Toward Global Earthquake Early Warning with the MyShake Smartphone Seismic Network, Part 2: Understanding MyShake Performance around the World

2020 ◽  
Vol 91 (4) ◽  
pp. 2218-2233
Author(s):  
Qingkai Kong ◽  
Robert Martin-Short ◽  
Richard M. Allen
Keyword(s):  
Early Warning ◽  
Mobile Application ◽  
Large Scale ◽  
Detection Algorithm ◽  
Seismic Network ◽  
Earthquake Early Warning ◽  
High Population ◽  
Simulation Platform ◽  
Origin Time ◽  
The World

Abstract The MyShake project aims to build a global smartphone seismic network to facilitate large-scale earthquake early warning (EEW) and other applications by leveraging the power of crowdsourcing. The MyShake mobile application first detects earthquake shaking on a single phone. The earthquake is then confirmed on the MyShake servers using a “network detection” algorithm that is activated by multiple single-phone detections. In part two of this two-article series, we report the first-order performance of MyShake’s EEW capability in various selected locations around the world. Because of the present sparseness of the MyShake network in most parts of the world, we use our simulation platform to understand and evaluate the system’s performance in various tectonic settings. We assume that 0.1% of the population in each region has the MyShake mobile application installed on their smartphone and use historical earthquakes from the last 20 yr to simulate triggering scenarios with different network configurations in various regions. Then, we run the detection algorithm with these simulated triggers to understand the performance of the system. The system performs best in regions featuring high population densities and onshore, upper crustal earthquakes M<7.0. In these cases, alerts can be generated ∼4–6  s after the origin time, magnitude errors are within ∼0.5 magnitude units, and epicenters are typically within 10 km of true locations. When the events are offshore or in sparsely populated regions, the alerts are slower and the uncertainties in magnitude and location increase. Furthermore, even with 0.01% of the population as the MyShake users, in regions of high population density, the system still performs well for earthquakes larger than M 5.5. The details of the simulation platform and the network detection algorithm are available in part 1 of this two-article series.

Toward Global Earthquake Early Warning with the MyShake Smartphone Seismic Network, Part 1: Simulation Platform and Detection Algorithm

2020 ◽  
Vol 91 (4) ◽  
pp. 2206-2217
Author(s):  
Qingkai Kong ◽  
Robert Martin-Short ◽  
Richard M. Allen
Keyword(s):  
Early Warning ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Detection Algorithm ◽  
Seismic Network ◽  
Earthquake Early Warning ◽  
Simulation Platform ◽  
Simulation Performance ◽  
The World

Abstract The MyShake project aims to build a global smartphone seismic network to facilitate large-scale earthquake early warning and other applications by leveraging the power of crowdsourcing. The MyShake mobile application first detects earthquake shaking on a single phone. The earthquake is then confirmed on the MyShake servers using a “network detection” algorithm that is activated by multiple single-phone detections. In this part one of the two article series, we present a simulation platform and a network detection algorithm to test earthquake scenarios at various locations around the world. The proposed network detection algorithm is built on the classic density-based spatial clustering of applications with noise spatial clustering algorithm, with modifications to take temporal characteristics into account and the association of new triggers. We test our network detection algorithm using real data recorded by MyShake users during the 4 January 2018 M 4.4 Berkeley and the 10 June 2016 M 5.2 Borrego Springs earthquakes to demonstrate the system’s utility. In order to test the entire detection procedure and to understand the first order performance of MyShake in various locations around the world representing different population and tectonic characteristics, we then present a software platform that can simulate earthquake triggers in hypothetical MyShake networks. Part two of this paper series explores our MyShake early warning simulation performance in selected regions around the world.

scholarly journals Earthquake Early Warning System in Israel—Towards an Operational Stage

2021 ◽  
Vol 9 ◽  
Author(s):  
Ran N. Nof ◽  
Itzhak Lior ◽  
Ittai Kurzon
Keyword(s):  
Ground Motion ◽  
Early Warning ◽  
Seismic Network ◽  
Earthquake Early Warning ◽  
Performance Estimation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Origin Time

The Geological Survey of Israel has upgraded and expanded the national Israeli Seismic Network (ISN), with more than 110 stations country-wide, as part of the implementation of a governmental decision to build a national Earthquake Early Warning (EEW) system named TRUAA. This upgraded seismic network exhibits a high station density and fast telemetry. The stations are distributed mainly along the main fault systems, the Dead Sea Transform, and the Carmel-Zfira Fault, which may potentially produce Mw 7.5 earthquakes. The system has recently entered a limited operational phase, allowing for initial performance estimation. Real-time performance during eight months of operation (41 earthquakes) matches expectations. Alert delays (interval between origin-time and Earthquake Early Warning alert time) are reduced to as low as 3 s, and source parameter errorstatistics are within expected values found in previous works using historical data playbacks. An evolutionary alert policy is implemented based on a magnitude threshold of Mw 4.2 and peak ground accelerations exceeding 2 cm/s2. A comparison between different ground motion prediction equations (GMPE) is presented for earthquakes from Israel and California using median ground motion prediction equations values. This analysis shows that a theoretical GMPE produced the best agreement with observed ground motions, with less bias and lower uncertainties. The performance of this GMPE was found to improve when an earthquake specific stress drop is implemented.

scholarly journals Status of Earthquake Early Warning in Switzerland

2021 ◽  
Vol 9 ◽  
Author(s):  
Frédérick Massin ◽  
John Clinton ◽  
Maren Böse
Keyword(s):  
Real Time ◽  
Open Source Software ◽  
Early Warning ◽  
Seismic Network ◽  
Earthquake Early Warning ◽  
Fault Rupture ◽  
Time Data ◽  
Origin Time ◽  
P Waves ◽  
Finite Fault

The Swiss Seismological Service (SED) at ETH has been developing methods and open-source software for Earthquake Early Warning (EEW) for more than a decade and has been using SeisComP for earthquake monitoring since 2012. The SED has built a comprehensive set of SeisComP modules that can provide EEW solutions in a quick and transparent manner by any seismic service operating SeisComP. To date, implementations of the Virtual Seismologist (VS) and Finite-Fault Rupture Detector (FinDer) EEW algorithms are available. VS provides rapid EEW magnitudes building on existing SeisComP detection and location modules for point-source origins. FinDer matches growing patterns of observed high-frequency seismic acceleration amplitudes with modeled templates to identify rupture extent, and hence can infer on-going finite-fault rupture in real-time. Together these methods can provide EEW for all event dimensions from moderate to great, if a high quality, EEW-ready, seismic network is available. In this paper, we benchmark the performance of this SeisComP-based EEW system using recent seismicity in Switzerland. Both algorithms are observed to be similarly fast and can often produce first EEW alerts within 4–6 s of origin time. In real time performance, the median delay for the first VS alert is 8.7 s after origin time (56 earthquakes since 2014, from M2.7 to M4.6), and 7 s for FinDer (10 earthquakes since 2017, from M2.7 to M4.3). The median value for the travel time of the P waves from event origin to the fourth station accounts for 3.5 s of delay; with an additional 1.4 s for real-time data sample delays. We demonstrate that operating two independent algorithms provides redundancy and tolerance to failures of a single algorithm. This is documented with the case of a moderate M3.9 event that occured seconds after a quarry blast, where picks from both events produced a 4 s delay in the pick-based VS, while FinDer performed as expected. Operating on the Swiss Seismic Network, that is being continuously optimised for EEW, the SED-ETHZ SeisComP EEW system is achieving performance that is comparable to operational EEW systems around the world.

An Advanced Seismic Network in the Southern Apennines (Italy) for Seismicity Investigations and Experimentation with Earthquake Early Warning

2007 ◽  
Vol 78 (6) ◽  
pp. 622-634 ◽  
Author(s):  
E. Weber ◽  
V. Convertito ◽  
G. Iannaccone ◽  
A. Zollo ◽  
A. Bobbio ◽  
...  
Keyword(s):  
Early Warning ◽  
Seismic Network ◽  
Earthquake Early Warning ◽  
Southern Apennines

TRUAA—Earthquake Early Warning System for Israel: Implementation and Current Status

2020 ◽  
Vol 92 (1) ◽  
pp. 325-341
Author(s):  
Ran N. Nof ◽  
Ittai Kurzon
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Dead Sea ◽  
Earthquake Early Warning ◽  
Current Status ◽  
Origin Time ◽  
Earthquake Early Warning System ◽  
Time Performance ◽  
Integrated Code

Abstract Following a governmental decision to build a national earthquake early warning system (EEWS) named TRUAA, the Geological Survey of Israel has upgraded the national Israeli Seismic Network with more than 100 stations countrywide. The stations are spread mainly along the main hazardous fault systems of the Dead Sea and Carmel-Zfira, which potentially may produce Mw 7.5 earthquakes. Currently the system is shifting from the deployment phase into a testing phase in which the earthquake point-source integrated code (EPIC) EEW algorithm is used. During the deployment phase, real-time performance of the EEW algorithm has steadily improved, with alert delays (span between origin time and EEW alert time) reduced down to 3 s in some cases. We present an overview of TRUAA, the performance of EPIC during the deployment phase and during playbacks of historic events, as well as our suggested alert approach for Israel.

scholarly journals A High-Density Seismic Network for Earthquake Early Warning in Taiwan Based on Low Cost Sensors

2013 ◽  
Vol 84 (6) ◽  
pp. 1048-1054 ◽  
Author(s):  
Y.-M. Wu ◽  
D.-Y. Chen ◽  
T.-L. Lin ◽  
C.-Y. Hsieh ◽  
T.-L. Chin ◽  
...  
Keyword(s):  
Early Warning ◽  
Low Cost ◽  
High Density ◽  
Seismic Network ◽  
Earthquake Early Warning

The “TRUAA” Seismic Network: Upgrading the Israel Seismic Network—Toward National Earthquake Early Warning System

2020 ◽  
Vol 91 (6) ◽  
pp. 3236-3255 ◽  
Author(s):  
Ittai Kurzon ◽  
Ran N. Nof ◽  
Michael Laporte ◽  
Hallel Lutzky ◽  
Andrey Polozov ◽  
...  
Keyword(s):  
Early Warning ◽  
High Speed ◽  
High Capacity ◽  
Strong Motion ◽  
Virtual Private Network ◽  
Seismic Network ◽  
Earthquake Early Warning ◽  
Fault System ◽  
Earthquake Early Warning System ◽  
Private Network

Abstract Following the recommendations of an international committee (Allen et al., 2012), since October 2017, the Israeli Seismic Network has been undergoing significant upgrades, with 120 stations being added or upgraded throughout the country and the addition of two new datacenters. These enhancements are the backbone of the TRUAA project, assigned to the Geological Survey of Israel (GSI) by the Israeli Government, to provide earthquake early warning (EEW) capabilities for the state of Israel. The GSI contracted Nanometrics (NMX), supported by Motorola Solutions Israel, to deliver these upgrades through a turnkey project, including detailed design, equipment supply, and deployment of the network and two datacenters. The TRUAA network was designed and tailored by the GSI, in collaboration with the NMX project team, specifically to achieve efficient and robust EEW. Several significant features comprise the pillars of this network:Coverage: Station distribution has high density (5–10 km spacing) along the two main fault systems—the Dead Sea Fault and the Carmel Fault System;Instrumentation: High-quality strong-motion accelerometers and broadband seismometers with modern three-channel and six-channel dataloggers sampling at 200 samples per second;Low latency acquisition: Data are encapsulated in small packets (<1  s), with primary routing via high-speed, high-capacity telemetry links (<1  s latency);Robustness: High level of redundancy throughout the system design:Dual active-active redundant acquisition routes from each station, each utilizing multicast streaming over an IP security Virtual Private Network tunnel, via independent high-bandwidth telemetry systemsTwo active-active independent geographically separate datacentersDual active-active redundant independent automatic seismic processing tool chains within each datacenter, implemented in a high availability protected virtual environment. At this time, both datacenters and over 100 stations are operational. The system is currently being commissioned, with initial early warning operation targeted for early 2021.

Event Detection Performance of the PLUM Earthquake Early Warning Algorithm in Southern California

2019 ◽  
Vol 109 (4) ◽  
pp. 1524-1541 ◽  
Author(s):  
Elizabeth S. Cochran ◽  
Julian Bunn ◽  
Sarah E. Minson ◽  
Annemarie S. Baltay ◽  
Deborah L. Kilb ◽  
...  
Keyword(s):  
Los Angeles ◽  
Ground Motion ◽  
Early Warning ◽  
Southern California ◽  
Ground Motions ◽  
Tohoku Earthquake ◽  
Prediction Equation ◽  
Earthquake Early Warning ◽  
Japan Meteorological Agency ◽  
Origin Time

Abstract We test the Japanese ground‐motion‐based earthquake early warning (EEW) algorithm, propagation of local undamped motion (PLUM), in southern California with application to the U.S. ShakeAlert system. In late 2018, ShakeAlert began limited public alerting in Los Angeles to areas of expected modified Mercalli intensity (IMMI) 4.0+ for magnitude 5.0+ earthquakes. Most EEW systems, including ShakeAlert, use source‐based methods: they estimate the location, magnitude, and origin time of an earthquake from P waves and use a ground‐motion prediction equation to identify regions of expected strong shaking. The PLUM algorithm uses observed ground motions directly to define alert areas and was developed to address deficiencies in the Japan Meteorological Agency source‐based EEW system during the 2011 Mw 9.0 Tohoku earthquake sequence. We assess PLUM using (a) a dataset of 193 magnitude 3.5+ earthquakes that occurred in southern California between 2012 and 2017 and (b) the ShakeAlert testing and certification suite of 49 earthquakes and other seismic signals. The latter suite includes events that challenge the current ShakeAlert algorithms. We provide a first‐order performance assessment using event‐based metrics similar to those used by ShakeAlert. We find that PLUM can be configured to successfully issue alerts using IMMI trigger thresholds that are lower than those implemented in Japan. Using two stations, a trigger threshold of IMMI 4.0 for the first station and a threshold of IMMI 2.5 for the second station PLUM successfully detect 12 of 13 magnitude 5.0+ earthquakes and issue no false alerts. PLUM alert latencies were similar to and in some cases faster than source‐based algorithms, reducing area that receives no warning near the source that generally have the highest ground motions. PLUM is a simple, independent seismic method that may complement existing source‐based algorithms in EEW systems, including the ShakeAlert system, even when alerting to light (IMMI 4.0) or higher ground‐motion levels.

Performance of a Hybrid Demonstration Earthquake Early Warning System in the Sichuan–Yunnan Border Region

2020 ◽  
Vol 91 (2A) ◽  
pp. 835-846
Author(s):  
Chaoyong Peng ◽  
Qiang Ma ◽  
Peng Jiang ◽  
Wenhui Huang ◽  
Dake Yang ◽  
...  
Keyword(s):  
Magnitude Estimation ◽  
Early Warning ◽  
Risk Mitigation ◽  
Effective Means ◽  
Early Warning Systems ◽  
Test Period ◽  
Earthquake Early Warning ◽  
Border Region ◽  
Hybrid Network ◽  
Origin Time

Abstract Earthquake early warning systems (EEWSs) are considered to be one of the most effective means for seismic risk mitigation, in terms of both losses and societal resilience, by releasing an alarm immediately after an earthquake occurs and before strong ground shaking arrives the target sites to be protected. To gain experience for the National System for Fast Seismic Intensity Report and Earthquake Early Warning project, we deployed a hybrid demonstration EEWS in the Sichuan–Yunnan border region with micro-electro-mechanical system-based sensors and broadband seismographs and low-latency data transmission. In this study, we described the structure of this EEWS and analyzed its performance in the first 2 yr from January 2017 to December 2018. During this test period, the EEWS detected and processed a total of 126 ML 3.0+ earthquakes, with excellent epicentral location and magnitude estimation. The average location and magnitude estimation errors for the first alert were 4.2±7.1  km and 0.2±0.31, respectively. For the earthquakes that occurred inside and outside the hybrid network, the first alert was generated 13.4±5.1  s and 26.3±13.5  s after the origin time (OT), respectively. We analyzed the performance of the EEWS for the 31 October 2018 M 5.1 earthquake, because it was the largest event that occurred inside the hybrid network during the test period. The first alert was obtained at 7.5 s after the OT, with a magnitude error of 0.1 magnitude unit, a location error of about 1 km, and a depth error of 8 km. Finally, we discussed the main differences between the EEWS’s estimates and the catalogs obtained by the China Earthquake Network Center, and proposed improvements to reduce the reporting time. This study demonstrated that we constructed a reliable, effective hybrid EEWS for the test region, which can provide sufficient support for the design of the National EEWS project.

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