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.

Timeliness of earthquake magnitude estimation from the prompt elasto-gravity signal  using Deep Learning

2021 ◽  
Author(s):  
Andrea Licciardi ◽  
Quentin Bletery ◽  
Bertrand Rouet-Leduc ◽  
Jean-Paul Ampuero ◽  
Kévin Juhel
Keyword(s):  
Deep Learning ◽  
Magnitude Estimation ◽  
Early Warning ◽  
Early Warning Systems ◽  
Earthquake Magnitude ◽  
Reliable Estimate ◽  
Origin Time ◽  
Tsunami Early Warning ◽  
Gravity Signal ◽  
Large Earthquakes

<p>Mass redistribution during large earthquakes produces a prompt elasto-gravity signal (PEGS) that travels at the speed of light and can be observed on seismograms before the arrival of P-waves. PEGS carries information about earthquake magnitude and the temporal evolution of seismic moment, therefore it could be used to both improve the accuracy of current early source estimation systems and speed-up early warning. However, PEGS has been detected for only a handful of very large earthquakes so far, and its potential use for operational early warning remains to be established. In this work, we study the timeliness of magnitude estimation for subduction earthquakes in Japan using PEGS waveforms by means of Deep Learning and Bayesian uncertainty analysis. Given the paucity of PEGS observations, we train the model on a database of synthetic seismograms augmented with empirical noise in order to simulate more realistic waveforms. We use about 80 stations from the Japanese F-Net network and from networks with data available through IRIS.</p><p>Under this experimental setup, we find that our model is able to track the moment release for earthquakes with a final Mw above 8.0, with a system latency that depends on the signal-to-noise ratio of PEGS. The application of our model to the Mw=9.1 Tohoku-Oki earthquake shows a latency of about 50 s after which the model is able to track well the evolving Mw of the earthquake. After about 2 minutes from the earthquake origin time, a reliable estimate of its final Mw is obtained. Similar performances in terms of timeliness of final Mw estimation are observed for the relatively smaller Hokkaido earthquake (Mw=8.1) although with higher uncertainty.</p><p>Our results highlight the potential of PEGS to enhance the performance of existing tsunami early warning systems where estimating the magnitude of very large earthquakes within few minutes is vital.</p><p> </p>

scholarly journals Quantifying the effectiveness of early warning systems for natural hazards

2016 ◽  
Vol 16 (1) ◽  
pp. 149-166 ◽  
Author(s):  
M. Sättele ◽  
M. Bründl ◽  
D. Straub
Keyword(s):  
Natural Hazards ◽  
Early Warning ◽  
Risk Mitigation ◽  
Early Warning Systems ◽  
Mitigation Measures ◽  
Management Approach ◽  
Warning Systems ◽  
Integrated Risk ◽  
Framework Approach

Abstract. Early warning systems (EWSs) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWSs is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWSs and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWSs is provided, which focuses on the degree of automations encountered in varying EWSs. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.

scholarly journals Quantifying the effectiveness of early warning systems for natural hazards

2015 ◽  
Vol 3 (7) ◽  
pp. 4479-4526 ◽  
Author(s):  
M. Sättele ◽  
M. Bründl ◽  
D. Straub
Keyword(s):  
Natural Hazards ◽  
Early Warning ◽  
Risk Mitigation ◽  
Early Warning Systems ◽  
Mitigation Measures ◽  
Management Approach ◽  
Warning Systems ◽  
Integrated Risk ◽  
Framework Approach

Abstract. Early warning systems (EWS) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWS is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWS and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWS is provided, which focuses on the degree of automations encountered in varying EWS. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.

Off-Network Earthquake Location by Earthquake Early Warning Systems: Methodology and Validation

2021 ◽  
Author(s):  
Mark Netanel ◽  
Andreas Samuel Eisermann ◽  
Alon Ziv
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
S Phase ◽  
Earthquake Early Warning ◽  
Warning Systems ◽  
S Wave ◽  
Earthquake Early Warning Systems ◽  
Back Azimuth ◽  
Magnitude Determination ◽  
Epicenter Location

ABSTRACT Regional source-based earthquake early warning systems perform three consecutive tasks: (1) detection and epicenter location, (2) magnitude determination, and (3) ground-motion prediction. The correctness of the magnitude determination is contingent on that of the epicenter location, and the credibility of the ground-motion prediction depends on those of the epicenter location and the magnitude determination. Thus, robust epicenter location scheme is key for regional earthquake early warning systems. Available source-based systems yield acceptably accurate locations when the earthquakes occur inside the real-time seismic network, but they return erroneous results otherwise. In this study, a real-time algorithm that is intended as a supplement to an existing regional earthquake early warning systems is introduced with the sole objective of ameliorating its off-network location capacity. The new algorithm combines measurements from three or more network stations that are analyzed jointly using an array methodology to give the P-wave slowness vector and S-phase arrival time. Prior to the S-phase picking, the nonarrival of the S phase is used for determining a minimum epicentral distance. This estimate is updated repeatedly with elapsed time until the S phase is picked. Thus, the system timeliness is not compromised by waiting for the S-phase arrival. After the S wave is picked, an epicentral location can be determined using a single array by intersecting the back-azimuth beam with the S-minus-P annulus. When several arrays are assembled, the back azimuth and P and S picks from all arrays are combined to constrain the epicenter. The performance of the array processing for back azimuth and S-wave picking is assessed using a large number of accelerograms, recorded by nine strong motion sensors of the KiK-net seismic network in Japan. The nine stations are treated as three distinct seismic arrays, comprising three stations each. Good agreement is found between array-based and catalog-reported parameters. Finally, the advantage of the new array methodology with respect to alternative schemes for back azimuth and distance is demonstrated.

Performance of Earthquake Early Warning Systems during the 2016–2017 Mw 5–6.5 Central Italy Sequence

2017 ◽  
Vol 89 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Gaetano Festa ◽  
Matteo Picozzi ◽  
Alessandro Caruso ◽  
Simona Colombelli ◽  
Marco Cattaneo ◽  
...  
Keyword(s):  
Early Warning ◽  
Central Italy ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
Warning Systems ◽  
Earthquake Early Warning Systems
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