scholarly journals The new Algerian Digital Seismic Network (ADSN): towards an earthquake early-warning system

2013 ◽  
Vol 36 ◽  
pp. 31-38 ◽  
Author(s):  
A. Yelles-Chaouche ◽  
T. Allili ◽  
A. Alili ◽  
W. Messemen ◽  
H. Beldjoudi ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Network Performance ◽  
Warning System ◽  
Civil Defense ◽  
Seismic Network ◽  
Earthquake Early Warning System ◽  
Alert Message ◽  
Short Period ◽  
Digital Seismic Network

Abstract. Seismic monitoring in Algeria has seen great changes since the Boumerdes earthquake of 21 May 2003. Indeed, the installation of a new digital seismic network has resulted in a significant upgrade of the previous analog telemetry network. During the last four years, the number of stations in operation has increased substantially from 25 to 69, and 20 of these are broadband, 2 are very broadband, 47 are short period. 21 are equipped with accelerometers. They are all managed by Antelope software from Kinemetrics (US Cie), and they are all connected in real time and use various modes of transmission (e.g., satellite, internet, mobile phone). The spatial repartition of the stations now cover most of northern Algeria. In addition, 70 GPS stations have recently been added to this seismological network, most of them collocated with the seismological stations. Since the installation of the network, the records of local or distant events have improved significantly. The automatic processing of the data in a few minutes allows alert messages to be distributed to Civil Defense and other national authorities to react promptly to any emergency. The current strategy is to improve the data quality, to increase the density of the network by adding about 50 new stations, to reduce the processing time, and to reduce the time needed to send out an alert message. The result should be greatly improved network performance, which will lead to an effective early-warning system.

scholarly journals Design, Implementation and Testing of a Network-Based Earthquake Early Warning System in Greece

2021 ◽  
Vol 9 ◽  
Author(s):  
M. Bracale ◽  
S. Colombelli ◽  
L. Elia ◽  
V. Karakostas ◽  
A. Zollo
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Seismic Network ◽  
Earthquake Early Warning ◽  
Data Set ◽  
Ionian Islands ◽  
Earthquake Early Warning System ◽  
Real Earthquake

In this study we implemented and tested the Earthquake Early Warning system PRESTo (PRobabilistic and Evolutionary early warning System, Satriano et al., 2011) on the Greek Ionian islands of Lefkada, Zakynthos and Kefalonia. PRESTo is a free and open source platform for regional Earthquake Early Warning developed at the University of Naples Federico II, which is currently under experimentation in Southern Italy, in the area covered by the Irpinia Seismic Network. The three Ionian islands selected for this study are located on the North-Western part of the Hellenic trench. Here the seismicity rate and the seismic hazard, coupled with the vulnerability of existing critical infrastructures, make this region among the highest seismic risk areas in Europe, where the application of Earthquake Early Warning systems may become a useful strategy to mitigate the potential damage caused by earthquakes. Here we studied the feasibility of implementing an Earthquake Early Warning system on an existing seismic network, which was not specifically made for earthquake early warning purposes, and evaluated the performance of the system, using a data set of real-earthquake recordings. We first describe the technical details of the implementation of PRESTo in the area of interest, including the preliminary parameter configuration and the empirical scaling relationship calibration. Then we evaluated the performance of the system through the off-line analysis of a database of real earthquake records belonging to the most recent M > 4.0 earthquakes occurred in the area. We evaluated the performance in terms of source parameter estimation (location, magnitude), accuracy of ground shaking prediction and lead-time analysis. Finally, we show the preliminary results of the real-time application of PRESTo, performed during the period 01–31 July 2019.

scholarly journals Performance Evaluation of an Earthquake Early Warning System in the 2019–2020 M6.0 Changning, Sichuan, China, Seismic Sequence

2021 ◽  
Vol 9 ◽  
Author(s):  
Chaoyong Peng ◽  
Peng Jiang ◽  
Qiang Ma ◽  
Peng Wu ◽  
Jingrong Su ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Low Latency ◽  
Seismic Sequence ◽  
Seismic Stations ◽  
Earthquake Early Warning System ◽  
Second Stage ◽  
Short Period

China is currently building a nationwide earthquake early warning system (EEWS) which will be completed in June 2023. Several regions have been selected as pilot areas for instrumentation, software system and dissemination verification. For these regions, their construction tasks will be completed in advance with trial runs being carried out in June 2021. Before the trial operation, we need to understand the actual processing capabilities of different EEWSs. In this work, we focus on the system deployed in Sichuan province and evaluate its real-time performance during the 2019–2020 M6.0 Changning seismic sequence. This period was divided into two stages. The first stage was the time from the occurrence of the M6.0 (Mw5.7) mainshock (June 17, 2019) to the end of October 2019 with no MEMS-based stations around the Changning seismic sequence deployed and most of the broadband and short period seismic stations not upgraded to low latency streaming, and the second one was from the beginning of November 2019 to March 2021 with deployments of more than 700 MEMS-based stations and low latency upgrades of ∼30 seismic stations. Median errors for the epicentral locations, depths and magnitude estimations were almost the same for both stages, 1.5 ± 6.0 km, 0.0 ± 3.6 km and −0.1 ± 0.46 for the first stage and 2.3 ± 3.0 km, −3.0 ± 3.6 km and −0.2 ± 0.32 for the second one. However, an obvious underestimation of the magnitude for earthquakes with M 5.0 + occurring in the first stage was observed, which would be caused by the clipped waveforms, sensors deployed in short period seismic stations and MEMS-based stations, the adopted magnitude estimation method, and the method used to computer the network magnitude. The median reporting time was significantly improved from 10.5 ± 3.0 s after origin time for the first stage to 6.3 ± 3.5 s for the second stage because of introduction of newly deployed MEMS-based stations. The results obtained from the second stage indicate that the system has entered a stable operating stage and we can officially launch the trial operation in the pilot areas for public early warning services.

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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