Citation: Wijaya, A., M. Yamada (2022), Development of an earthquake early warning system in thewestern part of Java using a strong motion network, Synopsis of IISEE-GRIPS Master’s Thesis.

DEVELOPMENT OF AN EARTHQUAKE EARLYWARNING SYSTEM IN THE WESTERN PART OF
JAVA USING A STRONG MOTION NETWORK
Angga Wijaya1
MEE21702

Supervisor: Masumi YAMADA2
ABSTRACTThe development of an earthquake early warning system (EEWS) has been done by applying theextended integrated particle filter (IPFx) method using a strong ground motion network in the westernpart of Java, Indonesia. The method was applied to continuous waveforms including 95 earthquakes (M> 4 and seismic intensity ≥ II MMI) and to the one-day waveforms including the 2022 Banten earthquake
sequences. We used 190 stations divided into 99 conventional force-balanced accelerometer sensors(FBA) and 91 Micro-Electro-Mechanical System (MEMS) sensors. Early warning criteria were given
when the number of picks was more than five and the maximum seismic intensity was ≥ 3.5 (IV MMI)
based on the seismic attenuation equation. This system has successfully detected 95 earthquakes andprovided warnings to 46 from 49 events with observed seismic intensity ≥ 3.5 MMI. The system also
successfully detected five earthquakes of magnitude ≥ 4.0 as converged earthquakes and provided
warnings to two events with an observed seismic intensity of ≥ 3.5 for the 2022 Banten earthquake
sequence. The IPFx method shows good accuracy to estimate earthquake source locations with medianerrors of 12 km, 22.7 km, 0.27, and 0.62 for the epicenter, depth, magnitude, and seismic intensity,
respectively, relative to the Indonesia Agency for Meteorology, climatology, and Geophysics (BMKG)
catalog. The system takes 3.4 seconds after the first P-wave was detected, and it is 6 seconds faster thanthe previous method tested by BMKG. Based on the good accuracy and speed in estimating earthquake
sources, the IPFx method has the potential to be developed into an earthquake early warning system inIndonesia in the future.
Keywords: Earthquake early warning, earthquake source, IPFx method.
1. INTRODUCTION
The development of an EEWS in Indonesia began with a collaboration between the Institute of CareLife (ICL) from China and BMKG in 2019 as a pilot stage by installing 197 sensors along the island of
Sumatra to Central Java. At the same time, BMKG is trying to self-develop an EEWS by adding 94
MEMS accelerometer sensors to the strong motion network in the West Java Region. The system wasdesigned with several improvements in the Taiwan regional EEW system (eBEAR). Both systems
currently show that there were still missed earthquakes and low accuracy of earthquake sources. We aim
to develop an earthquake early warning system with a strong motion network in Indonesia byimplementing the extended IPF (IPFx) method by Yamada et al. (2021). The IPFx method has theadvantage of separating the phases of multiple earthquakes and having high accuracy in estimating
earthquake sources. In addition, it also takes a shorter time to detect earthquakes. The performance ofthe method was evaluate based on the system's ability to detect and provide warnings for earthquakes
with an intensity of more than 3.5 (IV MMI) with allowed the seismic intensity error ±1. We also
12Meteorological, Climatological, and Geophysical Agency (BMKG), Indonesia.
Assistant Professor, Disaster Prevention Research Institute (DPRI), Kyoto University.

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analyzed the accuracy of the earthquake source estimation by comparing the IPFx method catalog with
the BMKG catalog.
2. DATAThis study used continuous waveform data from the BMKG strong motion network in the western partof Java, as many as 190 stations with three components consisting of 99 FBA accelerometers and 91
MEMS accelerometers, as shown in Figure 1. The instruments used in the FBA network are TSA-100(Metrozet), Titan (nanometrics), and G210S (Meisei Electrics), which has a sampling rate of 100Hzwith a resolution of 24 bits. Meanwhile, the MEMS accelerometer, P-Alert plus (Sanlien) with asampling frequency of 100 Hz and a resolution of 16 bits, was also used. Earthquake data used in thisstudy were 95 events between January 1, 2020, and March 30, 2022, with a magnitude greater than 4.0,
as shown in Figure 1. Stations with a data loss of more than 15 seconds during an earthquake or a clock
error were not included in the simulation. Simulations were also carried out with a one-day continuous
waveform on January 14, 2022, during the Banten Earthquake (M6.7).

Figure 1. Seismic station and earthquake distribution used in this study.
3. METHODOLOGY
3.1. Extended IPF Method
The IPFx method is a new source estimation algorithm updated from the IPF method (Tamaribuchi etal., 2014) for EEWS enhancement (Yamada et al., 2021). This method has two steps: single station
processing for every station and network processing for centralized event detection and source
estimation. This method can analyze multi-events simultaneously with several predetermined conditions
based on the amplitude and P-wave arrival time data.
In the single station processing every one-second accelerometer data was processed byeliminating the DC offset and instrument response (Yamada et al., 2014). P-wave detection was carriedout by applying the Tpd method (Yamada and Mori., 2022) after applying a second-order bandpass filter
with a corner frequency of 5-10 Hz to eliminate the effects of noises. Furthermore, the amplitude
parameter was extracted in the form of maximum vertical velocity, maximum vertical displacement, andamplitude (the sum of the three component vectors) of acceleration, velocity, and displacement. The
information was used for multiple definitions of magnitudes.
The trigger information obtained from single station processing was transmitted to network
processing to detect new earthquakes and estimate earthquake sources. When a new trigger was observed2and does not match the existing event, it will be categorized as an EQp (pending earthquake), which
may be a P-wave from a potential earthquake. If there are multiple triggers close to the first trigger (atleast 3 triggers), the earthquake was converted into EQ (ongoing earthquake), and the earthquake source
estimation begins. The location of the EQ was estimated and updated every second. The source
parameters were estimated using the IPF method (Wu et al., 2014) with a Bayesian Inference approach
where the initial hypocenter is the center of the Voronoi cell from the first trigger. The number of stations
used for the source estimation was defined by the estimation group. The estimation group includes the
20 closest stations from the first trigger station and 10 additional stations with wide azimuthal coverage.
The EQ was converted into EQc (converged earthquake) when the source parameters become stable.
3.2. Seismic Intensity Estimation
In this study, the intensity estimation becomes essential to decide which earthquake warning information
was disseminated to the public. The warning criteria used the number of picks more than five and seismic
intensity (SI) estimation greater than 3.5 (IV MMI). Intensity estimation used the converted peak groundacceleration (PGA) by equation from Wald (1999). The PGA calculation in this study followed theground motion prediction equation (GMPE) used by BMKG in producing the shakemap. Two formulas
were used depending on the depth identified as the interface, crustal or slab earthquakes, and theearthquake magnitude. The GMPE in Zhao et al. (2006) was used for deep earthquakes with a depth of
more than 50 km or large earthquakes with a magnitude of more than 5.3. The GMPE in Akkar andBoomer (2007) was used for small and shallow earthquakes (depth < 50 km and magnitude < 5.3).
4. RESULTS AND DISCUSSION
4.1. System performance
The results showed that the IPFx method detected all 95 earthquakes divided into groups : (1) 43 events
that succeeded in providing the warnings (observed seismic intensity ≥ 3.5 and estimated seismic
intensity ≥ 3.5- 1.0), (2) three that failed to give a warning (underestimated seismic intensity), (3) threewere false alarms (overestimated seismic intensity), and (4) 46 events that succeeded to not giving awarning (observed seismic intensity < 3.5 and estimated seismic intensity < 3.5+1.0). The system took
an average of 3.4 seconds to detect earthquake events after the first P-wave detection or 16.8 seconds
after the origin time. Since the earthquakes mostly occurred off the southern coast of the island of Java

Figure 2. Histograms of epicenter, depth, magnitude, and SI errors in first warning catalog forearthquake with SI ≥ 3.5 (a) and final catalog (b). Red histogram in (b) shows the parameters error forearthquake with SI ≥ 3.5. The top-right numbers in each histrogram show the median error for IPFx
method compared with BMKG catalog.
and in the Sunda Strait, the time to detect earthquakes was longer than that of the shallow inland3

earthquakes. Our results showed the earthquake was detected 17.5 seconds after the origin time, and 7.5
seconds for inland earthquakes.
Figure 2 shows the accuracy of the earthquake source parameters from the estimation resultsof 95 earthquakes using the IPFx method, compared with the BMKG earthquake catalog. Figure 2a
represents the accuracy of the initial earthquake parameters obtained when the system gave the warning.
In the histogram, 50% of events have errors of 31 km, 9.9 km, 0.66, and 0.85 for the epicenter, depth,
magnitude, and seismic intensity, respectively. In addition, the location of the earthquake source isreasonably accurate, although there are still more than 50% of events with errors > 30km. Figure 2b
shows the accuracy of earthquake parameters in the final catalog. The final source parameters estimatedby the IPFx method were improved, especially for the epicenter and magnitude, where 50% of events
had errors of 12km and 0.27, respectively. In addition, in the histogram, the epicenter of the 79%
earthquake has an error of <30 km.
4.2. Comparison IPFx and eBEAR method
We compared the current system with the previously tested EEWS, the eBEAR method. We used 12
earthquakes in 2022 with an observed intensity of ≥ 3.5 for the comparison. Figure 3 shows an exampleof the mainshock of the 2022 Banten earthquake. It shows the difference in the accuracy of theearthquake parameters of the two methods. The hypocenter location of the IPFx method is closer to the
BMKG catalog than the eBEAR method. Based on the time history of the earthquake parameters, theIPFx method can detect the earthquake and provide an early warning 12 seconds faster.
The comparison of earthquake
detection speed is shown in Figure 4. The
average time to issue earthquake warnings by
the IPFx method after detecting the first Pwave was 3.4 seconds, and 17.6 seconds after
the origin time. Meanwhile, the eBEAR
method took 10.2 seconds after the first Pwave detection and 23.5 seconds after theorigin time. It showed that the IPFx method
was 6 seconds faster than the eBEAR method.

Figure 3. Result of the Banten Mainshock on January
14, 2020, with magnitude 6.7 and SI 7.5 (VIII MMI).
a) Estimated source location at converged time.
Black, blue, and white stars, respectively, indicate
the source location for BMKG catalog, IPFx, andeBEAR. The colored and white triangles,
respectively, indicates the triggered and nontriggered stations in estimation group. (b)-(d) Timehistory of estimated earthquakes parameters after
origin time. Red and black lines, indicate the resultof IPFx method and eBEAR method. The vertical
black lines indicate time of earthquake warning. (b)epicenter error, (c) magnitude, (d) seismic intensity.

Figure 4. Time of first event detection after
origin time for IPFx and eBEAR.

4.3 Example of Incorrect Earthquake Source Estimation
Figure 5 shows an example of earthquakes with incorrect epicenter estimation. The left example is anearthquake on July 28, 2020. It shows a large error in the earthquake hypocenter estimation from the
IPFx method. The MEMS sensor marked by red boxes failed to detect the P wave due to weak ground4

motions and low sensitivity of the MEMS sensors, and then the S-wave was detected and treated as the
P-wave arrival time for earthquake source estimation.
The right figure shows an example of the earthquake on January 27, 2020. The estimatedearthquake source was far from that of the BMKG catalog. Due to this location error, SI wasunderestimated. The error was caused by the poor quality of the accelerograms on the distant FBA sensor.
The triggers were caused by noises before the P-wave arrivals which moved the estimated source farther
offshore than the actual location.

Figure 5. Result of 28 July 2020 (left) and 27 January 2022 (right) earthquakes. (a) the location of theearthquake source where the blue and black stars indicate the optimal location of the IPFx and BMKG
catalog estimates, respectively. (b) the time history of magnitude (black line) and seismic intensity (redline) after the detection time. The black and red circles indicate the magnitude and seismic intensityfrom the BMKG catalog. (c) Comparison of the theoretical and observed P-wave arrival times from the
IPFx method. Blue lines indicate the envelope signal after the first detection time. (d) waveforms of theearthquake with ‘A’ as P-wave arrivals and the red box indicates the bad detection (left) and the noisy
signal (right).
4.4. Performance comparison of different strong motion network
We tried to compare the effect of sensor type by simulating one-day waveform data with three different
stations groups, FBA Accelerometer only, MEMS Accelerometer only, and a combination of the two
sensors (FBA+MEMS). The simulation results show that seven events, three events, and five eventswere detected by FBA, MEMS, and combined sensor networks, respectively.
Table 1. Average errors of epicenter, magnitude, and detection time after the origin time for the different
seismic networks.
∆M∆R (km)Network Type
Detection Time (s)
FBA

5.6

0.3

17.3

MEMS

54.2

0.7

13.8

FBA+MEMS

9.6

0.33

12.1

5

Table 1 compares the accuracy of the three earthquakes detected with three types of seismic
networks. The result of the FBA sensor only showed higher accuracy for epicenter error. On the other
hand, the average detection time after the origin time was 17.3 seconds, 13.8 seconds, and 12.1 seconds
for FBA only, MEMS only, and combined seismic networks, respectively. The station density and sensor
noise level greatly influenced the detection time and accuracy.
5. CONCLUSIONSThe IPFx method showed a good performance to detect 95 earthquakes with reasonable accuracy. Fortythree earthquakes above the warning criterion (seismic intensity ≥ 3.5) were successfully identified as alarge event (estimated seismic intensity ≥ 3.5- 1.0), 46 earthquakes under the warning criterion weresuccessfully estimated as a small event (estimated seismic intensity < 3.5+1.0), three events failed togive a warning (underestimated seismic intensity), and three events were false alarms (overestimatedseismic intensity). This system could detect events 3.4 seconds after the first P wave was detected, 17.5
seconds after the origin time for offshore earthquakes, and 7.5 seconds for inland earthquakes. Our
results showed that the IPFx method had median errors of 31 km, 9.9 km, 0.66, and 0.85 for the epicenter,
depth, magnitude, and seismic intensity, respectively. The final catalog showed that 76 of the 95
earthquakes had an epicenter error of < 30 km, and 65 events had an absolute error of seismic intensity
less than 1. The comparison of the performance of the IPFx method with the eBEAR method showed
the IPFx method had an average epicenter error of 24.95 km, while the eBEAR method was 59.59 km.
In addition, the IPFx method was 6 seconds faster in detecting and providing earthquake warnings thanthe eBEAR method.
We conclude that the IPFx method has the potential to be developed as an earthquake early
warning system in Indonesia using the BMKG strong motion network. This system can improve theaccuracy and speed of detecting and providing earthquake warnings. While there were limitations to the
IPFx method caused by the poor quality of the accelerometer data from the FBA sensor and thedifference in the sensors used, the MEMS sensor with low sensitivity cannot correctly record weak
earthquake motion.
ACKNOWLEDGEMENTS
This research was conducted during the individual study period of the training course “Seismology,
Earthquake Engineering and Tsunami Disaster Mitigation” by the Building Research Institute, JICA,
and GRIPS. I would like to express my deep gratitude to my supervisor, Masumi Yamada, Ph.D. from
Kyoto University for her valuable support, experience, and knowledge to use the IPFx programs during
my individual study. I would also like to acknowledge Dr. Takumi Hayashida for his permanent support
from the beginning to the end of the program.
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