FEASIBILITY STUDY ON EARTHQUAKE EARLYWARNING SYSTEM FOR THE CITY OF LIMA, PERU,USING A NEWLY DEPLOYED STRONG-MOTIONNETWORK
Cinthia CALDERONMEE17701

Supervisor:

Takumi HAYASHIDA
Toshiaki YOKOI

ABSTRACT
Feasibility of earthquake early warning system (EWS) for the city of Lima, Peru, was investigated using
seismic waveform data from the newly installed real-time strong motion observation network. We
selected waveform data from 24 earthquakes (ML>3.5, depth<100 km) and estimated sizes and locationsof the events using initial P-wave portions, based on the conventional approaches. For the estimation ofearthquake magnitude, we determined “τc” parameter using 3 s time window after the P-wave onset. We
found a reasonable correlation between the estimated and catalogue magnitudes for earthquakes in thevicinity (hypocentral distance<130 km). The hypocenter locations were determined with the detected Pwave arrival times. To investigate the accuracy of existing ground-motion prediction equations(GMPEs) in a real-time scheme, we selected two GMPEs for subduction zone earthquakes to compare
the predicted peak ground accelerations (PGA) with observed ones. We also confirmed that the size ofblind zones of the EWS is not so large for most earthquakes and it is possible to give an alert to the cityof Lima before S-wave arrivals, if a hypocenter location was accurately determined.
Keywords: Earthquake Early Warning System, Hypocenter, Peak Ground Acceleration, Blind zone.

1. INTRODUCTION
Peru is located in the most seismically active region of the world, called the Circum-Pacific belt orPacific Ring of Fire. Lima City, the capital of Peru, experienced earthquakes and tsunamis frequently
due to the location. The last large event in the city occurred in 1974 (Mw 8.0) and strong ground motions
hit the coastal areas of Lima and Callao, causing severe damage. More than 40 years have passed sincethen and the probability of larger seismic events has been increasing. According to earthquake scenarios
by Pulido et al. (2015), an Mw 8.9 earthquake in the central coast of Peru would directly affect Lima
City is expected to occur. Earthquakes EWS can be used to prepare the citizens for the earthquake justbefore the strong ground shakings and reduce the casualty and economic loss. The purpose of this study
is to examine the performance and expected problems of an earthquake EWS in Peru using data fromthe newly installed real-time strong ground motion network.

2. DATAThe data presented in this study was taken from the accelerograph network that belongs to the Japan
Peru Center for Earthquake Engineering and Disaster Mitigation (CISMID). At each station strongJapan Peru Center for Earthquake Engineering and Disaster Mitigation (CISMID), Peru.
International Institute of Seismology and Earthquake Engineering, Building Research Institute, Japan.

1motion accelerograph REFTEK 130-SMA is installed at sample frequency of 200 Hz. There are 19
observation stations in Lima with 2-12 km station intervals. We selected earthquakes occurred in 2017
when the operation of the network started and obtained 347 records from 24 events. The magnitudes ofthe events are bigger than or equal to ML 3.5 and the focal depths are smaller or equal to 100 km. Table
1 is the list of the events used in this study.
Table 1. Catalog of the selected data after the magnitude estimation (data from the accelerographnetwork of CISMID).
Origin Time (UT)

Lat.

Lon.

Depth (Km)Magnitude (ML)2017/5/3 19:05:49
2017/5/11 10:32:47
2017/7/8 20:07:24
2017/9/14 8:19:23
2017/9/15 4:10:36
2017/9/25 19:54:14
2017/10/22 0:09:13
2017/11/1 3:36:03
2017/11/24 2:24:26
2017/11/24 11:15:53-12.24-12.07-11.72-11.91-11.91-11.96-12.32-11.77-12.10-12.09-77.40-77.45-77.37-76.34-76.33-77.66-77.34-77.55-77.55-76.25

32
31
73
40
18
28
36
50
46
76

4.0
3.9
4.8
4.8
4.4
4.0
4.7
4.4
4.1
4.7

No.
Records
9
8
12
17
16
17
17
19
19
19

No. Select.
Records
9
8
12
17
5
3
17
16
12
17

3. METHODOLOGY
3.1. Tauc (τc) parameter
In order to determine the size of an earthquake, it is important to know if the fault rupture stopped or isstill growing, which is generally reflected in the period of the initial motion (Wu and Kanamori, 2005).
We use an average of the period during the first motion (3 s of initial P-wave) to judge the source
process. At first we computed the ratios of displacement 𝑢(𝑡) and the velocity 𝑢̇ (𝑡) waveforms using
the vertical component of initial P-wave motions2
0 u (t )dt .
(1)
r 0
0 2
 u (t )dt0

The integration is over the time (0, τ0) after the P-wave onset. We assigned 3 s for τ0.
According to Parseval’s theorem, Eqs. (1) can be modified asr

4 2  f 2 uˆ ( f ) df
02 uˆ ( f ) df
2

 4 2 f 2 ,(2)0

where |𝑢̂(𝑓)| is the amplitude spectra of 𝑢(𝑡) and 〈𝑓 2 〉 is the average of 𝑓 2 weighted by |𝑢̂(𝑓)|2.
Finally the parameter 𝜏𝑐 is defined as

c 

1



f2

2

,

(3)

r

and the relationship between the average 𝜏𝑐 and magnitude is formulated (Kanamori, 2005; Wu andKanamori, 2005) as
M est ( c )  (4.525 ) log  c  5.036 .
(4)2

3.2. Automatic Picking of P-wave
To detect the initial arrival of P wave automatically from the observed waveforms, we tested theautomatic phase-picking algorithm proposed by Allen (1982). The detection of the event isaccomplished by comparing a characteristic function or its short-term average (STA) with a thresholdvalue (THR). If the STA exceeds THR, a trigger (P arrival) is declared.
3.3. Attenuation laws for estimating ground motion parameters
In Peru, there is no original ground motion prediction equation (GMPE) and various existing GMPEs
derived in other seismotectonic environments have been tested. The frequently used ones are theattenuation law of Youngs et al. (1997) and Zhao et al. (2006).
3.3.1. GMPE of Youngs et al. (1997)
The relationship was developed based on regression analyses using data from 174 earthquakes in Alaska,
Chile, Cascadia, Japan, Mexico, Peru, and Salomon Islands. They used events whose moment magnitude
was larger than or equal to 5 and hypocentral distance from 10 to 500 km. There are two different
functional forms of the relationship depending on site characteristics (soil site and rock site). Here weassumed that rupture distance is equivalent to hypocentral distance and source types of all earthquakes
are interplate events.
3.3.2. GMPE of Zhao et al. (2006)
The authors used 4518 earthquakes in Japan, 208 earthquakes in Iran and Western USA. In the dataset
the maximum focal depth is 162 km and the maximum source distance is 300 km. The events areclassified into three source types; crustal, interplate and intraslab earthquakes. The maximum possible
values for source depth is 25 km for crustal events, 50 km for interface events, and between 15 and 162
km for intraslab events. This equation also considers site conditions and the site amplification isevaluated depending on the NEHRP (National Earthquake Hazards Reduction Program) siteclassification.
4. RESULTS AND DISCUSSION
4.1. Magnitude estimation
The magnitude was computed using the software “tauc”
provided by Dr. Masumi Yamada, which is based on the Tauc(τc) theory. We applied the software “tauc” for all the stations
and events, considering two parameters, 0.2 Hz for the cutoff
frequency of Butterworth high pass filter and 0.05 gal for
acceleration threshold value.
After the processing for all the recordings (as isshown in Figure 1), we selected the data again because theestimated values of magnitude were largely overestimated forevents whose epicentral distance was long and whose
magnitude was small, due to low S/N ratios. After thescreening, we found that the software can be applied to data
whose hypocentral distance is less than 130 km and magnitudeis larger than 3.5. The number of available events was reduced
to 10, although the predicted magnitude scales are slightly
overestimated.

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Figure 1. Comparison between the
catalogued and estimated magnitude
for the selected 24 events.

4.2. Hypocenter location estimation
The hypocenter estimation was computed with the software“hypo” developed by Dr. Takumi Hayashida, which uses theautomatically picked P-wave arrivals to find reasonablehypocenter location that reduces the detected and predictedP-wave arrivals by repeated calculation. In general, theresults show large differences between estimated andcatalogue hypocenter locations as is shown in Figure 2,
which were not reasonable due to the lack of stations along
the coastal line for the case of subductions earthquakes.
4.3. Peak Ground Acceleration estimation
We obtained the observed PGAs from the dataset. We also
applied the two GMPEs, considering soil conditions (Vs30
values) where the stations are located. We found that theagreement overestimated, underestimated and goodcorrelations on the calculated values of PGA as is shown in
Figure 3.

OverestimatedUnderestimatedFigure 2. Map showing epicentral
locations of the selected events (redcircles: catalogue epicenters, purple
circles: estimated epicenters).

Good correlation

Figure 3. Comparison between the observed and predicted PGAs at three stations.
4.4. Analysis of the blind zone
Earthquake Early Warning Systems
usually has a blind zone around theepicenter where the S-wave arrives
ahead of or coincident with thewarning issuance. The blind zone isdefined as the region around theepicenter where no warning is issued
because the strong shaking has alreadyarrived by the time the alert isgenerated (Kuyuk and Allen, 2013).
Figure 4 shows the resultof blind zone analysis for an event(date, time, magnitude, depth). Here
we assumed that 3 seconds is required
for the processing. For thisearthquake P-wave front reached thefarthest station 11.9 s after the

Figure 4. Left: Map of the calculated and catalogue (star)
epicenter locations, along with P- and S-wave arrivals. Right:
Time-distance plot illustrating the relative timing of arrivals of
P and S waves, warning time and blind zone.

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earthquake origin time and we found that at least 14.9 s is needed to determine magnitude andhypocenter location, and give a first alert to Lima (dashed black line). According to this result, the Swave front arrives at a distance of 38.5 km at the warning time and Lima is outside of the blind zone.
However, the result with the real-time estimated epicenter location indicates that the S-wave reaches all
the stations at the warning time and this differs from the actual observations. This indicates that theestimated hypocenter is not reliable and accurate estimation of hypocentral location is important toprevent false alert and to find the affected area.
4.5. Experiment on determination of the epicentral distance using single stations
In this study, we also investigated the possibility to estimate epicentral locations using a single-station
approach, since we have difficulty in accurate estimations of hypocenter location. Odaka et al. (2003)
proposed a concept of estimation of epicentral distance and this is based on the analysis of the initial Pwave portion (2-3 s) observed at a single seismic station. In this method, the coefficient B is treated as
an index to indicate the increasing ratio (i.e. slope of the envelope) of the P-wave. It can be obtained byfitting the envelope curve of absolute amplitude for the observed P-wave’s initial phase into thefollowing equation;y (t )  Bt exp(  At ) ,(8)where y represents envelope of high-frequency UD-component acceleration and t represents the timeafter P-wave onset. Parameter A is the regression coefficient that relates to earthquake size. After that,
Yamamoto et al. (2012) proposed a method to improve the performance of the approach using thefollowing approximation,y (t )  Ct ,(9)where coefficients C are derived by an ordinary
least square regression. This method utilizes only
0.5 s window data of P-wave initial motion.
Here we tested the C-∆ method in thisstudy to investigate if it can be a reasonable tool forthe estimation of the epicentral distance also in
Peru. For the procedure, we considered a 10-20 Hz
bandpass filter and extracted 0.5 s of the P-wave
onset for all earthquake recordings selected in thisstudy. The relationship between the derived C value
and the epicenter distance is shown in Figure 5. The
number of data is limited and it is difficult toestablish a reliable formula in Peru, but the derived
trend is similar to those of Japan (Yamamoto et al.,2012).
Figure 5. Relationship between C values
and epicentral distance ∆ for earthquakes
presented in this study. Black circles
represent the total data (24 earthquakes) andred circles represent the selected data (10earthquakes).

55. CONCLUSIONSIn the hypocenter determination, we found that at least 8 stations are needed to estimate the hypocentral
parameters. When the number of the seismic stations was larger than 16, the results were good and the
hypocenter locations became very similar to those of the CISMID catalogue. On the other hand, when
the number of the stations was between 9 and 12, the results were not reasonable, indicating that realtime observation stations should be distributed not only in Lima city, but also outside the city.
From the PGAs comparison, we noticed an overestimated tendency in the calculated valuesof PGA for stations with small Vs30 values (≤ 500 m/s). On the other hand, we also noticed anunderestimated tendency in the calculated values of PGA for those stations with larger Vs30 values.
We found that the blind zone cannot be a big problem in the case of Lima but it is not feasible
to install EWS in Peru at the present, because the hypocentral locations were not well determined under
the present observation network.
Moreover, the determination of epicentral distance using single station can be used for thecase of Peru, since we do not have enough seismic stations and it is used also for small earthquakes. We
found a relationship between the C parameter and the epicentral distance of the earthquakes used forthis study, and this information could help to implement the earthquake EWS of Peru in the future.
6. RECOMMENDATION
For the reasonable detection of subduction zone earthquakes, the stations should be located along thecoastal line.
It is important to remember that the values of PGAs were calculated with GMPEs from othercountries. Therefore, in order to increase the accuracy of the predictions the development of GMPE in
the study area is recommended.
In order to increase the accuracy of the source-to-station distance, further accumulation ofearthquake data including future earthquakes and past earthquakes are needed.
ACKNOWLEDGEMENTS
I would like to express my gratitude to my supervisor Dr. Takumi Hayashida and Dr. Toshiaki Yokoi
for their guidance, teachings, kindness, valuable suggestion and instruction during my study.

REFERENCES
Allen, R., 1982, Bull. Seism. Soc. Am., 72, 6B, S225-S242.
Kanamori, H., 2005, Annu. Rev., Earth Planet. Sci., 33, 195-214.
Kennett, B. L. N., Engdahl, E. R., 1991, Geophysical Journal International, 105, 2, 429-465.
Kuyuk, H. S., Allen, R. M., 2013, Seismological Research Letters, 84, 6, 946-954.
Nakamura, Y., 1988, In Proc. of the 9th World Conference on Earthquake Engineering, 7, 673-678.
Odaka, T., Ashiya, K., Tsukada, S. Y., Sato, S., Ohtake, K., Nozaka, D., 2003, Bull. Seism. Soc. Am.,93, 1, 526-532.
Pulido, N., Aguilar, Z., Tavera, H., Chlieh, M., Calderón, D., Sekiguchi, T., Yamazaki, F., 2015, Bull.
Seism. Soc. Am., 105, 1, 368-386.
Wu, Y. M., Kanamori, H., 2005, Bull. Seism. Soc. Am., 95, 1, 347-353.
Yamamoto, S., Noda, S., Korenaga, M., 2012, RTRI report, 26, 9, 5-10.
Youngs, R. R., Chiou, S. J., Silva, W. J., and Humphrey, J. R., 1997, Seism. Res. Letters, 68, 1, 58-73.
Zhao, J. X., Zhang, J., Asano, A., Ohno, Y., Oouchi, T., Takahashi, T., Ogawa, H., Irikura, K., Thio, H.,
K., Somerville, P., Fukushima, Y., 2006, Bull. Seism. Soc. Am., 96, 3, 898-913.

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