find in some specific cases that the performance of 1-D Green’s
functions must be evaluated, such as, when an earthquake occurs
northeast offshore, which is far away from the island (∼123◦E).
The location of the earthquake can sometimes have an obvious
difference with respect to that reported in the CWB earthquake
catalogue. This could be due to the complex wave propagation path
effect caused by the Ryukyu subduction zone. In this case, the
1-D velocity model might not be able to explain the complex path
effect even in a low-frequency signal. To deal with this problem, we
are preparing to use 3-D Green’s functions calculated by spectral-
element method based on a new 3-D tomography model (H.-H.
Huang et al., 2013), which has improved the resolution in area
offshore northeast Taiwan. Furthermore, a finer grid (<0.05◦in
horizontal and <5.0 km in depth) and a hybrid seismic network,
including BATS, CWB marine cable observatory and F-net from
Japan (YNG station on Okinawa-ken Yaeyama-gun) can help to
improve station coverage. With precise 3-D Green’s functions, finer
grid, more stations for better azimuth coverage, we can expect that
the monitoring performance of RMT can be further improved.
For large earthquake (M>7.0), the rupture duration might be
longer and the frequency range that is used in RMT will fall in the
slope of the displacement spectra of a large event leading to moment
magnitude saturation (Guilhem et al. 2013). Also the use of broad-
band stations might not be feasible during a large earthquake as they
are subject to clipping in the strong motions. We design a proce-
dure to deal with this problem in the RMT system. When the strong
ground shaking makes the broad-band recording saturated, the sys-
tem will automatically remove that record by given a zero weighting
on the waveform in the inversion. Thus, the clipped waveforms will
not influence the estimate of CMT. We are also planning to develop
a multichannel RMT for large earthquakes. In that case, a lower
frequency band (0.01–0.033 Hz) and a longer waveform time win-
dow (>200 s) will be considered based on low-gain strong-motion
recordings. In this case, however, the source information will be
obtained after 2 min. A multiple-source moment tensor inversion
procedure for monitoring the finite-fault rupture characteristics of
big earthquakes (M>8.0) is also planned. These two approaches,
which are based on the recent RMT system for large events, will be
developed and practiced online in the near future.
4.6 Implication in seismic hazard assessment
A routine CWB earthquake report can provide the information of
earthquake origin time, location and local magnitude about 2–5
min after the occurrence of an earthquake. If the earthquake has
a larger magnitude, the moment tensor inversion will also be per-
formed. Because this analysis is not done automatically, it needs
more time to obtain the CMT solution, usually more than 1 hr.For the
RMT monitoring system, all the point source parameters, including
event time, location, moment magnitude, moment tensor and focal
mechanism can be obtained simultaneously within 2 min. Com-
pared to the routine CWB earthquake report, the RMT has a great
12 S.-J. Lee et al.
Figure 9. (a) Comparison of specific offshore events between RMT and BATS CMT solutions. Left-hand side shows the map view of their locations and focal
mechanisms; right panel lists the detailed source parameters. (b) Comparison between vertical components observed velocity data and synthetic waveforms
determined by RMT and BATS CMT solutions. The maximum observed ground velocity is shown at the end of each waveform.
improvement in time saving (see Fig. 13). This is very important
for rapid assessment and response to seismic hazards. For example,
when a big earthquake occurs along the southernmost Ryukyu sub-
duction zone (Hsu et al. 2012), a potential tsunami hazard needs
to be taken into account. The tsunami arrives on the east coast in
less than 30 min based upon dispersive (DSP) tsunami equations
simulation (Saito & Furumura 2009). If all the source information
can be obtained within 2 min, especially the focal mechanism, the
appropriate government and emergency response agencies can have
more time to judge whether people need to evacuate the coastal area
Towards real-time earthquake simulation 13
Figure 10. An example of data missing happened in the RMT system. The real-time data of SSLB were missed during the 2012 December 11 earthquake
(Mw4.2). In this case, the system was inverting flat seismograms for the missing data.
Figure 11. An example of the response of a big teleseismic event in the RMT system. All of the used six BATS stations recorded large long-period surface
waves after 2012 April 11 off the west coast of northern Sumatra earthquake (Mw8.6).
14 S.-J. Lee et al.
Figure 12. The relationship between moment magnitude (Mw)andmisfit
reduction (MR) of the events determined by RMT. The green dashed line
shows the MR threshold (MR =60), and the blue dashed line marks the mo-
ment magnitude of Mw3.7. The percentages of the events in each quadrant
are also shown.
Figure 13. The timelines of the RMT monitoring, CWB earthquake report
and the Pwave, Swave, surface wave, as well as tsunami arrivals of a big
earthquake occurring offshore northeast of Taiwan. T0indicates the event
origin time. T1is the time to obtain the information of earthquake origin
time, location and magnitude; the CMT solution and focal mechanism are
determined in T2. The final earthquake report is obtained at Tf.
or not. In this case, the RMT will play an important role in providing
crucial source information in real time.
By connecting the RMT with real-time online earthquake sim-
ulation (ROS) system (Lee et al. 2013), the real-time earthquake
simulation will be achievable. When an earthquake occurs, all the
point source parameters, including event time, location, moment
magnitude, moment tensor and focal mechanism can be obtained
simultaneously within 2 min by RMT. These source parameters will
automatically forward to the ROS system to perform an earthquake
simulation in 3 min. All the numerical simulation results, including
the ShakeMovie and ShakeMap, can be obtained in 5 min after the
occurrence of an earthquake. This real-time earthquake simulation
result can provide a complete ground shaking information for a
rapid response for seismic hazard assessment. Details of the devel-
opment of ROS and its interaction with RMT will be discussed in
another paper (Lee et al. 2013).
5CONCLUSION
We have developed an RMT system to provide the real-time moni-
toring of the earthquake activity in Taiwan. This system uses BATS
continuous broad-band data and searches the best-fitting moment
tensor solution from a 3-D distributed virtual source grid. The
event origin time, location, magnitude, moment tensor and focal
mechanism can be obtained simultaneously within 2 min after an
earthquake occurs. We improve the system performance by taking
advantage of a parallel computing technique, which basically di-
vided the total virtual sources into several parts depending on the
number of computing nodes. This improvement is crucial for the
purpose of real-time earthquake monitoring at a regional scale. In
addition, the use of six BATS stations can form a redundant virtual
seismic network to deal with the data missing problem. The RMT
system has been operated online for more than 1 yr (since 2012).
The online (2012) and offline (from 2010 to 2011) analyses show
that the event origin time, hypocentral location and magnitude deter-
mined by RMT are similar to that in the CWB earthquake catalogue.
The focal mechanisms are also comparable to solutions in the BATS
CMT catalogue.
Our results indicate that the RMT is a robust automatic system
that uses long-period wavefield (10–50 s) of broad-band records to
monitor the earthquake activities at a regional scale in real time.
The real-time source information, especially the focal mechanism,
provided by RMT will be crucial in seismic hazard assessment when
a big earthquake occurs. Furthermore, all the source parameters
provided by RMT will be forwarded to ROS to perform earthquake
simulation, which will provide dense ground shaking information
in real time. The next stage of the RMT development will include
the application of 3-D Green’s functions, finer virtual source points
and hybrid seismic network to improve both the system performance
and station coverage.
ACKNOWLEDGEMENTS
We thank the BATS for providing high-quality real-time broad-band
waveform data. We would also like to thank Dr Aurelie Guilhem
and one anonymous reviewer for their comment and suggestions,
which significantly improved the quality of the paper. This research
was supported by the Taiwan Earthquake Research Center (TEC)
funded through the National Science Council (NSC) with grant
number NSC 100-2628-M-001-007-MY3. The TEC contribution
number for this paper is 00095. A part of the research was funded
by the ERI’s international visiting program.
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