Performance of a Low‐Cost Earthquake Early Warning System (P‐Alert) and Shake Map Production during the 2018 Mw 6.4 Hualien, Taiwan, Earthquake

2018 ◽  
Vol 90 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Yih‐Min Wu ◽  
Himanshu Mittal ◽  
Ting‐Chung Huang ◽  
Benjamin M. Yang ◽  
Jyh‐Cherng Jan ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Low Cost ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Earthquake Early Warning System ◽  
System P ◽  
Shake Map ◽  
Taiwan Earthquake

Performance of a Low‐Cost Earthquake Early Warning System (P‐Alert) during the 2016ML 6.4 Meinong (Taiwan) Earthquake

2016 ◽  
Vol 87 (5) ◽  
pp. 1050-1059 ◽  
Author(s):  
Yih‐Min Wu ◽  
Wen‐Tzong Liang ◽  
Himanshu Mittal ◽  
Wei‐An Chao ◽  
Cheng‐Horng Lin ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Low Cost ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Earthquake Early Warning System ◽  
System P ◽  
Taiwan Earthquake

scholarly journals Feasibility study of earthquake early warning in Tehran, Iran

2021 ◽  
Author(s):  
S. Enferadi ◽  
Z. H. Shomali ◽  
A. Niksejel
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Disaster Mitigation ◽  
Lead Times ◽  
Worst Case ◽  
Earthquake Parameters ◽  
Earthquake Early Warning System ◽  
Earthquake Scenarios

AbstractIn this study, we examine the scientific feasibility of an Earthquake Early Warning System in Tehran, Iran, by the integration of the Tehran Disaster Mitigation and Management Organization (TDMMO) accelerometric network and the PRobabilistic and Evolutionary early warning SysTem (PRESTo). To evaluate the performance of the TDMMO-PRESTo system in providing the reliable estimations of earthquake parameters and the available lead-times for The Metropolis of Tehran, two different approaches were analyzed in this work. The first approach was assessed by applying the PRESTo algorithms on waveforms from 11 moderate instrumental earthquakes that occurred in the vicinity of Tehran during the period 2009–2020. Moreover, we conducted a simulation analysis using synthetic waveforms of 10 large historical earthquakes that occurred in the vicinity of Tehran. We demonstrated that the six worst-case earthquake scenarios can be considered for The Metropolis of Tehran, which are mostly related to the historical and instrumental events that occurred in the southern, eastern, and western parts of Tehran. Our results indicate that the TDMMO-PRESTo system could provide reliable and sufficient lead-times of about 1 to 15s and maximum lead-times of about 20s for civil protection purposes in The Metropolis of Tehran.

The First Stage of an Earthquake Early Warning System in South Korea

2017 ◽  
Vol 88 (6) ◽  
pp. 1491-1498 ◽  
Author(s):  
Dong‐Hoon Sheen ◽  
Jung‐Ho Park ◽  
Heon‐Cheol Chi ◽  
Eui‐Hong Hwang ◽  
In‐Seub Lim ◽  
...  
Keyword(s):  
South Korea ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Earthquake Early Warning System

Robust, an Earthquake Early Warning System in the Lower Rhine Embayment, Germany

2021 ◽  
Author(s):  
Bita Najdahmadi ◽  
Marco Pilz ◽  
Dino Bindi ◽  
Hoby Njara Tendrisoa Razafindrakoto ◽  
Adrien Oth ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Optimization Approach ◽  
Ground Acceleration ◽  
Earthquake Early Warning System ◽  
Scenario Earthquakes ◽  
Lower Rhine Embayment ◽  
Lower Rhine

<p>The Lower Rhine Embayment in western Germany is one of the most important areas of earthquake recurrence north of the Alps, facing a moderate level of seismic hazard in the European context but a significant level of risk due to a large number of important industrial infrastructures. In this context, the project ROBUST aims at designing a user-oriented hybrid earthquake early warning and rapid response system where regional seismic monitoring is combined with smart, on-site sensors, resulting in the implementation of decentralized early warning procedures.<br><br>One of the research areas of this project deals with finding an optimal regional seismic network arrangement. With the optimally compacted network, strong ground movements can be detected quickly and reliably. In this work simulated scenario earthquakes in the area are used with an optimization approach in order to densify the existing sparse network through the installation of additional decentralized measuring stations. Genetic algorithms are used to design efficient EEW networks, computing optimal station locations and trigger thresholds in recorded ground acceleration. By minimizing the cost function, a comparison of the best earthquake early warning system designs is performed and the potential usefulness of existing stations in the region is considered as will be presented in the meeting.</p>

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