Rapid Estimation of the Epicentral Distance in the Earthquake Early Warning System around the Tehran Region, Iran

2020 ◽  
Vol 91 (4) ◽  
pp. 2438-2438
Author(s):  
Sahar Nazeri ◽  
Zaher Hossein Shomali
Author(s):  
Sahar Nazeri ◽  
Zaher Hossein Shomali

ABSTRACT The estimation of epicentral distance is a critical step in earthquake early warning systems (EEWSs) that is necessary to characterize the level of expected ground shaking. In this study, two rapid methodologies, that is, B‐Δ and C‐Δ, are evaluated to estimate the epicentral distance for use in the EEWSs around the Tehran region. Traditionally, the B and C coefficients are computed using acceleration records, however, in this study, we utilize both acceleration and velocity waveforms for obtaining a suitable B‐Δ and C‐Δ relationships for the Tehran region. In comparison with observations from Japan, our measurements fall within the range of scatter. However, our results show a lower trend, which can strongly depend on the few numbers of events and range of magnitude (small‐to‐moderate) of earthquakes used in the current research. To improve our result, we include some large earthquakes from Iran, Italy, and Japan with magnitude larger than 5.9. Although the optimal trend is finally obtained by fitting a line to the distance‐averaged points, we conclude that the same trend and relationship as Japan can be used in Tehran early warning system. We also found that B and C parameters are strongly compatible to each other. As time windows of 3.0 and 0.5 s after the P onset are chosen respectively to compute the B and C values, so by selecting the C parameter as a proxy of B parameter to estimate the epicentral distance, we may save significant time in order of about 2.5 s in any earthquake early warning applications.


Author(s):  
S. Enferadi ◽  
Z. H. Shomali ◽  
A. Niksejel

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.


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

2021 ◽  
Author(s):  
Bita Najdahmadi ◽  
Marco Pilz ◽  
Dino Bindi ◽  
Hoby Njara Tendrisoa Razafindrakoto ◽  
Adrien Oth ◽  
...  

<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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