Earthq Sci (2020)33: 281–292 281 doi: 10.29382/eqs-2020-0281-01

Earthquake early warning system in Liaoning, based on PRESTo*

Wuchuan Xu1 Xiangyu An2 Enlai Li2 Chengwei Wang2 Li Zhao1,*

1 School of Earth and Space Sciences, Peking University, Beijing 100871, China 2 Liaoning Earthquake Agency, Shenyang, 110034, China

Abstract Liaoning is located in northeast China with fault zone, the Tancheng-Lujiang fault zone, also known as a high level of seismic activity, and earthquake early warning Tanlu fault zone (TLFZ), crosses the middle of the is important for the mitigation of seismic hazard. In this work, province in a northeast direction. The TLFZ has a mainly we implement PRESTo, an open-source software platform for right-lateral strike-slip motion and is considered to be the earthquake early warning based on regional seismic records, eastern margin of the North China basin (Allen et al., to the Liaoning seismic network. For the early warning of earthquakes in Liaoning, a travel-time table is created for 1997; Yin, 2010). It is seismically active and faults in and event detection and location using an average crustal model, around the TLFZ are predominantly striking northeast. All and the empirical relation is established between the earth- major historical earthquakes in eastern China, and in quake magnitude and the initial P-wave amplitudes. Using Liaoning Province in particular, are related to the TLFZ. archived seismic records of past earthquakes, we determine Although not as seismically active as other earthquake- the optimal values for Liaoning using the core algorithms of prone regions such as the western Pacific or the PRESTo. Based on the optimal parameters, the uncertainty in Himalayas, there are frequent earthquakes in Liaoning and event location is generally less than 5 km, and the lead time of surrounding areas (Figure 1) with a dozen or so strong the early warning is ~15 s at 100-km epicentral distance. The historical earthquakes, such as the 1944 M 6.6 earthquake implemented system can be directly put into routine W in Dandong (39.887°N, 124.148°E) near the China-Korea earthquake early warning operation by linking it with the real- time data stream from the Liaoning seismic network. border; the famous 1975 Haicheng earthquake (MS7.5), which was arguably the first successfully “predicted” Keywords: PRESTo; earthquake early waring; Liaoning seismic major earthquake in human history (Wang et al., 2006); network and the disastrous (MW7.6) which occurred in the neighboring Hebei Province and

killed more than 200,000 people. There are also occasional moderate (M5~6) earthquakes with very shallow depths 1 Introduction that can cause extraordinary damages, such as the 2013

MS5.1 Dengta earthquake (Su et al., 2020). Therefore, Liaoning is located in northeast China and is Liaoning is a region with a high earthquake hazard considered as part of the Northeast Asia Active Block potential and, given the high population density and recent (Zhang, 2003). The region is under the influence from the economic development, earthquake early warning (EEW) east by the subduction of the Pacific Plate under the is very important for the mitigation of seismic disasters. Eurasian Plate and from the southwest by the collision of In the past few decades, with the development in the Indian Plate with the Eurasian Plate. As shown in seismic monitoring networks and telecommunications Figure 1, the topography of Liaoning Province varies in technologies, EEW systems have been developed and put ESE direction with uplifts on both the east and west border into routine operation in many parts of the world, regions and a depression in between. A major NE-trending including (UrEDAS, Kamigaichi et al., 2009), Mexico (SASMEX, Espinosa-Aranda et al., 2009), * Received 3 August 2020; accepted in revised form 14 December California (ShakeAlert, Kohler et al., 2018), Italy 2020; published 26 December 2020. (PRESTo, Zollo et al., 2009; Satriano et al., 2011), Turkey * Corresponding author. e-mail: [email protected] © The Seismological Society of China and Institute of Geophysics, (SOSEWIN, Fleming et al., 2009) and Taiwan (Hsiao et China Earthquake Administration 2020 al., 2009). Since the turn of the century, especially after the

282 Earthq Sci (2020)33: 281–292

118°E 120° 122° 124° 126°

44°N Inner Mongolia

Jilin

HXQ XFN FKU TIL FXI XMN QYU BEP FSH 42° JIP Liaoning SNY MQI CHY LHT Shenyang BZH GSH TanluLYN Fault ZoneBXI HUR LYA NAP JZH ANS H58 JCA SHS Haicheng KDN SUZ YKO XYN GAX Hebei DDO North Korea GUS 40° WFD HSH DLD

Bohai Sea DL2

2.5 ≤ ML ≤ 3.0

Yellow Sea 3.0 < ML ≤ 3.5 (12) 38°

3.5 < ML ≤ 6.0 (13) 6.0 ≤ M < 7.0 (9) M ≥ 7.0 (5)

−500 0 500 1000 1500 2000

Elevation (m) Figure 1 Map of Liaoning Province and surrounding areas. Background color shows the topography. Black triangles indicate locations of the seismic stations of Liaoning seismic Network (LNNet). The black lines represent major faults (fault data are from https://gmt-china.org/data/) in the region, and the thick red lines depict the Tanlu fault zone. Black dots are epicenters of earthquakes of 2.5 ≤ ML < 3.0. Red squares and stars mark the epicenters of 25 earthquakes of magnitudes 3.0 < ML ≤ 3.5 and 3.5 < ML ≤ 6.0, respectively, during 2009–2019 whose records are used in this study to implement PRESTo for LNNet. Orange and red circles are strong historical earthquakes of magnitudes 6.0 ≤ M < 7.0 and M ≥ 7.0 since 1900, respectively. White circles show major cities in Liaoning Province disastrous MW7.9 Wenchuan earthquake in 2008, EEW Federico II in Naples, Italy. All the PRESTo-related files systems have been established quickly in earthquake prone including software as well as documentations such as in- provinces in Chinese mainland, such as in Sichuan and stallation instruction and user manual can be freely down- Yunnan region (Peng et al., 2013; 2015, 2017; 2019; 2020; loaded from its official website (http://www.prestoews. Peng and Yang, 2019), and in Fujian (Zhang et al., 2016). org). The software integrates recent algorithms for real- This study is the first effort in using the recently deployed time, rapid earthquake detection, location, magnitude estim- seismic network in Liaoning Province in northeast China ation and damage assessment into an easily configurable to build an effective EEW system. and portable package (Satriano et al., 2011). PRESTo has In establishing the EEW system for the Liaoning regi- been under active experimentation in southern Italy on the on, we employ the open source software PRobabilistic and Irpinia Seismic Network (ISNet). It is a readily adaptable Evolutionary early warning SysTem (PRESTo). PRESTo and user-friendly platform and has been adopted in the was developed by the RISSC (RIcerca in Sesmologia EEW operations in many seismic networks worldwide Sperimentale Conputazionale) laboratory of the University (e.g. Picozzi et al., 2015; Pitilakis et al., 2016).

Earthq Sci (2020)33: 281–292 283

2 Method and seismic data The 25 events are listed in Table 1 and their locations are shown in Figure 1. Once PRESTo can run successfully for archived data, the EEW system can be put into practical We first make a brief introduction about the main operation by plugging in real-time data streams from the concept of PRESTo for the benefit of discussion in Section Liaoning seismic network. 3 on our implementation to the Liaoning region. Theo- retical and technical details on PRESTo can be found on the official website of the package (http://www.prestoews. 3 Implementation of PRESTo to LNNet org) as well as references listed therein. PRESTo is composed of four core algorithms for event In general, PRESTo is a user-friendly software detection, location, magnitude determination and ground platform that can easily be implemented in different motion prediction (Satriano et al., 2008). The first one is regions using local network such as the LNNet data. FilterPicker (FP) for automatic, real-time phase picking However, a number of parameters used by the core (Lomax et al., 2012; Vassallo et al., 2012). FP is designed algorithms in PRESTo must be tuned based on data from on the basis of the classical short-term average/long-term specific regions. Therefore, our aim in this study is to average (STA/LTA) algorithms (Allen, 1982; Baer and determine the optimal set of parameters for the LNNet and Kradolfer, 1987) and can realize real-time phase picking discuss the performance of the EEW system. For the from continuous data streams with high efficiency and implementation and offline testing purposes, PRESTo accuracy. FP adopts two picking thresholds S1 and S2, and can be run in simulation mode in which it reads the SAC the picking is carried out when the value of a characteristic files from the archived records and converts them into data streams to simulate the actual early warning operation function exceeds S1 and meanwhile the integral of the using real-time data. The implementation of PRESTo characteristic function exceeds S2. The second core algo- rithm in PRESTo is real-time evolutionary earthquake involves four major tasks: configuration of region-specific location algorithm (RTloc) (Satriano et al., 2008) for real- files according to the network information; building the time evolutionary earthquake location. It starts locating the travel-time table for all seismic stations involved; event as soon as the first station is triggered (i.e. when the supplying the equation for magnitude estimation and P-wave is detected and picked by FP), and stations that are ground motion prediction; and setting the optimal values not triggered can also be included to reduce the uncertainty of miscellaneous parameters for the algorithms in of the location result. The third core algorithm is RTmag PRESTo. (Lancieri and Zollo, 2008), which uses an empirical 3.1 Region-specific information relationship to estimate the magnitude M of the earthquake from the peak ground displacement (PGD), dpeak and the When implementing PRESTo to a new seismic epicentral distance R. The fourth core algorithm of network, several text files containing information on the PRESTo is for ground motion estimation, which calculates seismic network and stations such as network and station the ground motion based on the event magnitude using names and coordinates, need to be configured. In appropriate ground motion prediction equations (GMPEs). particular, a list of target sites can be supplied by a text The user interface of PRESTo for Liaoning is shown in file, to which EEW alerts can be sent. Figure 2. Once the target region for the EEW coverage is In this study, our goal is to implement the PRESTo determined based on the station distribution, a map file for platform to realize the EEW system for Liaoning region the target region needs to be provided for the user using the Liaoning seismic network LNNet. The station interface. For the EEW using LNNet records, we choose distribution of LNNet is shown in Figure 1. Currently, the the target region with longitude and latitude ranges of LNNet has 37 stations, 5 of which belong to the national 118.4°E–127.0°E and 38.3°N–43.6°N (only partly shown network equipped with JCZ-1, CTS-1E, CTS-1EP, CTS- in the user interface in Figure 2), respectively. The depth 1E and CTS-1EF instruments, respectively, while the rest range of the target region is from the surface down to are equipped with BBVS-60 seismometers. The implemen- depth of 40.0 km. tation involves configuring the region-specific files and 3.2 Travel-time table calculation finding the optimal parameters in the four core algorithms in PRESTo. For this purpose, we use archived LNNet The speed of operation is one of the key requirements broadband records of 25 earthquakes of magnitudes 3.0 < for the EEW system. A standard approach used by seismic

ML ≤ 6.0 in Liaoning from November 2012 to March 2017. networks is to establish a pre-calculated travel-time table

284 Earthq Sci (2020)33: 281–292

Figure 2 Screenshot of the user interface of PRESTo in processing the archived records from the LNNet near the origin time of the 10 January 2013 ML3.9 earthquake (event #2 in Table 1). The left panel shows the z-component of velocity data streams and picking process. Waveforms in the time windows highlighted in yellow are used to estimate the magnitude. The right panels illustrate the location result (top: latitude and longitude; middle: depth) and magnitude estimation process (bottom). The red star denotes the hypocenter with the estimated magnitude also shown. The tetrahedrons are the stations of the LNNet, with the color indicating the peak displacement at the station. The yellow and red circles represent the wave front of P- and S- wave, respectively. The red line in the bottom panel denotes the origin time of the earthquake. The location and magnitude estimation results are given at the bottom. that can be looked up during real-time operations to speed models in Table 2 with different grid sizes for generating up the process. In order to cover all possible locations of travel time tables and compared the event locations result earthquakes, a network composed of grid points is set up in to find the most appropriate velocity model and the the entire target region and the travel-time table contains optimal grid size. Table 3 provides the locations for the 23 the travel times of P and S waves between all the grid January 2013 ML5.1 earthquake (event #4 in Table 1). The points to all the stations. The grid size is determined based results show clearly that the grid size of 4 km is too coarse, on the density of the stations and the expected performance leading to relatively large uncertainties in location and of the EEW system. Given the station and target site origin times, and the size of 1 km does not improve the locations, the grid and the crustal velocity model, the performance further while increasing the calculation cost. travel-time table can be generated by NLLoc (Lomax et Therefore, we use the model HC with a grid size of 2 km al., 2000). We tested a 7-layer model of Haicheng (HC) for in PRESTo for LNNet. the Haicheng region (Figure 1) used by Zhao and Chen 3.3 Magnitude estimation equation (2017) to study the ground motion of the 1975 Haicheng earthquake. As the same of many other EEW platforms, PRESTo We tested the location algorithm using the two velocity estimates the magnitude of a detected event using the peak

Earthq Sci (2020)33: 281–292 285

Table 1 Events used for the implementation of PRESTo

PRESTo PRESTo Event No. Event date Long. (°E) Lat. (°N) Depth (km) M ΔLong. (°) ΔLat. (°) Use Depth (km) Mag.

1 2012-11-01 122.383 40.483 – 3.3 0.003 0.012 7.805 3.4 S

2 2013-01-10 123.500 39.500 10 3.9 0.021 0.056 6.727 3.7 R

3 2013-01-21 122.400 42.900 8 3.9 0.021 0.042 11.039 3.9 R

4 2013-01-23 123.217 41.483 7 5.1 0.021 0.003 2.234 5.1 B

5 2013-03-30 122.390 40.520 6 3.6 0.004 0.024 7.805 3.4 R

6 2013-04-22 122.400 42.900 6 5.3 0.020 0.024 11.039 5.2 R

7 2013-04-25 122.370 42.930 7 3.6 0.008 0.009 10.680 3.6 R

8 2013-05-10 122.340 42.930 7 3.7 0.050 0.070 9.781 3.7 R

9 2014-04-17 122.867 40.650 – 3.2 0.030 0.022 8.344 2.9 S

10 2014-04-28 122.300 40.467 – 3.4 0.024 0.016 6.367 3.3 S

11 2014-06-26 122.317 40.467 – 3.4 0.030 0.023 8.164 3.6 S

12 2014-07-10 121.117 39.317 – 3.1 0.173 0.187 6.100 2.9 S

13 2014-08-19 122.217 40.133 – 3.1 0.044 0.035 4.570 2.8 S

14 2014-08-22 122.317 40.467 6 3.8 0.018 0.044 11.938 3.8 B

15 2015-08-04 122.433 40.483 6 4.3 0.025 0.012 9.242 4.4 B

16 2015-08-19 122.400 40.517 – 3.1 0.053 0.035 2.953 2.8 S

17 2015-08-25 122.917 39.483 – 3.1 0.022 0.223 0.000 3.1 S

18 2015-11-23 122.450 40.817 9 4.0 0.014 0.015 7.805 4.3 B

19 2016-04-24 122.783 39.567 – 3.1 0.004 0.128 0.617 3.0 S

20 2016-05-22 122.100 41.630 6 4.6 0.008 0.015 24.516 4.3 R

21 2016-05-22 122.080 41.620 6 4.3 – – – – –

22 2016-07-03 122.633 40.700 – 3.4 0.022 0.045 0.000 3.1 S

23 2016-10-29 119.750 41.330 7 3.8 0.080 0.010 19.484 3.7 R

24 2017-01-17 121.367 41.367 – 3.2 0.038 0.029 12.117 2.8 S

25 2017-03-04 122.500 42.067 – 3.4 0.024 0.004 10.680 3.0 S

Note: The Liaoning regional earthquake catalog does not have event depth. Depths in this table for events of magnitude ML ≥ 3.5 are from the catalog of China Earthquake Networks Center (CENC, http://www.cenc.ac.cn/). The two catalogs have the same longitude and latitude for the same event. ΔLong. and ΔLat. are differences in event longitude and latitude, respectively, and are defined as the PRESTo values minus the catalog values. The last column gives how the event is used in PRESTo implementation in this study (S: simulation; R: regression for magnitude estimation equation; B: both). Events #20 and #21 are too close in time, and only event #20 can be processed by PRESTo. The differences between catalog and PRESTo in epicenters, depths and magnitudes are shown in Figure 5 ground displacement (PGD) in the first few seconds earthquakes in existing catalog. (usually 2–4 s) after the P-wave arrival. Once the value of In this study, we chose 13 earthquakes with magni- dpeak is determined at a station following the detection of tudes 3.5 < ML ≤ 6.0 (see Table 1, red squares and stars in an event, the magnitude M can be calculated by the region- Figure 1). We first use FilterPicker to automatically pick specific empirical equation (Wu and Zhao, 2006; Zollo et the P-wave arrivals from z-component records. Then the al., 2006; Lancieri and Zollo, 2008): records are removed of means and the signal-to-noise ( ) ratios (SNRs) are calculated for the records in the 2-s and log d = a + b ? M + c ? log(R) (1) peak 4-s windows following the picked P arrivals. The SNR of a where R is the hypocentral distance in kilometer. a, b and c record is defined as the ratio of the maximum amplitude are region-specific constants to be determined by regres- (norm of the vector sum of three components) in the 2-s or sion using the magnitudes and dpeak measurements for 4-s P-wave window to the root mean square (RMS) of the

286 Earthq Sci (2020)33: 281–292

record in the 10-s window before the picked P-wave Table 2 Crustal velocity model HC (Zhao and Chen, 2017) arrival. We only use records with SNR ≥ 5. Next, the tested in this study three-component records are converted to ground velocities by removing the magnification factor and Layer Depth to layer top (km) vP (km/s) vS (km/s) bandpass filtered to 0.075–25 Hz. After integration to 1 0.0 2.5 1.07 displacement, the dpeak is obtained by finding the 2 1.0 6.10 3.53 maximum displacement in the 2-s or 4-s time window after 3 15.0 6.20 3.59 P-arrival. We obtain a total of 303 dPeak values for the 2-s 4 20.0 6.10 3.53 window and 321 for the 4-s window. These dpeak values are used to determine the constants a, b and c that are specific 5 22.0 6.50 3.76 to the Liaoning region based on the ML reported in the 6 26.0 7.10 4.12 Liaoning regional earthquake catalog. These constants are 7 32.0 8.02 4.46 listed in Table 4 together with their uncertainties.

Table 3 Location results for the 23 January 2013 earthquake

Model Grid size (km) Origin time RMS (s) Long. (°E) εx (km) Lat. (°N) εy (km) Depth (km) εz (km)

HC 4 04:18:16.73 1.207 123.205 4.5 41.517 2 3.3 22.375 0.0

HC 2 04:18:15.39 0.486 123.196 1.4 41.486 3 0.9 2.234 1.2

HC 1 04:18:15.45 0.478 123.191 1.9 41.484 8 1.7 4.543 1.7

Note: εx, εy and εz are PRESTo’s location uncertainties in longitude, latitude and depth, respectively

Table 4 Constants in magnitude prediction for Italy and Liaoning

Window Length Region Component a εa b εb c εc

2 s Italy Z,N,E −7.69 0.06 1.00 0.00 −1.89 0.03

Liaoning Z,N,E −4.05 0.70 0.81 0.11 −1.32 0.25

4 s Italy Z,N,E −7.69 0.06 1.00 0.00 −1.89 0.03

Liaoning Z,N,E −4.53 0.62 0.90 0.09 −1.21 0.23

Note: εa, εb and εc are uncertainties of a, b and c, respectively.

In Figure 3, we display the dpeak-determined magnitu- using the USGS ShakeMap Small Regression (Wald et al., des of the 13 earthquakes of magnitudes 3.5 < M ≤ 6.0 by 1999) equation: L ( √ ) equation (1) using Liaoning-specific constants. The results 2 2 log(x) = b1 + b2 (M − 6) + b5 ? log R + H (2) demonstrate the effectiveness of the constants in Table 4 in determining the magnitudes of local earthquake in with an uncertainty σ1. In equation (2), M and R are the Liaoning from the dpeak values (average of 2 s and 4 s) event magnitude and epicentral distance in kilometer, using equation (1). It may be expected that the region- respectively, and x can be either PGV (in cm/s) or PGA 2 specific constants can be further improved to reduce the (cm/s ), with corresponding constants b1, b2, b5, H and σ1 uncertainties in the constants and magnitudes by using determined by regression using PGV or PGA data from dpeak data from more earthquakes. historical earthquakes. Table 5 lists the constants for Italy. For events of magnitude MPGD > 4.0, the GMPE used 3.4 Ground motion prediction equations in PRESTo is (Akkar and Bommer, 2007): = + + 2+ Following the detection and location of an event, and log(x) b1 b2 ? M b3 ?(M√ ) the magnitude estimation, PRESTo uses the ground motion 2 2 (3) (b4 + b5 ? M) ? log R + b prediction equations (GMPEs) to calculate the peak ground 6 acceleration (PGA) and peak ground velocity (PGV) with the uncertainty: √ distributions in the EEW coverage region. If the estimated 2 2 σ2 = (s1 + s1m ? M) + (s2 + s2m ? M) (4) magnitude MPGD ≤ 4.0, the PGV and PGA are calculated

Earthq Sci (2020)33: 281–292 287

5.5 GMPEs using the values in Tables 5 and 6 for Italy, which can be easily replaced once the regression constants are 5.0 available for Liaoning.

4.5 3.5 Miscellaneous parameters

PRESTo also has a number of miscellaneous parame-

PGD 4.0

M ters to control the detection, phase picking and event location processes. We run PRESTo in simulation mode 3.5 using archived records for 15 earthquakes in Table 1 to find the optimal values of the parameters for LNNet. The 3.0 values for the parameters are determined by trial-and-error process. The optimal values of the main parameters are 2.5 2.5 3.0 3.5 4.0 4.5 5.0 5.5 given in Table 7. M L Owing to the large region of Liaoning, the picking Figure 3 Performances of the magnitude prediction equation thresholds of LNNet is smaller than those of ISNet. Also, due to the lower station density of LNNet, the time for the 13 earthquakes of magnitudes 3.5 < ML ≤ 6.0 in Liaoning using different region-specific constants in Table 4. windows for binding are larger than those of ISNet. Horizontal and vertical axes are the catalog magnitude ML and magnitude MPGD calculated by PRESTo, respectively. Black 4 Discussion circles with error bars are obtained using Liaoning-specific constants. The diagonal solid line indicates MPGD = ML, and the two dashed lines show the range of 0.3 from the solid line In the process of determining the optimal values of the parameters in Table 7, we find that they have different

Table 5 Values of the regression constants in Equation (2) and influence on the detection and location of events. By associated uncertainties for Italy comparing the resulting RMS values of the predicted arrival times and uncertainties in event locations, we can x b1 b2 b3 H σ1 determine the optimal values of the parameters. Tables 8

PGV 2.223 0.740 −1.386 6.0 0.326 8 and 9 present an example for determining the parameters

PGA 4.037 0.572 −1.757 6.0 0.366 7 picker_filterWindow and picker_longTermWindow for the 23 January 2013 event. The results show clearly that when Again, x in equation (3) can either be PGV (in cm/s) or the values of these two parameters are too large, the PGA (cm/s2) with corresponding constants. The default uncertainties in event location increase. Thus, we choose values in PRESTo for the constants in equations (3) and the values of 0.5 s for picker_filterWindow and 7 s for picker_longTermWindow. (4) are given in Table 6. From the simulation results, we find that the parameter It should be noted that to fully establish the GPMEs for picker_threshold2 plays an important role in phase picking Liaoning region it is necessary to determine the regression and location. Empirically, the values of picker_threshold1 constants in equations (2–4) using sufficiently large and picker_threshold2 can be simply set equal, which we number of strong-motion records of various magnitudes denote as S. Figure 4 illustrates the phase picking and from a range of epicentral distances, especially for events event location results with different S values in the range with relatively large magnitudes. More observational of 6–10 s. Combining the results of mean RMS, mean lo- efforts are needed to accumulate records over a longer cation uncertainty and the mean number of phase picks, we period of time to obtain reliable GMPEs for Liaoning choose S=8 as the optimal picking thresholds for LNNet. region. It would be a significant undertaking and is beyond The parameter binder_stations_for_coincidence also the scope of this study. For the moment, we implement the needs to be considered carefully. If it is too small, the rate

Table 6 Values of the regression constants in Equations (3) and (4)

x b1 b2 b3 b4 b5 b6 s1 s1m s2 s2m PGV −1.36 1.06 −0.079 −2.95 0.31 5.55 0.85 −0.096 0.31 −0.040

PGA 1.65 0.77 −0.074 −3.16 0.32 7.68 0.56 −0.049 0.19 −0.017

288 Earthq Sci (2020)33: 281–292

Table 7 Optimal values of miscellaneous parameters in PRESTo for ISNet and LNNet

(a) Parameters for picking

Parameter name Description ISNet value LNNet value

picker_filterWindow filter window length, setting filter frequency band 0.5 s 0.5 s

long-term window length for calculating picker_longTermWindow 5.0 s 7.0 s average signal amplitude

threshold of characteristic function picker_threshold1 10.0 8.0 to trigger picking

threshold of integral of characteristic function picker_threshold2 10.0 8.0 to trigger picking

time after reaching threshold1 to check picker_tUpEvent 0.5 s 0.5 s if threshold2 is reached

(b) Parmaters for event binding and location

Parameter name Description ISNet value LNNet value

minimum number of triggered stations in coincidence time binder_stations_for_coincidence 6 4 window to declare an event

binder_secs_for_coincidence length of coincidence window 3 s 20 s

binder_secs_for_association length of window for all picks to belong to same event 10 s 50 s

binder_quakes_life length of time for earthquake parameters be refined 15 s 60 s

binder_quakes_separation minimum time interval of first picks of two earthquakes 30 s 200 s

binder_apparent_vel_stations_spacing average distance between stations 30 km 70 km

binder_apparent_vel_max_distance maximum distance between stations 120 km 700 km

locate_use_non_triggering_stations whether to use non-triggering stations to locate earthquakes 1 (true) 1

Table 8 Influence of picker_filterWindow on location uncertainty for the 23 January 2013 event

picker_filterWindow values (s) RMS (s) Long. (°E) εx (km) Lat. (°N) εy (km) Depth (km) εz (km)

0.001 0.523 123.185 1.6 41.493 5 1.5 0.617 2.2

0.01 0.523 123.185 1.6 41.493 5 1.5 0.617 2.2

0.05 0.474 123.196 1.6 41.486 3 1.1 2.234 1.3

0.1 0.473 123.196 1.6 41.486 3 1.1 2.234 1.4

0.2 0.491 123.196 1.5 41.486 3 1.1 2.234 1.5

0.3 0.491 123.196 1.2 41.486 3 0.8 2.234 1.7

0.4 0.486 123.196 1.5 41.486 3 1 2.234 1.4

0.5 0.486 123.196 1.5 41.486 3 1 2.234 1.4

1.0 0.486 123.196 1.5 41.486 3 1 2.234 1.4

10.0 0.486 123.196 1.5 41.486 3 1 2.234 1.4 of false alarm is high, whereas if it is too large, the lead regional average P-wave speed. Therefore, the related time (the time interval between first alert and S-wave parameters binder_secs_for_coincidence, binder_secs_ arrival at a target site, where the S-wave arrival is directly for_association, binder_quakes_life and binder_quakes_ read from the travel-time table) is too short. Thus, its separation are determined to be 20 s, 50 s, 60 s and 100 s, preferable value is between 3 and 5. The length of the respectively. location time windows are closely related to the average When the value of S (the value of parameters picker_ inter-station distance of the seismic network and the threshold1 and picker_threshold2) is low, more phases

Earthq Sci (2020)33: 281–292 289

Table 9 Influence of picker_ longTermWindow on location uncertainty for the 23 January 2013 event

picker_longTermWindow values (s) RMS (s) Long. (°E) εx (km) Lat. (°N) εy (km) Depth (km) εz (km)

1 0.492 123.196 1.4 41.486 3 1 2.953 2

2 0.484 123.196 1.6 41.486 3 1.3 2.234 1.7

3 0.484 123.196 1.6 41.486 3 1.3 2.234 1.6

4 0.484 123.196 1.5 41.486 3 1 2.234 1.4

5 0.484 123.196 1.5 41.486 3 1.2 2.234 1.6

7 0.486 123.196 1.5 41.486 3 1 2.234 1.4

10 0.486 123.196 1.2 41.486 3 0.8 2.234 2

7 25 24 (a) (b) (c)

23 6 20 22 5

21 15 4

phase 20 (km) N r ε RMS (s) 3 10 19

2 18 5 1 17

0 0 16 6 7 8 9 10 6 7 8 9 10 6 7 8 9 10 S S S Figure 4 The influence of the value of S (picker_threshold1 and picker_threshold2) on phase picking and location. (a) Variation of RMS of arrival picks with S. (b) Variation of location uncertainty εr (maximum of εx, εy and εz) with S. (c) Variation of the number of phase picks with S. Black, red and blue lines represent the average of all 15 events, events of lower magnitudes (ML < 3.5) and events of higher magnitudes (ML ≥ 4.0), respectively

with relatively low SNRs can be picked, which reduces the Table 10 Location uncertainties of several earthquakes with rate of missed picks, but at the same time increases the rate optimal value of S = 8

of false picks. Higher values of S have the opposite effect. Use non-triggering Only use triggering On the other hand, for small earthquakes with a large value stations stations for the parameter binder_apparent_vel_stations_spacing, if Event date εx εy εz εx εy εz the picking threshold S is large, the results become worse (km) (km) (km) (km) (km) (km) when non-triggering stations are used in location (i.e. 2012-11-01 3.4 2.7 1.8 3.4 3.2 3 locate_use_non_triggering_stations is set as 1). The reason for this is that P waves at some stations that should have 2013-01-10 2.4 3.3 2.7 2.4 3.5 2.9 been picked are not picked. However, while under the 2014-04-17 3.7 2.4 3.8 3.9 2.5 3.8 optimal value of S, using non-triggering stations gives 2015-08-19 4.2 6.6 2.8 5.1 7.1 4.0 better results, as shown in Table 10. 2016-04-24 2.1 3.6 1.5 6.3 7.1 4.5 In Table 1, we also compare the PRESTo location 2017-01-17 3.4 3.1 2.9 3.3 3 3.6 results for the 24 earthquakes of magnitudes M > 3.0 with

290 Earthq Sci (2020)33: 281–292

30 (a) Epicenter difference 25 Depth difference

20

15

10 Difference (km)

5

0 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 Event number

(b) 0.4

0.3

0.2

Magnitude difference 0.1

0 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 Event number Figure 5 (a) Differences between catalog and PRESTo epicenters (blue dots) and depths (red dots). The event numbers are same as in Table 1. (b) Difference between catalog and PRESTo magnitudes

those in the catalogs (See Figure 5 for their differences). 70 For most events, the discrepancies in latitudes and 60 longitudes between catalog and PRESTo locations are less 50 than 0.05º. Events with discrepancies larger than 0.05º are 40 30 located near the edge of the network (event #23 in Table 1) 20

with low magnitudes (event #12). This demonstrates the Lead time (s) 10 reliability of the event locations by PRESTo. 0 For events near the edge of the LNNet, stations far −10 from the epicenter may not be triggered, which will result −20 −50 0 50 100 150 200 250 300 350 in few triggered stations and large location errors. One Epicentral distance (km) way to solve this problem is to enlarge the target region by Figure 6 The variation of lead time with epicentral distance. including stations in neighboring provinces. The crosses are the simulation results of earthquakes with Another important performance factor of an EEW different epicentral distance to Shenyang. The dashed line is system is the time efficiency, which is reflected by the the linear fit with a slope of 0.2033 s/km and an intercept of length of the lead time mentioned earlier. The balance –5.2001 s between time efficiency and warning accuracy is around 100 km (generally SmS/S < 2). This affects the controlled by the parameter binder_stations_for_coinci- peak ground motion as described in Mori and Helmberger dence (see Table 7). Figure 6 shows the variation of lead (1996), which should be considered in establishing a time (binder_stations_for_coincidence is set to 4) with reliable GMPE. epicentral distance. We can see that the lead time at 100- km epicentral distance is ~15 s. More stations are needed to improve the time efficiency of the EEW system in 5 Conclusions LNNet. It is worth noting that because of the simple and stable In this study, we implement the EEW software crustal structure in Liaoning region, the amplitude of SmS PRESTo to Liaoning seismic network. Using archived wave is larger than that of S wave in the distance range seismic records of past earthquakes, we determin the

Earthq Sci (2020)33: 281–292 291 optimal values for LNNet using PRESTo. The velocity and teleseismic events. Bull Seismol Soc Amer 77: 1 437–1 445 model for Haicheng region with a grid size of 2 km has Espinosa-Aranda JM, Cuellar A, Garcia A, Ibarrola G, Islas R, been used to generate the travel-time table. A magnitude Maldonado S and Rodriguez FH (2009) Evolution of the estimation equation is established by regression which can Mexican Seismic Alert System (SASMEX). Seismol Res Lett be used to calculate the magnitudes of earthquakes in 80(5): 694–706 Liaoning region from the peak amplitudes in the initial 2-s Fleming K, Picozzi M, Milkereit C, Kühnlenz F, Lichtblau B, Fischer or 4-s of the P-wave signals. Other parameters used by J, Zulfikar C and Özel O (2009) The Self-organizing Seismic PRESTo are also determined by trial-and-error approach. Early Warning Information Network (SOSEWIN). Seismol Res We carefully investigat the influence of the picking Lett 80(5): 755–771 Hsiao N-C, Wu Y-M, Shin T-C, Zhao L and Teng T-L (2009) threshold S. The value for the picking threshold is slightly Development of earthquake early warning system in Taiwan. smaller for LNNet (S = 8) than for ISNet (S = 10), due to Geophys Res Lett 36(5): L00B02 LNNet’s larger station spacing than ISNet. The discre- Kamigaichi O, Saito M, DoiK, Matsumori T, Tsukada S, Takeda K, pancies between the location results by PRESTo and those Shimoyama T, Nakamura K, Kiyomoto M and Watanabe Y in the catalogs are mostly below 0.05º (~5 km). Finally, (2009) Earthquake early warning in Japan: Warning the general the lead time of PRESTo-based EEW system for Liaoning public and future prospects. Seismol Res Lett 80: 717–726 is approximately a linear function of the epicentral Kohler MD, Cochran ES, Given D, Guiwits S, Neuhauser D, Henson distance. I, Hartog R, Bodin P, Kress V, Thompson S, Felizardo C, Brody With the optimal parameters determined in this study, J, Bhadha R and Schwarz S (2018) Earthquake early warning the PRESTo system works well for Liaoning region, and ShakeAlert System: West Coast Wide Production Prototype. can be easily put into routine earthquake early warning Seismol Res Lett 89(1): 99–107 operation by linking it with the real-time data stream from Lancieri M and Zollo A (2008) A Bayesian approach to the real-time LNNet. The system can be improved by further parameter estimation of magnitude from the early P and S wave tuning using records of future earthquakes. displacement peaks. J Geophys Res 113: B12302 Lomax A, Satriano C and Vassallo M (2012) Automatic picker Acknowledgments developments and optimization: FilterPicker–a robust, broadband picker for real-time seismic monitoring and earthquake early warning. Seismol Res Lett 83(3): 531–540 The PRESTo software package can be downloaded at Lomax A, Virieux J, Volant P and Berge-Thierry C (2000) its official website http://www.prestoews.org/. The Probabilistic earthquake location in 3D and layered models. In: program NLLoc for generating the travel-time table can be Thurber CH and Rabinowitz N ed. Advances in Seismic Event downloaded at http://alomax.free.fr/nlloc/. We thank the Location. Netherlands, Springer, pp.101–134, doi: 10.1007/978- developers of the PRESTo platform for making their 94-015-9536-0_5 products available to the public. Data used in this study are Mori J and Helmberger D (1996) Large-amplitude moho reflections provided by Liaoning Earthquake Agency. All figures (SmS) from Landers aftershocks, Southern California. Bull except for Figure 2 are generated by the Generic Mapping Seismol Soc Amer 86(6): 1 845–1 852 Tools (Wessel and Smith, 1998). The authors acknowledge Peng CY, Chen Y, Chen QS, Yang JS, Wang HT, Zhu XY, Xu ZQ the course English Presentation for Geophysical Research and Zheng Y (2017) A new type of tri-axial accelerometers with of Peking University in improving the manuscript. high dynamic range MEMS for earthquake early warning. Comput Geosci 100: 179–187 References Peng CY, Jiang P, Chen QS, Ma Q and Yang JS (2019) Performance evaluation of a dense MEMS-based seismic sensor array Akkar S and Bommer JJ (2007) Empirical prediction equations for deployed in the Sichuan-Yunnan border region for earthquake peak ground velocity derived from strong-motion records from early warning. Micromachines 10(11): 735 Europe and the Middle East. Bull Seismol Soc Amer 97(2): Peng CY, Ma Q, Jiang P, Huang WH, Yang DK, Peng HS, Chen L, 511–530 and Yang JS (2020) Performance of a hybrid demonstration Allen MB, Macdonald DIM, Zhao X, Vincent SJ, Brouet-Menzies C earthquake early warning system in the Sichuan-Yunnan border (1997) Early Cenozoic two-phase extension and late Cenozoic region. Seismol Res Lett 91(2A): 835–846 thermal subsidence and inversion of the Bohai Basin, northern Peng CY and Yang JS (2019) Real-time estimation of potentially China. Mar Petrol Geol 14: 951–972 damaged zone for earthquake early warning based on thresholds Allen R (1982) Automatic phase pickers: Their present use and future of P-wave parameters. Acta Seismol Sin 41(3): 354–365 (in prospects. Bull Seismol Soc Amer 72: S225–S242 Chinese with English abstract) Baer M and Kradolfer U (1987) An automatic phase picker for local Peng CY, Yang JS, Chen Y, Zhu XY, Xu ZQ, Zheng Y and Jiang

292 Earthq Sci (2020)33: 281–292

XD (2015) Application of a threshold-based earthquake early Wald DJ, Quitoriano V, Heaton TH, Kanamori H, Scrivner CW and

warning method to the MW6.6 Lushan Earthquake, Sichuan, Worden CB (1999) TriNet "ShakeMaps": Rapid generation of China. Seismol Res Lett 86(3): 841–847 peak ground-motion and intensity maps for earthquakes in Peng CY, Zhu XY, Yang JS, Xue B and Chen Y (2013) Southern California. Earthq Spectra 15(3): 537–556 Development of an integrated onsite earthquake early warning Wang K, Chen Q-F, Sun S and Wang A (2006) Predicting the 1975 system and test deployment in Zhaotong, China. Comput Geosci Haicheng earthquake. Bull Seismol Soc Amer 96(3): 757–795 56: 170–177 Wessel P and Smith WHF (1998) New improved version of generic Picozzi M, Elia L, Pesaresi D, Zollo A, Mucciarelli M, Gosar A, mapping tools released. Eos Trans Am Geophys Union 79(47): Lenhardt W and Živčić M (2015) Trans-national earthquake early 579–579 Wu Y-M and Zhao L (2006) Magnitude estimation using the first warning (EEW) in north-eastern Italy, Slovenia and Austria: First three seconds P-wave amplitude in earthquake early warning. experience with PRESTo at the CE3RN network. Adv Geosci 40: Geophys Res Lett 33(16): L16312 51–61 Yin A (2010) Cenozoic tectonic evolution of Asia: A preliminary Pitilakis K, Karapetrou S, Bindi D, Manakou M, Petrovic B, synthesis. Tectonophysics 488: 293–325 Roumelioti Z, Boxberger T and Parolai S (2016) Structural Zhang HC, Jin X, Wei YX, Li J, Kang LC, Wang S, Huang L and Yu monitoring and earthquake early warning systems for the P (2016) An Earthquake Early Warning System in Fujian, China. AHEPA hospital in Thessaloniki. Bull Earthq Eng 14(9): 2 543– Bull Seismol Soc Amer 106(2): 755–765 2 563 Zhang PZ, Deng QD, Zhang GM, Ma J, Gan WJ, Min W, Mao FY, Satriano C, Lomax A and Zollo A (2008) Real-time evolutionary Wang Q (2003) Active tectonic blocks and strong earthquakes in earthquake location for seismic early warning. Bull Seismol Soc continental China. Sci China Ser D 46: 13–24 (in Chinese with Amer 98(3): 1 482–1 494 English abstract) Satriano C, Elia L, Martino C, Lancieri M, Zollo A and Iannaccone G Zhao HY and Chen XF (2017) Simulation of strong ground motion (2011) PRESTo, the earthquake early warning system for by the 1975 Haicheng MS7.3 earthquake. Chin J Geophys 60(7): Southern Italy: Concepts, capabilities and future perspectives. 2707–2 715 (in Chinese with English abstract) Soil Dyn Earthq Eng 31(2): 137–153 Zollo A, Lancieri M and Nielsen S (2006) Earthquake magnitude Su PZ, An XY, Li EL, Wang CW, Zhang XB and Zhao L (2020). estimation from peak amplitudes of very early seismic signals on Focal mechanisms of small and moderate earthquakes in strong motion records. Geophys Res Lett 33(23): L23312 Liaoning region. Chin J Geophys submitted (in Chinese) Zollo A, Iannaccone G, Lancieri M, Cantore L, Convertito V, Emolo Vassallo M, Satriano C and Lomax A (2012) Automatic picker A, Festa G, Gallovič F, Vassallo M, Martino C, Satriano C and developments and optimization: A strategy for improving the Gasparini P (2009) Earthquake early warning system in southern performances of automatic phase pickers. Seismol Res Lett 83: Italy: Methodologies and performance evaluation. Geophys Res 541–554 Lett 36(4): L00B07