earthquake alarm research paper

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Annual Review of Earth and Planetary Sciences

Volume 47, 2019, review article, earthquake early warning: advances, scientific challenges, and societal needs.

• Richard M. Allen 1 , and Diego Melgar 2
• View Affiliations Hide Affiliations Affiliations: 1 Department of Earth and Planetary Science, University of California, Berkeley, California 94720-4760, USA; email: [email protected] 2 Department of Earth Sciences, University of Oregon, Eugene, Oregon 97403-1272, USA; email: [email protected]
• Vol. 47:361-388 (Volume publication date May 2019) https://doi.org/10.1146/annurev-earth-053018-060457
• First published as a Review in Advance on January 30, 2019
• Copyright © 2019 by Annual Reviews. All rights reserved
• ▪  Earthquake early warning (EEW) is the rapid detection and characterization of earthquakes and delivery of an alert so that protective actions can be taken.
• ▪  EEW systems now provide public alerts in Mexico, Japan, South Korea, and Taiwan and alerts to select user groups in India, Turkey, Romania, and the United States.
• ▪  EEW methodologies fall into three categories, point source, finite fault, and ground motion models, and we review the advantages of each of these approaches.
• ▪  The wealth of information about EEW uses and user needs must be employed to focus future developments and improvements in EEW systems.

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ORIGINAL RESEARCH article

Feasibility study of an earthquake early warning system in eastern central italy.

• 1 Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, Ancona, Italy
• 2 Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, Roma, Italy

An earthquake early warning system (EEWS) is a monitoring infrastructure that allows alerting strategic points (targets) before the arrival of strong shaking waves during an earthquake. In a region like Central Italy, struck by recent and historical destructive earthquakes, the assessment of implementation of an EEWS is a significant challenge due to the proximity of seismic sources to many potential targets, such as historical towns, industrial plants, and hospitals. In order to understand the feasibility of an EEWS in such an area, we developed an original method of event declaration simulation (EDS), a tool for assessing the effectiveness of an EEWS for existing seismic networks, improving them with new stations, and designing new networks for EEW applications. Values of the time first alert (TFA), blind zone radius (BZ), and lead time (LT) have been estimated with respect to selected targets for different network configurations in the study region. Starting from virtual sources homogeneously arranged on regular mesh grids, the alert response was evaluated for actual and improved seismic networks operating in the area, taking into account the effects of the transmission and acquisition systems. In the procedure, the arrival times of the P wave picks, the association binder, the transmission latencies, and the computation times were used to simulate the configuration of PRESTo EEWS, simulating both real-time and playback elaborations of real earthquakes. The NLLOC software was used to estimate P and S arrival times, with a local velocity model also implemented in the PRESTo EEWS. Our results show that, although Italy’s main seismic sources are located close to urban areas, the lead times calculated with the EDS procedure, applied to actual and to improved seismic networks, encourage the implementation of EEWS in the study area. Considering actual delays due to data transmission and computation time, lead times of 5–10 s were obtained simulating real historical events striking some important targets of the region. We conclude that EEWSs are useful tools that can contribute to protecting people from the harmful effects of earthquakes in Italy.

1 Introduction

In the past twenty years, EEWSs have been implemented in different regions of the world and are considered a useful tool to reduce seismic risk ( Satriano et al., 2011b ). EEWSs were developed with different approaches, methodologies, and combining new experiences. At present, many countries have operational or prototype EEWSs. Allen et al. (2009b) described the status of EEW in the world and the principal operating systems at that time. Other examples include EEWS in Japan ( Odaka et al., 2003 ), Taiwan ( Wu and Teng, 2002 ; Hsiao et al., 2009 ), Mexico ( Suarez et al., 2009 ), Turkey ( Erdik et al., 2003 ; Alcik et al., 2009 ), and Romania ( Böse et al., 2007 ). The principal active systems are based on the software ElarmS ( Allen and Kanamori, 2003 ; Allen et al., 2009a ) and ShakeAlert ( Kohler et al., 2020 ) in California, on Virtual Seismologist in California and Switzerland ( Cua et al., 2009 ), in Europe ( Clinton et al., 2016 ), and in particular PRESTo in Italy ( Iannaccone et al., 2010 ; Satriano et al., 2011a ).

Major developments have led to two main types of systems: a regional alert system and an on-site system ( Satriano et al., 2011b ; Zollo et al., 2014 ). The regional system, based on the use of a regional network that records seismic events, aims to detect, locate, and determine the magnitude of an event starting from the analysis of a few seconds of the first arrivals of the P waves recorded at the stations ( Picozzi, 2012 ). The on-site system consists of a single sensor or more sensors near or inside the target structure to be alerted. In this system the P-wave recordings to the sensor are used to predict the peak ground motion at the site ( Colombelli et al., 2015 ). This approach could be considered useful for sites located within the BZ of a regional EEW system, allowing for a useful warning before the arrival of strong shaking waves. Caruso et al. (2017) proposed a P-wave-based EEW approach called on-site alert level (SAVE). Many studies combined the two EEW approaches ( Zollo et al., 2010 ; Colombelli et al., 2012a ); these systems combine local parameters and predicted ground motions at a regional scale to provide reliable and rapid estimates of the seismic source and the expected damage zone ( Colombelli et al., 2015 ).

The approaches for regional EEW can be classified as the “point-source” (simply the source as a concentrated volume) or “finite fault” (a more sophisticated and realistic characterization of the source, considering the entire fault area). Most studies have used the “point-source” demonstrating the reliability of this approach for the magnitude estimation of small to moderate events. However, it has been shown that this approach is not always accurate for strong earthquakes (magnitude> 6.5–7), due to the saturation of the P-wave parameters. Several authors (for example, Colombelli et al., 2012b ) estimated the magnitude over time windows longer than the recorded P-wave and/or the S-wave signal to obtain more accurate final values. These magnitude calculations are reliable at the cost of requiring more data and time ( Velazquez et al., 2020 ). In our study, the selected earthquakes have a moderate magnitude (≤ 6.5) and were considered as point sources.

Potentially, an EEWS can produce and transmit alert messages to different end-users to allow them to adopt several types of safety measures in a few seconds. The main benefits of an EEWS include public warning, first responder mobilization, and safety of health care and utility infrastructures, transit systems, and workplaces ( Allen and Melgar, 2019 ). Whereas in most cases evacuation of buildings is unrealistic, due to the short time available to act; a portion of the affected population can receive the alert and take safety measures in certain types of structures and infrastructures ( Iervolino et al., 2008 ).

Receiving an alert message increases personal situational awareness and yields a more rapid response, especially in well-trained people who can take precautionary and protective actions like “Drop-Cover-Hold on”, suspending delicate medical procedures, or slowing down a train ride. In shaking areas, a time of 10 s allows people to protect themselves and prepare for evacuation ( Fujinawa and Noda, 2013 ). A time interval of 5–7 s could be enough to trigger automatic mitigation actions ( Cauzzi et al., 2016 ) at power plants, energy sector grids, and utilities infrastructures to prevent explosions, combustions, loss of water, flooding, fatal collisions, and elevator interruptions. Social studies have demonstrated that receiving alert messages even a few seconds before the shaking occurs help people to prepare and react in the proper way ( Dunn et al., 2016 ; Becker et al., 2020a ).

The elongated shape of the Italian peninsula, combined with the small damage area for moderate, but often destructing Apenninic earthquakes (M6-7), determine small distances between sources and potential EEW targets. For this reason, in many cases the time to start safety actions may be too short. Therefore, an evaluation of the feasibility of an EEW implementation is needed in this area. A first theoretical evaluation was performed by Olivieri et al. (2008) with RSN (National Seismic Network, IV, INGV Seismological Data Center, 2006) and by Picozzi et al. (2015) using the RAN seismic network (Italian strong motion network) managed by the National Civil Protection ( Gorini et al., 2010 ), whose stations are mostly not connected in real-time.

Our study area extends for about 200 × 200 km in eastern central Italy and is characterized by the following two main seismic zones: 1) a NNW-SSE seismic zone elongated in the Appennines, where several moderate to strong earthquakes have occurred in the past and 2) a coastline-offshore seismic zone ( Figure 1a ), with less frequent and on average weaker seismicity. Figure 1b shows the target points chosen in the study compared to the individual and composite seismogenic sources from the DISS catalog ( DISS Working Group, 2018 ) . An individual seismogenic source (ISS) is a simplified, three-dimensional representation of a rectangular fault plane, whereas a composite seismogenic source (CSS) is a simplified, three-dimensional representation of a crustal fault containing an unspecified number of seismogenic sources that cannot be singled out. The area analyzed in our study is affected by different fault systems. We select as targets the cities with at least 40,000 inhabitants or with a significant cultural value. The selected cities are Ancona, Pesaro, Macerata, Ascoli Piceno, Fermo, Fabriano, Urbino, San Benedetto del Tronto, Civitanova Marche, Senigallia, Jesi, Perugia, Foligno, and Terni ( Table 1 ).

FIGURE 1 . Seismicity, seismic network, different fault zones, and selected targets of the study area. a) PSN20: Permanent Seismic Network in the year 2020. Blue triangles: velocimetric sensors. Purple triangles: FBA high performance accelerometer sensors. Pink triangles: MEMS accelerometer. Black squares: 1219–2019 EQ, moderate to strong earthquakes (M ≥ 5.5) extracted from CPTI15 database ( Rovida et al., 2021 ). Grey circles: earthquakes recorded by the seismic network from 2010 to 2020 in the magnitude range 2.5 ≤ M ≤ 5.4. b) Pentagon: Targets. ISS: individual seismogenic source. CSS: composite seismogenic source (see text for explanation, DISS Working Group (2018)) .

TABLE 1 . Target: city code. Place: extended name of cities.

In the study area, an EEWS based on PRESTo software has been operating since 2015. The system was based on permanent seismic networks managed by the INGV (National Institute of Geophysics and Vulcanology) composed by different sensors: velocimeters (short period and broad-band), high performance accelerometers, and MEMS. The seismic network in this area includes the RSN, a more dense local network (namely, the Alto Tiberina Near Fault Observatory—Taboo ( Chiaraluce et al., 2014 )), and some seismic stations installed for regional monitoring ( Cattaneo et al., 2017 ). A first evaluation of the performance of the EEWS was made analyzing the seismic sequence of 2016–2017 ( Festa et al., 2018 ). The system has been continuously operating over the years, without changing configuration, with some temporary interruptions.

In this work, the feasibility of a regional EEWS was evaluated by developing a procedure of event declaration simulation (EDS) for estimating the time useful to activate safety actions. The EDS procedure can be used for different applications: 1) to assess the feasibility of an EEWS in a specific area with an operating seismic network; 2) to plan the integration of new stations into an existing network; 3) to design a new network for an EEWS; and finally, 4) to assess the feasibility of an EEWS varying network density and trigger parameters. Regarding 3) and 4), the EDS can create virtual networks for the areas of interest and allow to plan investments and installations in advance.

The study was mainly based on the calculation of the time first alert (TFA)—the instant in which the event is declared starting from the coincidence of P phases at the stations, the blind zone radius (BZ)—the area in which no safety action can be carried out, and the lead time (LT)—the useful time to initiate safety actions on the targets. In the paper, we first propose a description of the method, of the parameters setting to obtain realistic simulations, and of the EDS validation with PRESTo EEWS. Then, an EDS application in eastern central Italy is showed, discussing the results of the TFA, BZ, and LT mapping in terms of feasibility and limits of the EEW implementation.

2 Event Declaration Simulation Method

The developed EDS is composed by a chain of subroutines including NonLinLoc modules (nonlinear location, or NLLoc; Lomax et al., 2009 ) in the preparatory phase and homemade python scripts in the core of the simulation that emulates some parameters similar to the PRESTo software. The NLLOC package is a well-known and widely used nonlinear inversion code, consisting of a set of programs and where it is possible to integrate an existing velocity model, travel-time calculation and probabilistic solution, for visualization of 3D volume data and location results ( http://alomax.free.fr/nlloc/ ).

The procedure of event declaration simulation needs the following inputs: arrival times of the P phases to the seismic stations, arrival time of the S phases to the targets, a binding configuration and latencies of the real-time data transmission vectors ( Figure 2 ). To obtain the arrival time of P and S phases, a velocity model, a seismic network, a seismic source, and target locations are required.

FIGURE 2 . Flow chart of the EDS method. Left (blue): P and S travel time calculation with NonLinLoc software modules ( Lomax et al., 2009 ). Middle (green): P and S arrival time relative at seismic stations and targets respectively. Right (red): input parameters and elements of the EDS core. Bottom : terms of the validation.

Figure 2 shows the most important steps of the simulation procedure, exemplifying the main blocks of the procedure from the inputs to the three outputs: TFA, BZ, and LT.

Starting from a velocity model and using the NLLoc Vel2Grid module, it is possible to create a defined grid of velocities, covering the volume of the study area. Then, the NLLoc Grid2Time module calculates the travel times from node points of the 3D velocity grid to the location of the seismic stations. The so obtained travel times are the same in use in the PRESTo system to locate the real seismic events.

The procedure estimates the arrival times of the P phases to the seismic stations and arrival times of the S phases to the targets, taking advantage of the NLLoc Time2EQ module, given the locations of seismic stations, seismic source and targets.

In the core of the simulation, the expected TFA, BZ, and LT are calculated starting from P and S arrival times, binder configuration (coincidences), and data latencies ( Figure 2 ).

The simulated parameters conceptually emulate some parameters of the PRESTo software.

“PICK time” is defined as the time of the P phase trigger at each seismic station, the sum of the estimated P phase arrival, the accumulation of 1 s of P waveform for the phase picker and the latency of the data packet. “LINK time” signs the moment when a certain number of PICKs are included in a relative small space and time interval and the binder declares an association. The simulation considers a “computation time” useful to locate the supposed event and the accumulation of 2 s of P waveform to compute the magnitude. The “computation time” and the time to compute the magnitude are inferred from real-time application of the PRESTo system implemented in the study area, collecting the log outputs relative to RTLOC and RTMAG modules ( Zollo et al., 2010 ; Satriano et al., 2011b ).

The integration of simulated parameters allows to calculate the “QUAKE time” (TFA) as the needed time to declare the event, summed to the “LINK time”. The BZ is estimated multiplying the TFA times the average of the S velocity in the travelled volume. Finally, the useful time (LT) to initiate actions to secure the targets is obtained from the difference between the arrival time of S phase at the targets and the TFA.

3 Event Declaration Simulation Configuration

The aim of this work is to assess the feasibility of an EEWS in eastern central Italy where a dense seismic network operates, and the main characteristics of the seismicity are well-known ( Figure 1a, b ). The study area is inhabited by about 2.5 million people, distributed in some main cities with population ranging from tens of thousands up to one hundred thousand inhabitants, and in several small historical villages where few hundred people live. Moreover, in most of the target towns and villages, both along the coasts and in the inner Umbria and Marche regions, residents increase dramatically during the summer and other vacation periods.

One of the starting points of the analysis is the velocity model of the volume crossed by seismic waves. A grid of 300 km × 300 km, 67 km thick, starting from 3 km above sea level was created, that could include all the seismicity of the region. A step of 1 km divides nodes of the grid and the central origin geographic point is 43.25 N – 13.00 E. To make the simulation more reliable, we chose a modified version of a 1D velocity model calculated for the region from an instrumental earthquakes catalog ( De Luca et al., 2009 ), preserving the Vp/Vs ratio equal to 1.85 and inserting a gradient between velocity layers ( Supplementary Table 1 ).

The configuration of the INGV seismic network has been evolving over the years. From 2015, when PRESTo software was installed for real-time monitoring and EEW testing, the number of stations has been increasing. Moreover, during the seismic sequence of the year 2016 ( Chiaraluce et al., 2017 ) an emergency temporary seismic network was installed to densify the permanent network ( Moretti et al., 2016 ; SISMIKO, 2020 ). Therefore, we set four seismic network configurations for EDS:

- PSN15: Permanent Seismic Network of the year 2015

- TSN16: Temporary Seismic Network of the 2016 seismic emergency (added to PSN15)

- PSN20: Permanent Seismic Network of the year 2020 (including PSN15)

- ASN20: Accelerometric Seismic Network of the year 2020.

The PSN15 is the same network configuration implemented in the real time PRESTo instance and contributed to validate EDS, to estimate data latencies and test performance of the PRESTo system in Festa et al. (2018) . TSN16 was used to test the response of the network with a significant increase of the density of the PSN15 up to a station inter-distance of about 5 km in the epicentral area of the 2016–2017 seismic sequence. PSN20 contributed to augment dataset of the EDS validation comparing results with a playback instance of the PRESTo software. The ASN20 helped to estimate the network response if we consider only accelerometric components, corresponding to a reduction of the network density, balanced by the certainty of unclipped records. A list of the seismic stations belonging to each network is inserted in Supplementary Table 2 .

The EDS is also able to manage arbitrary virtual networks, composed by scattered or equally spaced grid of stations to design network response in uncovered areas.

Also for the sources, we can input a single seismic source or a set of sources, scattered or equally spaced. The location of a single source, for example, is useful to reproduce the response of the seismic network in terms of TFA for an historical or a recent significant event. Furthermore, for the same event, the EDS returns LT relative to the main cities of the region. Extending the principle to a grid of equally spaced sources, the EDS can map the three output parameters (TFA, BZ, and LT) over the whole region. This approach allows classifying areas characterized by small or large TFA and BZ relative to the events’ epicenters. In this work, for a first mapping of TFA and BZ, we choose a grid of sources 5 km spaced at depth of 10 km. The LT was estimated for all the selected cities ( Table 1 ).

The number of triggered stations and the time interval for association are the main parameters for the set of the binder, the module that allows to declare an event. In the EDS, the binder configuration emulates the “Binder” of PRESTo software parameters ( PRESTo, 2013 ). We adopt the setup used in the real-time PRESTo instance for choosing the values of binder parameters in the EDS ( Table 2 ). The number of at least six stations in coincidence (STA_CO) inside a time window of 3 s (SEC_CO) is a good compromise between the heterogeneous density of the seismic network and the requirement of a rapid response of the system considered the distances of targets from the sources. The SEC_AS parameter is set to 10 s, a value that avoids the effect of the shift of a good location during grid search in the playback PRESTo instance with respect to the results of the real-time instance with SEC_AS = 15 s. The AVEL_MIN, AVEL_MAX, VEL_SPA, VEL_DIST parameters design a cone inside which the coincidence picks must fall.

TABLE 2 . Configuration Parameters of the EDS binder.

A key factor to take into account for an EEWS is the data latency due to the communication protocol ( Satriano et al., 2011a ). The latency affects the alert times of the EEWS which can only be activated when a good part of the data is available in nearly true real-time. The seismic data of the stations in eastern central Italy are transmitted by different types of transmission vectors: TCP/IP, WiFi, GPRS/UMTS, and Satellite (SAT-LIBRA and KA-SAT). At the time of the analysis, LTE routers were not available. Starting from the PRESTo log files, the real latency data for 86 seismic stations for the period 2015–2019 were collected. The long time window of the analysis allowed in some cases to assess the improved performance of the stations after the change of the transmission protocol. An average value of the latency for each type of transmission vectors was calculated by the geometric mean to exclude outliers, that is, values significantly out of the trend, referred to a malfunction of the station. The results of the latency classification are listed in Table 3 with the average values calculated and the number of stations used. Considering the main transmission vectors used in the network of this study, it is possible to make some considerations. As expected, the stations with a satellite time division multiple access carrier system (i.e., SAT-LIBRA) are those with the greatest latency and are not good for EEW application in small areas, but in our case few stations are equipped with this type of satellite transmission. Instead, another satellite system (i.e., KA-SAT) returns an average of 2.64 s, an acceptable value for EEW applications. The TCP/IP, WiFi, and GSM/UMTS latencies range between 1.86 and 2.83 s. The best value is for direct connection TCP/IP from remote stations. TCP/IP connections from other acquisition centers connected with a mixed copper-fiber line return a slight worsening. The transmission by WiFi backbone gives back a latency of 2.07 s, confirming suitable use for EEW systems. The WiFi system is not a public system, but a system dedicated to civil protection services, available thanks to the Regione Marche authority through an agreement with INGV. Each system used for data transmission (TCP/IP, WiFi, and GSM/UMTS, Satellite) could suffer temporary blackout of the communication lines. In case of a strong earthquake the redundancy of the transmission lines used in the network should reduce the risk of data blackout.

TABLE 3 . Transmission vector, mean and standard deviation of data latency, and number of stations used for each type.

Besides transmission times, another important parameter for an efficient EEWS is the computing time, defined as the difference between the association time of the stations and the computed TFA.

During this time, in the real-time system, the location and magnitude are estimated and the event is declared. The EDS does not simulate the earthquake location process and the magnitude estimate. So, the computing times inserted in our simulation are extracted from a statistics of the real-time PRESTo instance. The time difference between “QUAKE time” and “LINK time” was calculated and compared on a dataset of 91 events ( Supplementary Table 3 ) detected in real-time by the PRESTo system and re-simulated by EDS. The events, belonging to the INGV bulletin, were selected from August 2016 to May 2020, with magnitude 3.0 ≤ Mw ≤ 6.0. These events were detected by the system in real-time for the same period in the study area. The calculation times, taken from the system files in real-time, were distributed according to a lognormal curve ( Figure 3 ). The calculated values are mode equal to 0.29 s, median equal to 0.42 s, and average 0.51 s. Following these results, a value of 0.30 s was chosen as the average computation time for the simulation.

FIGURE 3 . Statistic of the computation time values.

4 Event Declaration Simulation Validation

The simulation procedure was validated comparing the first time of the alerts (TFA) of the EDS with first “QUAKE time” of the PRESTo instances. In the comparison with real time outputs of the PRESTo system, the values of mean latencies belonging to different transmission vectors are inserted in the EDS. Differently, for the comparison with PRESTo playback outputs, a zero latency was set in EDS. These two approaches help to increase the reliability of the validation, excluding that results are affected by a bad estimate of the latencies.

Figure 4 shows a detail of the processing of Mw 5.4 October 26, 2016 event and the comparison of arrival times relative to P phases of the triggered stations, the TFA and the arrival of S phases useful to calculate the LT at the target INGV_AN in the city of Ancona (epicentral distance 88 km). The black marks represent an operator review of the P phases at the triggered stations and the S phase at the target station (cT0). The black point represents the origin time of the located event, taken into account for the EDS test. The red marks are relative to PRESTo instances (cT1, cT3), while the blue marks are relative to EDS (cT2, cT4, cT5).

FIGURE 4 . Comparison of arrival times relative to Mw 5.4 October 26, 2016 earthquake. Circles: Origin Time. Diamonds: P phase arrival times of the triggered stations. Squares: TFA. Triangles: S phase arrival times at INGV_AN target in city of Ancona (88 km epicentral distance). cT0: arrival times of human event revision. cT1: PRESTo real time instance with PSN15. cT2: EDS with real latency and with PSN15. cT3: PRESTo playback instance with PSN15 and TSN16. cT4: EDS with zero latency and PSN15 + TSN16. cT5: EDS with real latency with PSN15 + TSN16.

The arrival times of operator reviewed P phases are lesser than the PRESTo and EDS real time tests where the data latencies are present. The reviewed arrival time is more similar for the tests in playback where zero latency is setting up but a 1 s of P waveform analysis remains.

The comparison of the TFA between PRESTo and EDS is very good for the two approaches, that is, real-time (cT1 vs cT2) and playback (cT3 vs cT4). The effect of the data latencies in the real time case imply a delay of about 3 s of the TFA with respect to the last P phase arrival time of the human reviewed case. This result is coherent with the values of the mean latencies calculated for the different transmission protocol ranging between 2 and 3 s inserted in the test, and 1 s needed to process the P waveform. The playback version of the test return a little early TFA respect to the last reviewed P phase, both for PRESTo and EDS, but the difference is small, 0.7 and 0.4 s respectively. Finally, the EDS allows to estimate a theoretical TFA adding emergency temporary seismic stations, active in epicentral area at the time of the earthquake, and stations data latencies (cT5). The effect on TFA is about 1 s of the advance respect the PRESTo real-time case (cT1), simply for the early achievement of the station coincidence determined by an augmented network density in the epicentral area. The values of S phase arrival times of PRESTo and EDS are coherent thanks to the same modeling of velocity volume used for travel time computation. From the good simulation of the TFA and S phase arrival at the target by EDS, a good estimate of the LT follows.

For a general validation of the TFA estimated with EDS, we selected 20 seismic events with 3.0 ≤ Mw ≤ 6.5, all elaborated also by PRESTo playback instance and 16 events by real-time instance. The selected events are scattered over the study area and over time, with the aim to insert in these tests different and unfavorable states of the network in terms of station inter-distances. The events belong to a time window from August 24, 2016, to January 28, 2020, with the hypocentral depth between 6.8 and 33.3 km ( Supplementary Table 4 ). The time window includes some major events of the 2016 Central Italy sequence, located in the southern sector of the region, while the other events were chosen in order to perform tests in the northern part of the region.

Table 4 summarizes the results of EDS estimates of TFA compared with real-time and playback PRESTo instances obtained by setting up the PSN15 configuration network. Moreover, the playback configuration (zero latency) was used to return a TFA mean difference, adding PSN16 emergency temporary station data relative to 2016–2017 events and the PSN20 network configuration for more recent events.

TABLE 4 . Results of the TFA comparison relative to real time and in playback PRESTo instances.

The TFA validation results return mean values lower than 1.0 s. The negative sign indicates an early TFA of the EDS, ascribable to non-simulation of the recursive recalculation of the location and magnitude that occurs in some real cases (PRESTo system) with the arrival of new data. The results of PSN15 real-time and playback cases (validation test 1 and 2) are similar, confirming a reliable simulation for both data latencies and station triggering, respectively. The last case (validation test 3) takes advantage of using 2016 temporary stations and latest installed stations, therefore the network density augmented in a part of analysis. The third test shows a better result, reducing the mean and the uncertainty of TFA difference.

The success of the EDS validation tests allows to perform simulations useful to quantify the EEW response of the actual seismic networks operating in eastern central Italy and to estimate the LT for the main cities in the region.

5 Event Declaration Simulation Applications, Results and Discussion

The EDS was developed to evaluate the EEW response of INGV seismic network in eastern central Italy in terms of TFA, BZ, and LD, but it is useful also to reproduce and design the feasibility of the system in different or not yet monitored areas.

The first possible application allowed by EDS is the response of a seismic network relative to one single seismic event with the aim of assessing LT for a set of targets.

Considering the hypocentral location of the Mw 6.0 August 24, 2016, event, we have simulated the response of four network configurations ( Table 5 ), assessing the LT for the main cities of the study area ( Figure 5 ). During the analysis, the calculation of the BZ is performed by multiplying the TFA by the average Vs estimated at the last station useful for coincidence. The TFA value is calculated as the arrival time of the P wave at the last station, added to the latency value and the estimated computing time of the system. Vs is calculated as the product of mean Vp at the last station for coincidence by the ratio Vs/Vp ( De Luca et al., 2009 , reported in Supplementary Table 1 ). In the first test (SST1), we used the PSN15 seismic network, the existing network at the time of the event. The second test (SST2) was performed inserting all stations of the TSN16, simulating the presence of the whole emergency network. This temporary seismic network was installed after the earthquake of August 24, 2016. The third test (SST3) represents the unfavorable case of a network consisting of only the current accelerometric INGV stations (ASN20). The last test (SST4) is an example of the design of a virtual arbitrary seismic network with the station inter-distance of 5 km. The calculated mean latency was associated to the PSN15 stations used for the latency statistics, a 2.75 s GSM/UMTS mean latency was associated to the TSN16 stations, and the mean latency was associated at new stations not present in PSN15, depending on their transmission vector. The virtual network with the inter-distance of 5 km was designed like a GSM/UMTS network and a mean latency of 2.75 s was applied to all virtual stations.

TABLE 5 . Results of tests with single source and different network configurations.

FIGURE 5 . EDS of the EEW response relative to a single source. Yellow star: location of the Mw 6.0 August 24, 2016 earthquake. Gray circle: Blind Zone. Pentagon: Targets. Legend: color map of the LT (s) linked to the targets. (A) SST1 test. Red triangles: PSN15 network. (B) SST2 test. Red triangles: PSN15 network; green triangles: TSN16. (C) SST3 test. Purple triangles: ASN20. (D) SST4 test. Blue triangles: 5 km inter-distance virtual network.

The network response in terms of TFA varies in dependence of the seismic station distribution and of network density around the epicentral area ( Figure 5 ). The addition of TSN16 to PSN15 around the seismic event causes a reduction of TFA from 8.7 to 6.3 s (gain of 2.4 s) and a reduction of the blind zone (BZ) radius from 26.5 to 18.3 km ( Table 5 ). In the third case, the sparse distribution of the accelerometric stations around the epicentral area causes a worsening of the network response with respect to the other two tests and TFA and BZ jump to 9.8 s and 31.4 km respectively. In the SST3 test, the MU_AP target has not safely time for activate protection actions, being on the BZ border, and MU_SB, MU_FX, and MU_TR targets have less than 10 s of LT ( Figure 5C and Table 6 ). The sample SST4 returns similar values of the SST2 test ( Table 5 and Table 6 ), strengthening the idea that the EDS can represent a tool for design an improvement of the EEWS starting from an existent seismic network or a new EEWS imaging an entirely new network in uncovered areas.

TABLE 6 . Results of LT for selected targets. Target: city code. Place: extended name of cities. LT: Values of LT in seconds referred to the test in Table 4 .

The EDS is also a tool to map the TFA, BZ, and LT over the territory. Indeed, it is possible to configure the procedure including a set of sources, every combined with a TFA and a BZ value. Moreover, it is possible to obtain LT linked to each epicentral location referred to a single target.

The example to explain the use of EDS to mapping EEW parameters is showed in Figure 6 , where the PSN20 is applied. In the left column, a set of locations referred to the main historical seismic events of Mw ≥ 5.5 from 1269 to 2017 AD that hit the region is presented ( Figure 6A ), extracted from CPTI15 catalog ( Rovida et al., 2021 ). In the right column, a grid of sources with inter-distance of 5 km, depth of 10 km and covering the whole region is depicted ( Figure 6B ). The example of historical events answers the question of which TFA and BZ would occur with the actual seismic network if seismic events repeat in the same locations. Each historical epicentral location is mapped in terms of TFA and BZ ( Figures 6C, E ). Smaller TFA (red points) are mapped where the network is denser in inland areas at the center of the network. For southern inland events and coastal events (yellow dots), the EEWS provides less protection which is significantly reduced for northern events (green dots) where TFA greater than 10 s results in BZ with a radius greater than 30 km.

FIGURE 6 . TFA and BZ mapping. (A) : historical events from CPTI15 v3.0 Mw ≥ 5.5 from1269 to 2017 AD, depth 10 km before 1997. (B) : virtual grid of sources distant of 5 km and depth of 10 km. (C) : mapping of TFA with historical events. (D) : mapping of TFA with virtual grid. (E) : mapping of BZ with historical events. (F) : mapping of BZ with virtual grid.

The same principle can be extended to the whole territory, mapping TFA ( Figure 6D ), and BZ ( Figure 6F ) with the grid of sources. The map of the TFA depicts the edges of the areas within which, if a seismic event occurs, the EEWS responds with a TFA threshold. In particular, for the eastern part of central Italy, with the actual INGV network, an EEWS could produce TFA less than 7.5 s for a large inland area that includes part of the most active seismic zones. Around the Adriatic coast, the seismic network is less dense and an earthquake that occurs off-shore is out of the network. Therefore, the coastal and off-shore TFAs are shorter than those in the inland zones. Besides, the elongated distribution of the seismic stations next to the coast could cause bad locations and estimate of the magnitude by the EEWS, worsening even more the protection provided by the alarm. Also, the northern part of the region is lacking stations and the resulting TFA are similar to the off-shore values. For the largest part of the region, a BZ ranging between 20 and 30 km from the epicenter would not be protected by a warning. The BZ radius increases up to over 30–40 km for the off-shore locations and the EEW system could not produce a TFA for coastal cities. To overcome this problem, offshore seismometers would be extremely useful.

The EDS allows exporting results of the TFA values to map LT referred to a single target. The LT map helps to link places of hypothetical epicenters and the time available for safety actions at the target. For example, the map in Figure 7 shows the LT available for the city of Fabriano (in particular for the location of the city Hall, MU_FB target). The color map shows the values of LT in equally spaced sources, located at 10 km depth. The black dots limit the epicenters for which the city of Fabriano falls in the BZ. Indeed, in this zone the LT is negative (≤0 s). The area marked with red dots corresponds to a 0 s < LT ≤ 5 s where an alarm could be provided but security actions are unlikely. In the orange area (5 s < LT ≤ 10 s) triggering automatic actions would be possible ( Cauzzi et al., 2016 ). Besides the orange area (LT > 10 s), trained people can take precautionary and protective actions ( Fujinawa and Noda, 2013 ) and the probability of a successful alert increases significantly ( Becker et al., 2020b ).

FIGURE 7 . LT mapping for city of Fabriano and historical destructive earthquake. Blue pentagon: place of city of Fabriano (MU_FB target). Colored circles: values of LT in seconds. Squares: CPTI15 v3.0 historical earthquakes and macroseismic intensity (DBMI15 v3.0, Locati et al., 2021 ) estimated for the city of Fabriano.

This argument makes sense in particular for those earthquakes that could damage the target. Therefore, the destructive earthquakes for the city of Fabriano, extracted from CPTI15 catalog ( Rovida et al., 2021 ), overlap the LT map in Figure 7 . Clearly, most of the events fall in black and red areas, since the MU_FB target is placed close to the inland active seismic zone ( Figure 1 ) and the EEWS would be almost useless. However, some historical events that caused damages with a macroseismic intensity from five to six to 7, fall in the orange area where automatic actions are possible. For the southernmost event (Mw 6.5 on October 30, 2016), an EEWS could have provided an alert 10 s before the arrival of the first S seismic waves to the city of Fabriano, a good LT to take several safety actions.

6 Conclusion

The feasibility of an EEWS derives from the design of a seismic network with respect to the seismic sources located within or around it. EEWSs are today an effective contribution to the problem of seismic risk mitigation, but few countries have operational systems ( Satriano et al., 2011b ). Implementation of EEWSs in Italy is a challenge. Italy is an elongated peninsula, with its central part crossed by a mountain chain that is seismically active and runs very close to urbanized areas. Therefore, the simulations of seismic networks contribute in the field of territorial safety by identifying the time in which some types of protection actions could be activated.

In this work, we have developed a simulation procedure (EDS), useful to estimate the feasibility of an EEWS and showed some applications in eastern central Italy, where INGV manages a dense seismic network. A validation process was performed by comparing results of simulations with real-time and playback instances of the EEW PRESTo system implemented in the same region ( Festa et al., 2018 ).

EDS is a tool to simulate different seismic network responses reproducing the physical contest of a specific historical or recent earthquake; moreover, EDS is a tool to map EEW parameters (TFA, BZ, and LT) to classify the whole territory in terms of areas where it is possible to activate safety actions. With EDS, it is possible to model real seismic networks inserting actual or theoretical data latencies due to different transmission vectors and to design new seismic networks in uncovered areas.

As expected, the results of the EDS application in eastern central Italy highlights short, but still useful, alert times for innermost land and coastal areas overlooking the active seismic zones. However, we have shown that most of the currently used transmission vectors have latencies between 2 and 3 s, an acceptable value for seismic early warnings. The quantitative estimate of TFA, BZ, and LT supplies useful information to project an improvement of the EEWS with the aim to reduce as much as possible the TFA. Modern approaches include the use of low-cost sensors for a greater diffusion of the seismic monitoring throughout the territory, and the development of networks oriented toward smart cities using fast protocols and connectivity ( Ladina et al., 2016 ; Pierleoni et al., 2018 ; D’Alessandro et al., 2019 ). In the area of the 2016 Central Italy sequence, we have estimated a gain of about 2.5 s of LT, adding the 2016 emergency temporary stations (station inter-distance about 5 km) over the permanent network, with a data latency similar to GPRS connections. The result was repeated setting a virtual network in a station grid with a constant inter-distance of 5 km and a mean data latency of 2.6 s to demonstrate the ability to project an EEWS in uncovered areas and to obtain realistic times of the first alert.

Although the Italian territory is mostly characterized by seismic sources next to urbanized areas, the lead times calculated with EDS procedure encourage the opportunity of implementing an EEWS for several interesting targets. Simulating historical events, a LT between 5 and 10 s was reproduced for some of these targets. We conclude that automatic safety actions, situational awareness and a more rapid response by well-trained people are a realistic goal in eastern central Italy.

We are aware that the study presented here has some limitations, first of all the system performance is evaluated analyzing mainly the rapidity of an EWS, whereas no evaluation assessment is made on the reliability of the earthquake impact prediction. This implies that the evaluation of the shaking could be inaccurate or even lead to false or missed alerts. However, the goal of our analysis was to determine whether an EWS could be a viable solution to reduce seismic exposure in certain regions of Italy, and in which conditions (network geometry, transmission times, etc.). Our results are encouraging provided that some technological issues are considered and that people’s awareness of seismic risk is increased. Further studies will be dedicated to a more thorough assessment of EWS.

The development of a widespread monitoring infrastructure near the main seismic sources, the massive training of citizens, and the collaboration with civil protection authorities could improve the scenarios simulated in this work, making an EEWS really effective in protecting people from the harmful effects of earthquakes in Italy.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

Conceptualization; methodology; validation; data analysis and curation: CL, SM; data analysis supervision: AA, MC; writing-original draft preparation; writing-review and editing: CL, SM, AA, MC. All authors contributed to manuscript, read and approved the submitted version.

This work has been carried out within the Project ART-IT (Allerta Rapida Terremoti in Italia), funded by the Italian Ministry of University and Research (Progetto Premiale 2015, DM. 850/2017), and supported also by the Agreement between Civil Protection Department of the Regione Marche and INGV.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors thank Mario Locati for support with DBMI15 v2.0, Luca Elia for suggestion and explanations with PRESTo configuration, and Simona Colombelli for the suggestions about the details of the work.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2021.685751/full#supplementary-material

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Keywords: earthquakes, early warning, seismic networks, seismic risk reduction, simulation

Citation: Ladina C, Marzorati S, Amato A and Cattaneo M (2021) Feasibility Study of an Earthquake Early Warning System in Eastern Central Italy. Front. Earth Sci. 9:685751. doi: 10.3389/feart.2021.685751

Received: 25 March 2021; Accepted: 05 July 2021; Published: 20 August 2021.

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Copyright © 2021 Ladina, Marzorati, Amato and Cattaneo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Chiara Ladina, [email protected]

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Earthquake Monitoring and Early Warning Systems

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• William H. K. Lee 2 &
• Yih-Min Wu 3  

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Definition of the Subject

When a sudden rupture occurs in the Earth, elastic (seismic) waves are generated. When these waves reach the Earth's surface, we may feel themas a series of vibrations, which we call an earthquake. Seismology is derived from the Greek word \( { \sigma \varepsilon \iota \sigma \mu \acute{o}\varsigma } \) (seismos or earthquake) and \( { \lambda \acute{o} \gamma o\varsigma }\) (logos or discourse); thus, it is the science of earthquakes and related phenomena. Seismic waves can be generatednaturally by earthquakes or artificially by explosions or other means. We define earthquake monitoring as a branch of seismology, whichsystematically observes earthquakes with instruments over a long period of time.

Instrumental recordings of earthquakes have been made since the later part of the 19th century by seismographic stations and networks of varioussizes from local to global scales. The observed data have been used, for example, (1) to compute the source parameters of...

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Abbreviations

A  fault (q.v.) that has moved in historic (e. g., past 10,000 years) or recent geological time (e. g., past 500,000 years).

Waves which propagate through the interior of a body. For the Earth, there are two types of seismic body waves: (1) compressional or longitudinal ( P wave), and (2) shear or transverse ( S wave).

Waves which are recorded on a  seismogram (q.v.) after the passage of body waves (q.v.) and surface waves (q.v.). They are thought to be back‐scattered waves due to the Earth's inhomogeneities.

An earthquake monitoring system that is capable of issuing warning message after an earthquake occurred and before strong ground shaking begins.

Anomalous phenomenon preceding an earthquake.

A statement, in advance of the event, of the time, location, and magnitude (q.v.) of a future earthquake.

The point on the Earth's surface vertically above the hypocenter (q.v.).

Observations made at large distances from the hypocenter (q.v.), compared to the wave‐length and/or the source dimension.

A fracture or fracture zone in the Earth along which the two sides have been displaced relative to one another parallel to the fracture.

The relative displacement of points on opposite sides of a  fault (q.v.), measured on the fault surface.

A description of the orientation and sense of slip on the causative fault plane derived from analysis of seismic waves (q.v.).

Point in the Earth where the rupture of the rocks originates during an earthquake and seismic waves (q.v.) begin to radiate. Its position is usually determined from arrival times of seismic waves (q.v.) recorded by seismographs (q.v.).

Rating of the effects of earthquake vibrations at a specific place. Intensity can be estimated from instrumental measurements, however, it is formally a rating assigned by an observer of these effects using a descriptive scale. Intensity grades are commonly given in Roman numerals (in the case of the Modified Mercalli Intensity Scale, from I for “not perceptible” to XII for “total destruction”).

Quantity intended to measure the size of earthquake at its source, independent of the place of observation. Richter magnitude ( \( { M_\mathrm{L} } \) ) was originally defined in 1935 as the logarithm of the maximum amplitude of seismic waves in a seismogram written by a Wood–Anderson seismograph (corrected to) a distance of 100 km from the epicenter. Many types of magnitudes exist, such as body‐wave magnitude ( \( { m_\mathrm{b} } \) ), surface‐wave magnitude ( \( { M_\mathrm{S} } \) ), and moment magnitude ( \( { M_\mathrm{W} } \) ).

A symmetric second‐order tensor that characterizes an internal seismic point source completely. For a finite source, it represents a point source approximation and can be determined from the analysis of seismic waves (q.v.) whose wavelengths are much greater than the source dimensions.

A term for the area near the causative rupture of an earthquake, often taken as extending a distance from the rupture equal to its length. It is also used to specify a distance to a seismic source comparable or shorter than the wavelength concerned. In engineering applications, near‐field is often defined as the area within 25 km of the fault rupture.

A theory of global tectonics (q.v.) in which the Earth's lithosphere is divided into a number of essentially rigid plates. These plates are in relative motion, causing earthquakes and deformation along the plate boundaries and adjacent regions.

Available information on earthquake sources in a given region is combined with theoretical and empirical relations among earthquake magnitude (q.v.), distance from the source, and local site conditions to evaluate the exceedance probability of a certain ground motion parameter, such as the peak ground acceleration, at a given site during a prescribed time period.

Any physical phenomena associated with an earthquake (e. g., ground motion, ground failure, liquefaction, and tsunami) and their effects on land use, man‐made structure, and socio‐economic systems that have the potential to produce a loss.

The calculation of the seismic hazard (q.v.), expressed in probabilistic terms (See probabilistic seismic hazard analysis , q.v.). The result is usually displayed in a  seismic hazard map (q.v.).

A map showing contours of a specified ground‐motion parameter or response spectrum ordinate for a given probabilistic seismic hazard analysis (q.v.) or return period.

The magnitude of the component couple of the double couple that is the point force system equivalent to a  fault slip (q.v.) in an isotropic elastic body. It is equal to rigidity times the fault slip integrated over the fault plane. It can be estimated from the far‐field seismic spectrum at wave lengths much longer than the source size. It can also be estimated from the near‐field seismic, geologic and geodetic data. Also called “scalar seismic moment” to distinguish it from moment tensor (q.v.).

The risk to life and property from earthquakes.

A general term for waves generated by earthquakes or explosions. There are many types of seismic waves. The principle ones are body waves (q.v.), surface waves (q.v.), and coda waves (q.v.).

Instrument which detects and records ground motion (and especially vibrations due to earthquakes) along with timing information. It consists of a  seismometer (q.v.) a precise timing device, and a recording unit (often including telemetry).

Record of ground motions made by a  seismograph (q.v.).

Inertial sensor which responds to ground motions and produces a signal that can be recorded.

The parameters specified for an earthquake source depends on the assumed earthquake model. They are origin time, hypocenter (q.v.), magnitude (q.v.), focal mechanism (q.v.), and moment tensor (q.v.) for a point source model. They include fault geometry, rupture velocity, stress drop, slip distribution, etc. for a finite fault model.

Waves which propagate along the surface of a body or along a subsurface interface. For the Earth, there are two common types of seismic surface waves: Rayleigh waves and Love waves (both named after their discoverers).

Branch of Earth science which deals with the structure, evolution, and relative motion of the outer part of the Earth, the lithosphere. The lithosphere includes the Earth's crust and part of the Earth's upper mantle and averages about 100 km thick. See plate tectonics (q.v.).

An earthquake at an epicentral distance greater than about 20° or 2000 km from the place of observation.

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Acknowledgments

We thank John Evans, Fred Klein, Woody Savage, and Chris Stephens for reviewing the manuscript, their comments and suggestionsgreatly improved it. We are grateful to Lind Gee and Bob Hutt for information about the Global Seismographic Network (GSN) and for providinga high‐resolution graphic file of an up‐to‐date GSN station map.

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Appendix: A Progress Report on Rotational Seismology

Seismology is based primarily on the observation and modeling of three orthogonal components of translational ground motions. Although effects of rotational motions due to earthquakes have long been observed (e. g., [ 80 ]), Richter (see, p. 213 in [ 97 ]) stated that:

Perfectly general motion would also involve rotations about three perpendicular axes, and three more instruments for these. Theory indicates, and observation confirms, that such rotations are negligible .

However, Richter provided no references for this claim, and the available instruments at that time did not have the sensitivity to measure the very small rotation motions that the classical elasticity theory predicts.

Some theoretical seismologists (e. g., [ 4 , 5 ]) and earthquake engineers have argued for decades that the rotational part of ground motions should also be recorded. It is well known that standard seismometers and accelerometers are profoundly sensitive to rotations, particularly tilt, and therefore subject to rotation‐induced errors (see e. g., [ 39 , 40 , 41 , 93 ]). The paucity of instrumental observations of rotational ground motions is mainly the result of the fact that, until recently, the rotational sensors did not have sufficient resolution to measure small rotational motions due to earthquakes.

Measurement of rotational motions has implications for: (1) recovering the complete ground‐displacement history from seismometer recordings; (2) further constraining earthquake rupture properties; (3) extracting information about subsurface properties; and (4) providing additional ground motion information to engineers for seismic design.

In this Appendix, we will first briefly review elastic wave propagation that is based on the linear elasticity theory of simple homogeneous materials under infinitesimal strain. This theory was developed mostly in the early nineteenth century: the differential equations of the linear elastic theory were first derived by Louis Navier in 1821, and Augustin Cauchy gave his formulation in 1822 that remains virtually unchanged to the present day [ 103 ]. From this theory, Simeon Poisson demonstrated in 1828 the existence of longitudinal and transverse elastic waves, and in 1885, Lord Rayleigh confirmed the existence of elastic surface waves. George Green put this theory on a physical basis by introducing the concept of strain energy, and, in 1837, derived the basic equations of elasticity from the principle of energy conservation. In 1897, Richard Oldham first identified these three types of waves in seismograms, and linear elasticity theory has been embedded in seismology ever since.

In the following we summarize recent progress in rotational seismology and the need to include measurements of rotational ground motions in earthquake monitoring. The monograph by Teisseyre et al. [ 109 ] provides a useful summary of rotational seismology.

Elastic Wave Propagation

The equations of motion for a homogeneous, isotropic, and initially unstressed elastic body may be obtained using the conservation principles of continuum mechanics (e. g., [ 30 ]) as

where θ is the dilatation, ρ is the density, u i is the ith component of the displacement vector \( { \vec{u} } \) , t is the time, and λ and μ are the elastic constants of the media. Eq. ( A1 ) may be rewritten in vector form as

If we differentiate both sides of Eq. ( A1 ) with respect to x i , sum over the three components, and bring ρ to the right‐hand side, we obtain

If we apply the curl operator ( \( { \nabla\times } \) ) to both sides of Eq. ( A3 ), and note that

Now Eqs. ( A4 ) and ( A6 ) are in the form of the classical wave equation

where Ψ is the wave potential, and  v is the wave‐propagation velocity (a pseudovector; wave slowness is a proper vector). Thus a dilatational disturbance θ (or a compressional wave) may be transmitted through a homogenous elastic body with a velocity \( { V_\mathrm{P} } \) where

according to Eq. ( A4 ), and a rotational disturbance \( { \nabla \times \vec{u} } \) (or a shear wave) may be transmitted with a wave velocity V S where

according to Eq. ( A6 ). In seismology, and for historical reasons, these two types of waves are called the primary ( P ) and the secondary ( S ) waves, respectively.

For a heterogeneous, isotropic, and elastic medium, the equation of motion is more complex than Eq. ( A3 ), and is given by Karal and Keller [ 65 ] as

Furthermore, the compressional wave motion is no longer purely longitudinal, and the shear wave motion is no longer purely transverse. A review of seismic wave propagation and imaging in complex media may be found in the entry by Igel et al.  Seismic Wave Propagation in Media with Complex Geometries, Simulation of .

A significant portion of seismological research is based on the solution of the elastic wave equations with the appropriate initial and boundary conditions. However, explicit and unique solutions are rare, except for a few simple problems. One approach is to transform the wave equation to the eikonal equation and seek solutions in terms of wave fronts and rays that are valid at high frequencies. Another approach is to develop through specific boundary conditions a solution in terms of normal modes [ 77 ]. Although ray theory is only an approximation [ 17 ], the classic work of Jeffreys and Bullen, and Gutenberg used it to determine Earth structure and locate earthquakes that occurred in the first half of the 20th century. It remains a principal tool used by seismologists even today. Impressive developments in normal mode and surface wave studies (in both theory and observation) started in the second half of the 20th century, leading to realistic quantification of earthquakes using moment tensor methodology [ 21 ].

Rotational Ground Motions

Rotations in ground motion and in structural responses have been deduced indirectly from accelerometer arrays, but such estimates are valid only for long wavelengths compared to the distances between sensors (e. g., [ 16 , 34 , 52 , 88 , 90 , 104 ]). The rotational components of ground motion have also been estimated theoretically using kinematic source models and linear elastodynamic theory of wave propagation in elastic solids [ 14 , 69 , 70 , 111 ].

In the past decade, rotational motions from teleseismic and small local earthquakes were also successfully recorded by sensitive rotational sensors, in Japan, Poland, Germany, New Zealand, and Taiwan (e. g., [ 53 , 55 , 56 , 105 , 106 , 107 , 108 ]). The observations in Japan and Taiwan show that the amplitudes of rotations can be one to two orders of magnitude greater than expected from the classical linear theory. Theoretical work has also suggested that, in granular materials or cracked continua, asymmetries of the stress and strain fields can create rotations in addition to those predicted by the classical elastodynamic theory for a perfect continuum ( Earthquake Source: Asymmetry and Rotation Effects ).

Because of lack of instrumentation, rotational motions have not yet been recorded in the near‐field (within \( { \sim 25\,\mathrm{km} } \) of fault ruptures) of strong earthquakes (magnitude \( { > 6.5 } \) ), where the discrepancy between observations and theoretical predictions may be the largest. Recording such ground motions will require extensive seismic instrumentation along some well‐chosen active faults and luck. To this end, several seismologists have been advocating such measurements, and a current deployment in southwestern Taiwan by its Central Weather Bureau is designed to “capture” a repeat of the 1906 Meishan earthquake (magnitude 7.1) with both translational and rotational instruments.

Rotations in structural response, and the contributions to the response from the rotational components of the ground motion, have also been of interest for many decades (e. g., [ 78 , 87 , 98 ]. Recent reviews on rotational motions in seismology and on the effects of the rotational components of ground motion on structures can be found, for examples, in Cochard et al. [ 18 ] and Pillet and Virieux [ 93 ], and Trifunac [ 112 ], respectively.

Growing Interest – The IWGoRS

Various factors have led to spontaneous organization within the scientific and engineering communities interested in rotational motions. Such factors include: the growing number of successful direct measurements of rotational ground motions (e. g., by ring laser gyros, fiber optic gyros, and sensors based on electro‐chemical technology); increasing awareness about the usefulness of the information they provide (e. g., in constraining the earthquake rupture properties, extracting information about subsurface properties, and about deformation of structures during seismic and other excitation); and a greater appreciation for the limitations on information that can be extracted from the translational sensors due to their sensitivity to rotational motions e. g., computation of permanent displacements from accelerograms (e. g., [ 13 , 39 , 40 , 41 , 93 , 113 ]).

A small workshop on Rotational Seismology was organized by W.H.K. Lee, K. Hudnut, and J.R. Evans of the USGS on 16 February 2006 in response to grassroots interest. It was held at the USGS offices in Menlo Park and in Pasadena, California, with about 30 participants from about a dozen institutions participating via teleconferencing and telephone [ 27 ]. This event led to the formation of the International Working Group on Rotational Seismology in 2006, inaugurated at a luncheon during the AGU 2006 Fall Meeting in San Francisco.

The International Working Group on Rotational Seismology (IWGoRS) aims to promote investigations of rotational motions and their implications, and the sharing of experience, data, software and results in an open web‐based environment ( http://www.rotational-seismology.org ). It consists of volunteers and has no official status. H. Igel and W.H.K. Lee currently serve as “co‐organizers”. Its charter is accessible on the IWGoRS web site. The Working Group has a number of active members leading task groups that focus on the organization of workshops and scientific projects, including: testing and verifying rotational sensors, broadband observations with ring laser systems, and developing a field laboratory for rotational motions. The IWGoRS web site also contains the presentations and posters from related meetings, and eventually will provide access to rotational data from many sources.

The IWGoRS organized a special session on Rotational Motions in Seismology , convened by H. Igel, W.H.K. Lee, and M. Todorovska during the 2006 AGU Fall Meeting [ 76 ]. The goal of that session was to discuss rotational sensors, observations, modeling, theoretical aspects, and potential applications of rotational ground motions. A total of 21 papers were submitted for this session, and over 100 individuals attended the oral session.

The large attendance at this session reflected common interests in rotational motions from a wide range of geophysical disciplines, including strong‐motion seismology, exploration geophysics, broadband seismology, earthquake engineering, earthquake physics, seismic instrumentation, seismic hazards, geodesy, and astrophysics, thus confirming the timeliness of IWGoRS. It became apparent that to establish an effective international collaboration within the IWGoRS, a larger workshop was needed to allow sufficient time to discuss the many issues of interest, and to draft research plans for rotational seismology and engineering applications.

First International Workshop

The First International Workshop on Rotational Seismology and Engineering Applications was held in Menlo Park, California, on 18–19 September 2007. This workshop was hosted by the US Geological Survey (USGS), which recognized this topic as a new research frontier for enabling a better understanding of the earthquake process and for the reduction of seismic hazards. The technical program consisted of three presentation sessions: plenary (4 papers) and oral (6 papers) held during the first day, and poster (30 papers) held during the morning of the second day. A post‐workshop session was held on the morning of September 20, in which scientists of the Laser Interferometer Gravitational‐wave Observatory (LIGO) presented their work on seismic isolation of their ultra‐high precision facility, which requires very accurate recording of translational and rotational components of ground motions (3 papers). Proceedings of this Workshop were released in Lee et al. [ 75 ] with a DVD disc that contains all the presentation files and supplementary information.

One afternoon of the workshop was devoted to in‐depth discussions on the key outstanding issues and future directions. The participants could join one of five panels on the following topics: (1) theoretical studies of rotational motions (chaired by L. Knopoff), (2) measuring far‐field rotational motions (chaired by H. Igel), (3) measuring near‐field rotational motions (chaired by T.L. Teng), (4) engineering applications of rotational motions (chaired by M.D. Trifunac), and (5) instrument design and testing (chaired by J.R. Evans). The panel reports on key issues and unsolved problems, and on research strategies and plans, can be found in Appendices 2.1 through 2.5 in Lee et al. [ 75 ]. Following the in‐depth group discussions, the panel chairs reported on the group discussions in a common session, with further discussions among all the participants.

Discussions

Since rotational ground motions may play a significant role in the near‐field of earthquakes, rotational seismology has emerged as a new frontier of research. During the Workshop discussions, L. Knopoff asked: Is there a quadratic rotation‐energy relation, in the spirit of Green's strain‐energy relation, coupled to it or independent of it? Can we write a rotation‐torque formula analogous to Hooke's law for linear elasticity in the form

where ω kl is the rotation,

L ij is the torque density; and d ijkl are the coefficients of rotational elasticity? How are the d's related to the usual c's of elasticity? If we define the rotation vector as

where the torque density is \( { \nabla \times \vec{f} } \) , \( { \vec{f} } \) is the body force density, and ρ is density of the medium. This shows that rotational waves propagate with  S ‑wave velocity and that it may be possible to store torques. Eq. ( 15 ) is essentially an extension using the classical elasticity theory.

Lakes [ 67 ] pointed out that the behavior of solids can be represented by a variety of continuum theories. In particular, the elasticity theory of the Cosserat brothers [ 19 ] incorporates (1) a local rotation of points as well as the translation motion assumed in the classical theory, and (2) a couple stress (a torque per unit area) as well as the force stress (force per unit area). In the constitutive equation for the classical elasticity theory, there are two independent elastic constants, whereas for the Cosserat elastic theory there are six. Lakes (personal communication, 2007) advocates that there is substantial potential for using generalized continuum theories in geo‐mechanics, and any theory must have a strong link with experiment (to determine the constants in the constitutive equation) and with physical reality.

Indeed some steps towards better understandings of rotational motions have taken place. For example, Twiss et al. [ 114 ] argued that brittle deformation of the Earth's crust ( Brittle Tectonics: A Non-linear Dynamical System ) involving block rotations is comparable to the deformation of a granular material, with fault blocks acting like the grains. They realized the inadequacy of classical continuum mechanics and applied the Cosserat or micropolar continuum theory to take into account two separate scales of motions: macro‐motion (large‐scale average motion composed of macrostrain rate and macrospin), and micro‐motion (local motion composed of microspin). A theoretical link is then established between the kinematics of crustal deformation involving block rotations and the effects on the seismic moment tensor and focal mechanism solutions.

Recognizing that rotational seismology is an emerging field, the Bulletin of Seismological Society of America will be publishing in 2009 a special issue under the guest editorship of W.H.K. Lee, M. Çelebi, M.I. Todorovska, and H. Igel.

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Lee, W.H.K., Wu, YM. (2009). Earthquake Monitoring and Early Warning Systems. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_152

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BLESeis: Low-Cost IoT Sensor for Smart Earthquake Detection and Notification

Jongbin won.

1 Department of Civil and Environmental Engineering, Chung-Ang University, Dongjak, Seoul 06974, Korea; rk.ca.uac@1271cas (J.W.); rk.ca.uac@1545yjp (J.P.)

Junyoung Park

Jong-woong park.

2 Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea

The Internet of Things (IoT) has been implemented to provide solutions for certain event detection because of ease of installation, computing and communication capability, and cost-effectiveness. Seismic event detection, however, is still a challenge due to a lack of high-fidelity sensing and classification efficiency. This study proposes BLESeis, an IoT sensor for smart earthquake detection. BLESeis comprises three main parts: (1) high-fidelity vibration sensing using a MEMS accelerometer and digital filtering; (2) an embedded earthquake detection algorithm; (3) BLE (Bluetooth low energy) beacon for earthquake notification. For high-fidelity vibration sensing, a triggering algorithm and embedded finite impulse response (FIR) low-pass filter are developed. The acquired vibration is then classified by the earthquake detection algorithm developed to identify the earthquake signal from other vibration sources using time and frequency domain analysis. Upon detection of an earthquake, the BLE beacon broadcasts using the proposed data packet for efficient notification and visualization. The performance of the proposed system is evaluated through numerical simulations and a set of experiments using shaking table tests. The experiments show the feasibility of the low-cost earthquake detection and notification system.

1. Introduction

Earthquakes can threaten the lives of thousands of people in densely populated regions resulting in substantial financial loss. Earthquake early-warning (EEW) systems serve to detect the magnitude of an earthquake rapidly and alert many people to take protective actions such as covering and stopping trains [ 1 , 2 ]. Several studies have been conducted to enable real-time or early earthquake detection and warning systems [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. For instance, Nakamura et al. [ 3 ] proposed an urgent earthquake detection and alarm system (UrEDAS) that uses not only maximum seismic motion but also an initial stage of the seismic motion to enable the early-stage detection. Although these studies proposed real-time and high-accuracy earthquake detection methods, numerous data would need to be collected from the seismic network with highly sensitive seismometers spread over the region. Therefore, current EEW systems based on traditional seismic and geodetic networks exist only in a few countries due to the high cost of installing and maintaining such systems.

Recently, the EEW system has been evolved with the rise in the Internet of Things (IoT) system driven by the convergence of technologies such as micro electro-mechanical systems (MEMS), wireless communication, and increased computing power [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. At the early stages, the MEMS sensors were connected to PCs and comprised a seismic sensor network by publishing the data on the Internet. For example, Quake-Catcher Network (QCN) [ 17 ], Community Seismic Network (CSN) [ 18 ], and NetQuakes [ 19 ] were projects led by University of Stanford, the California Institute of Technology, and the United States Geological Survey (USGS) to detect earthquakes using low-cost MEMS sensors. Recently, EEW has been built for smartphones that have MEMS sensors, data processing, and communication capabilities. The iShake project [ 20 ] used an iPhone for measuring ground motion, intensity parameters and provided the first proof of principle that showed the ability of smartphones for earthquake motion capture. The MyShake [ 21 ], built on iShake, is a smartphone application that detects whether the movement of a phone is made by an earthquake or by other human activities and have the ability to recognize earthquake shaking from background noise by aggregating multiple recording of every smartphone. When a potential earthquake event is detected, preprocessed measurement data is sent rapidly to the centralized processing hub for accurate data processing and aggregation with other sensor data. However, as the current EEW is network-wise alert system using sensing clients and backend servers, residential or industrial areas that need customized alert, even without internet connectivity, may not benefit from the EEW.

In helping to resolve these issues, in this paper, a low-cost IoT sensor for earthquake detection, BLESeis was developed. BLESeis, built with BLE (Bluetooth low energy) technology, measures high fidelity vibration, processes seismic event data, and broadcasts results such as PGA (peak ground acceleration), and MMI-scale. Contributions of this paper are enumerated as follows: (1) BLESeis was designed with a MEMS accelerometer and a BLE integrated MCU (NRF52840BLE, Nordic) as a stand-alone seismic sensor that conducts data measurement, processing, and notification on board; (2) embedded software was developed to establish high-fidelity data measurement, seismic event detection; (3) custom BLE data packet was proposed for seismic event notification. The performance of the developed BLESeis was evaluated through a lab-scale test using a shaking table. The remainder of this paper is organized as follows. Section 2 proposes a developed hardware and software system for BLESeis, including data acquisition, and FIR filtering for low-pass filtering. Numerical and lab-scale experimental validation are presented in Section 3 and Section 4 , respectively. The conclusion is given in Section 5 .

2. Proposed BLESeis

BLESeis is designed to deliver a reliable and fast customized alert to nearby Bluetooth devices. The hardware of the BLESeis consists of a high sensitivity MEMS accelerometer and BLE-integrated MCU. The software framework is divided into three main tasks: (1) sensing task, (2) seismic event detection task, and (3) notification task (see Figure 1 ).

Hardware and software framework.

2.1. Hardware Configuration

The IoT sensor platform used in the BLESeis is the Adafruit nRF52840 Feather Express (Adafruit industries, New York city, U.S.) that was developed based on nRF52840 (Nordic, 2019) with Arduino IDE support (see Figure 2 ); the nRF52840 is built with 32-bit ARM ® Cortex™-M4 CPU running at 64 MHz, allowing fast and efficient computation of complex functions requiring floating-point math for signal processing. nRf52840 provides extensive memory availability of 1MB and 256kB respectively in flash and RAM, enabling high-frequency sampling and computationally intense data processing required for seismic data acquisition and processing. Another salient feature of nRF52840 is the integration of Bluetooth 5 that enables low-power and long-range communication.

Developed BLESeis.

For high fidelity seismic data acquisition, the BLESeis uses a digital MEMS accelerometer, LIS3DHH, which features a high sampling of 1100 Hz and a low noise density of 45   μ g / H z . Note that the noise density of LIS3DHH corresponds to the RMS level of 0.2 mg for 20 Hz bandwidth, which will be implemented in the sensing process. The detailed specification of the LIS3DHH is summarized in Table 1 .

Specification of BLESeis.

2.2. Sensing Task

The sensing task consists of STA (short-term average)/LTA (long-term average) trigger [ 24 ], low-pass filtering, and decimation (see Figure 3 ). When the STA/LTA trigger detects an event using the ratio of STA to LTA, the proposed BLESeis measures acceleration for 10 s. Afterward, measured data is low-pass filtered to remove unnecessary high-frequency noise and then downsampled to reduce the sampling rate by a factor of 10 for efficient memory use.

Flowchart of the sensing task.

The STA/LTA trigger is computationally efficient and the most widely used method for seismic vibration detection [ 25 ]. The STA measures the instantaneous amplitude of the newly input acceleration signal, while the LTA takes care of the current average of amplitude (see Figure 4 ). When the ratio of STA to LTA exceeds a threshold representing the event, the system is triggered.

STA/LTA trigger.

The implementation of the STA/LTA algorithm for proposed BLESeis follows the flowchart, as shown in Figure 5 . In the initialization step, acceleration is collected at a sampling frequency of 1100 Hz. STA and LTA are calculated based on predefined short-term and long-term window. Then, the ratio of STA to LTA is compared with the threshold; if the ratio of STA to LTA exceeds the preset threshold, the trigger is fired, otherwise moving window is applied to update LTA and STA. Note that in the implementation, each length of the predefined window for STA and LTA was 50 ms and 1000 ms, and the preset threshold was 1.3.

Implementation of the STA/LTA trigger.

Once the trigger is activated, the acceleration is measured for 10 s at a sampling rate of 1100 Hz. In order to avoid aliasing, FIR (finite impulse response) low-pass filter [ 26 ] is applied, attributed to its stability and linear phase delay and ease of implementation.

Liu et al. have proposed transfer function for the low-pass filter expressed as

where f is the frequency, β L is the regularization factor, and n is the filter order. The regularization factor β L can be represented as

where α L is the accuracy factor that indicates the magnitude of the transfer function at the cut-off frequency, f c . Note that f c can be located at the edge of the pass band as α L is close to 1. The filter coefficients for the implementation of the FIR filter can be approximated using inverse discrete Fourier transform as

where r c = f c / f s and denotes the ratio of the cut-off frequency to the sampling frequency, and f ˜ = f / f c is the normalized frequency. In this study, the property of the low-pass filter designed in with the cut-off frequency (i.e., f c ) at 20 Hz, α L of 0.99, and the filter order n of 7. The cut-off frequency is determined to remove unnecessary noise above 20 Hz to improve the accuracy of the earthquake detection. The length of the filter is truncated to 275 samples (i.e., 250 ms) for efficient filter implementation. Figure 6 shows the 275 filter coefficients and transfer function, which have the pass band region up to 20 Hz and the transient band up to 50 Hz. The filtered acceleration is downsampled by a factor of 10, to make a sampling rate of 110 Hz.

( a ) Filter coefficient and ( b ) frequency response function.

2.3. Seismic Event Detection Task

The seismic event detection task analyzes the acquired signal in the time and frequency domain to distinguish earthquake from type 1-stationary signal and type 2-non-stationary signal, including a few major frequency components (see Figure 7 ). Note that the proposed detection algorithm classifies signals into three types: type 1 (random vibration), type 2 (structural vibration), and type 3 (earthquake). The proposed detection algorithm first distinguishes the type 1 signal and potential earthquake in the time-domain analysis followed by frequency-domain analysis for classifying earthquakes from the nonstationary signal.

The flow of the seismic event detection task.

Let X ( i ) ,     i = 1 , … , 1100 be a sample from a measured signal for 10 s at 110 Hz, which was downsampled from 1100 Hz (i.e., 1100 data points). Then, four windowed segments of the length of N without overlap were taken. Let X k ( i ) ,     k = 1 , 2 , 3 , 4 be the segments of each windowed sample such that X k ( i ) = X ( i + ( k − 1 ) N ) , where i = 1 , … , N and N is 275 samples, which is a quarter of the total data size. The type 1 signals such as random and harmonic oscillations can be classified using the stationarity index of the signal defined as

where σ k is the standard deviation of each windowed signal X k , and τ r a n d o m is the threshold for type 1 signal classification. In this study, we set τ r a n d o m as 0.75, considering measurement noise.

After the time-domain classification, the frequency-domain analysis was conducted to detect the earthquake signal. The PSD (power spectral density) of the measured signal S x x ( i )     ( 1 ≤ i ≤ 512 ) was calculated with the number of Discrete Fourier transforms of 1024, such that the frequency spectral resolution has 0.01 Hz (i.e., 110 Hz/1024). Let N s be the number of elements of S T H defined as

where λ is adjusting factor and σ is standard deviation of S x x . Given the distribution of frequency components of an earthquake, the signal is classified as an earthquake if N s > τ s e i s m i c , where τ s e i s m i c is an adjustable threshold. In this study, λ of 0.5 and τ s e i s m i c of 25 are used, respectively. The MMI was obtained using peak measured acceleration.

2.4. BLE for Seismic Event Notification

BLE is the term for Bluetooth 4.0+ and is characterized by low power consumption, and it has the ability to exchange data either in connection or advertising mode [ 27 ]. In advertising mode, BLE devices can broadcast certain information (e.g., temperature, battery status, etc.) without direct connection to neighboring devices. Advertising mode uses the generic access profile (GAP) layer to broadcast data, specially formatted advertising packets, in a one-to-many transfer. Each type of beacon uses a custom specification to partition up the advertising data according to the SIG (signal interest group) specifications. For instance, iBeacon and Eddystone are the commonly used data packet (31 Bytes) for the BLE beacon (see Figure 8 ). iBeacon, developed by Apple, is the first beacon protocol introducing the beacon to broadcast their identifier to nearby portable electronic devices. Eddystone was the protocol developed two years later by Google. Eddystone has more flexibility in data packets and compatibility with both Android and IOS. However, using and modifying data packets requires a more complex programming process compared to iBeacon.

BLE packet format.

This paper proposes a BLESeis protocol to more effectively advertise seismic information (i.e., location of the sensor, peak acceleration, MMI scale, and transmission power (TX power)) to surrounding IoT devices. The proposed protocol was created by modifying iBeacon’s data packets without affecting the standard. Note that iBeacon protocol was selected because of ease of modification for the intended use (i.e., UUID for GPS, Major for PGA, and Minor for level). The definition of the BLESeis data packets is given as:

• Company ID (2-Byte): Identifier of the manufacturer of a BLESeis (e.g., BS (0x4353))
• GPS coordinate: (16-Byte): GPS latitude (8-Byte) and longitude (8-Byte) of the BLESeis (e.g., Lat: 37.503640480778266, Long: 126.95702612400056 for Chung-Ang University, Seoul, Korea)
• PGA (2-Byte): Maximum measured acceleration in mg unit. (e.g., 34)
• Level (2-Byte): MMI scale of PGA multiplied by 10
• TX (1-Byte): TX power level, indicating the signal strength of the BLE device when transmitted

3. Numerical Validation

3.1. numerical setup.

The numerical simulation was conducted to validate the performance of the STA/LTA detector and seismic event detection algorithm. The artificial earthquake, structural vibration, and random signal were prepared; the artificial earthquake was generated by Kanai–Tajimi [ 28 , 29 , 30 , 31 , 32 ] spectrum whose transfer function is expressed as:

where S 0 is the power of the random input signal ξ g , ω g are soil damping and soil frequency, respectively. In the numerical simulation, ξ g , ω g are set to 0.3 and 17 rad/s to generate an artificial earthquake in the specific ground condition. The type 2 vibration that is modeled as a combination of two sine wave, v ( t ) = A 1 sin ( 2 π f 1 t ) + A 2 sin ( 2 π f 2 t ) which follows normal distribution as:

Note that the Hanning window [ 33 ] is applied to smoothen the seismic and type 2 vibration. Type 1 vibration was modeled as a random signal having power spectral density of S 0 without the Hanning window.

3.2. STA/LTA Trigger

With a total of 300 vibration datasets consisting of 100 datasets for each case (i.e., type 1, type 2, and type 3–earthquake), the detection of the trigger was validated as shown in Figure 9 with the red line that represents the time of trigger detected by the STA/LTA algorithm. STA/LTA ratio showed an abrupt change at the occurrence of the vibration, whereas the ratio is 1 for the vibration-free region. Figure 9 a shows the time-domain acceleration of type 1 (e.g., random vibration), and Figure 9 d shows the corresponding ratio of STA/LTA. Since the signal is stationary after a certain point, the STA/LTA ratio shows a peak point that is the time of the trigger. Figure 9 b,c show type 2 (e.g., structural vibration) and type 3 signal (e.g., earthquake), respectively. Since both type 2 and type 3 signals are nonstationary, many peaks in the ratio of STA/LTA are shown in Figure 9 e,f.

Examples of sampled acceleration and trigger: ( a ) random vibration; ( b ) structural vibration; ( c ) artificial earthquake; ( d–f ) STA/LTA ratio and detected triggering time (red).

The STA/LTA algorithm successfully detected the change in vibration and showed a high precision and reliable detection capability by showing the time of trigger at 3000 ms for type 1, 4713 ± 76 ms for an earthquake, and 4994 ± 791 ms for type 2. The time of trigger can differ by types of vibration as type 2 and type 3 (earthquake) vibrations are smoothed by the Hann window that delayed the occurrence of the vibration, whereas type 1 vibration caused an abrupt change in the STA/LTA ratio. The STA/LTA ratio can be adjusted depending on the site condition; if there is no ambient source of vibration, STA/LTA is set to low to increase detection sensitivity; the site with ambient vibration source can have higher STA/LTA to make more robust detection lowering the false-positive rate.

3.3. Performance of the Proposed Seismic Event Detection

A total of 300 datasets were evaluated using the proposed earthquake detection algorithm. The seismic event detector first classifies the stationary and nonstationary signal using Equation (4) with the τ r a n d o m of 0.75. In this time-domain analysis step, 100 type 1-stationary vibration datasets were all classified as type 1 vibration. A nonstationary signal that passed time-domain analysis was classified by frequency-domain analysis given in Equation (5). Figure 10 shows the PSD of generated seismic wave and type 2 with a pre-determined threshold of E [ S x x ] + 0.5 σ s x x . Using the number of PSD data points exceeding the threshold, the seismic wave and type 2 can be successfully classified. For example, the signal in Figure 10 a is classified as seismic wave because the number of PSD data points exceeding the threshold was greater than 25, whereas the signal in Figure 10 b had only two data points exceeding the threshold and was classified as type 2 signal. Out of 200 nonstationary signals consisting of the generated earthquake and type 2, all the vibration were successfully classified as seismic wave and type 2 signal, respectively, without a single misclassification.

Power spectral density (PSD) of ( a ) the generated earthquake and ( b ) structural vibration.

4. Experimental Validation

The performance of developed BLESeis was evaluated experimentally through two types of tests: ambient vibration tests and shaking table tests. Ambient vibration tests were conducted to demonstrate the low-noise level and performance of the embedded filter of the sensor. The shaking table tests were conducted on a shaking table with a generated random, earthquake, and type 2 vibration in order to demonstrate the performance of the seismic event detection.

4.1. Ambient Vibration Test on BLESeis

The ambient vibration tests were conducted to measure the noise floor of developed BLESeis and compared with the amplitudes of the various magnitude of earthquakes measured at the distance of 10 km [ 34 ].

Figure 11 shows that developed BLESeis has sensitivity to magnitude 3.5 (M3.5) and MMI-2 (PGA of 0.7 mg) or larger earthquakes in the frequency range of 1 to 10 Hz, which is the most critical frequency component of an earthquake that cause the most damage.

Noise floor of developed BLESeis: ( a ) time-domain noise; ( b ) frequency-domain noise.

4.2. Shaking Table Test

To validate the seismic event detection efficiency of the developed BLESeis, three types of vibration were excited by the shaking table: (1) type 1-random, (2) type 2-vibration with different driving frequencies, and (3) type 3 – artificial earthquake. The three types of vibration followed the design made in the numerical validation. The peak magnitude of each vibration generated by the shaking table was adjusted to have an MMI scale of 5 and 7. A total of 60 tests were conducted to validate the performance of developed BLESeis and were compared with the reference accelerometer.

Figure 12 shows the experimental setup with developed BLESeis and reference accelerometer fixed on the 1-dof shaking table. The sampling rate of reference accelerometer was set to 100 Hz for comparison with developed BLESeis. Each vibration was designed to have 10 s of delay before the main event occurred. Note that, due to the ambient vibration of the shaking table, vibrations with a small MMI scale were not considered in the experiments. In the experiment, the parameters related to triggering, STA/LTA, τ r a n d o m , λ , τ s e i s m i c were set 1.3, 0.75, 0.5, and 25, respectively.

Experimental Setup.

Figure 13 shows the measured acceleration of BLESeis compared with the reference accelerometer. The measurement was triggered at the beginning of the occurrence of the vibration, and 10 s of data were acquired. The measured acceleration of BLESeis showed good agreement with reference acceleration, validating the performance.

Comparison of BLESeis with reference acceleration: ( a ) artificial earthquake MMI-5; ( b ) type 2 vibration MMI-7.

The detection capability of the BLESeis was evaluated through a total of 60 experiments using different loadings, and the results are summarized in Table 2 . It was shown that the proposed BLESeis successfully detected seismic events as well as classifying type 1 and type 2 signals.

Event detection accuracy.

The proposed beacon packet was captured and visualized through a smartphone (see Figure 14 ). The UUID highlighted by the red box indicates the location (i.e., latitude and longitude) of the BLESeis. The major and minor are the PGA in mg unit, and MMI-scale multiplied by 10, respectively. The BLESeis packet was visualized with its location and MMI-scale, which can be transmitted to any BLE devices nearby.

Visualization of BLESeis Packet.

5. Conclusions

In this article, the BLESeis, which can detect earthquakes using low-cost MEMS sensors, is introduced. The hardware and software framework were designed carefully for the BLESeis to enable STA/LTA trigger, high-fidelity vibration sensing, and earthquake detection using time and frequency domain analysis. Moreover, the beacon packet is designed to efficiently broadcast the sensor’s location, PGA, and MMI-scale. The numerical simulation was conducted to validate the performance of the proposed detection algorithm, and parameters associated with triggering and detection are set accordingly. The experimental validation was carried out on a shaking table excited by random vibration, vibration with two major modes, and earthquake. The experimental results showed that the developed BLESeis detects the occurrence of a vibration and successfully classifies an earthquake with 100% accuracy. Moreover, the BLE beacon packets were shown to broadcast with defined information, including location, PGA, and MMI-scale that can be used for a customized alert system.

Note that, even though the proposed sensor showed remarkable performance, it has the following limitations: (1) the notification range is limited to 10 m with BLE, so that long-range communication is difficult. The proposed sensor used BLE for close-range notification with advertising mode, and it cannot transfer the notification to distant devices; (2) the parameters of the proposed algorithm, such as the ratio of STA/LTA, should be re-defined according to the properties of the ground or place where the sensors will be deployed. Numerical simulation considering specific ground properties might have to be conducted.

Future research will be conducted to develop a low-power sensor that can be used for long-term monitoring of a seismic event. Furthermore, long-range earthquake notification using ZigBee or LoRa will be researched to address the limitation of the notification range.

Author Contributions

All authors contributed to the main idea of this paper. J.W. and J.P. wrote the software and designed the experiments. I.K. and J.-W.P. analyzed the experimental results. J.P. and J.W. wrote the article, and J.-W.P. and I.K. supervised the overall research effort. All authors have read and agreed to the published version of the manuscript.

This research was supported by the National Research Foundation of Korea (NRF) Grant funded by the Ministry of Science and ICT for convergent research in Development program for convergence R&D over Science and Technology Liberal Arts (NRF-2017M3C1B6069981) and the Chung-Ang University Research Grants in 2019.

Conflicts of Interest

The authors declare no conflict of interest.

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Earthquake Detection and Alerting System

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This paper offers a brief summary of my group's Earthquake Detection and Alerting System project. As Earthquakes pose a serious threat to human life.They are caused by seismic waves, which are caused by a sudden release of energy in the Earth's crust. Earthquakes can be so powerful that they can throw people around and kill whole cities. Earthquakes, as we all know, are a natural occurrence that cannot be prevented.However, if we do not take appropriate action to tackle it, it can be extremely dangerous. Furthermore, seismometers can be used to track earthquakes, but they are very expensive. As a result, there should be a mechanism in place that can detect an earthquake without the use of a seismometer and warn the DMT and residents.

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