HR: 09:00h
AN: S21D-04    [Abstracts]
TI: Istanbul Earthquake Early Warning System
AU: Alcik, H
EM: alcik@boun.edu.tr
AF: Bogazici University, Cengelkoy, Istanbul, 34684, Turkey
AU: Mert, A
EM: mertayqboun.edu.tr
AF: Bogazici University, Cengelkoy, Istanbul, 34684, Turkey
AU: Ozel, O
EM: ozeoguz@boun.edu.tr
AF: Istanbul University, Avcilar, Istanbul, 34600, Turkey
AU: * Erdik, M
EM: erdik@boun.edu.tr
AF: Bogazici University, Cengelkoy, Istanbul, 34684, Turkey
AB: As part of the preparations for the future earthquake in Istanbul a Rapid Response and Early Warning system in the metropolitan area is in operation. For the Early Warning system ten strong motion stations were installed as close as possible to the fault zone. Continuous on-line data from these stations via digital radio modem provide early warning for potentially disastrous earthquakes. Considering the complexity of fault rupture and the short fault distances involved, a simple and robust Early Warning algorithm, based on the exceedance of specified threshold time domain amplitude levels is implemented. The band-pass filtered accelerations and the cumulative absolute velocity (CAV) are compared with specified threshold levels. When any acceleration or CAV (on any channel) in a given station exceeds specific threshold values it is considered a vote. Whenever we have 2 station votes within selectable time interval, after the first vote, the first alarm is declared. In order to specify the appropriate threshold levels a data set of near field strong ground motions records form Turkey and the world has been analyzed. Correlations among these thresholds in terms of the epicenter distance the magnitude of the earthquake have been studied. The encrypted early warning signals will be communicated to the respective end users. Depending on the location of the earthquake (initiation of fault rupture) and the recipient facility the alarm time can be as high as about 8s. The first users of the early warning signal will be the Istanbul gas company (IGDAS) and the metro line using the immersed tube tunnel (MARMARAY). Other prospective users are power plants and power distribution systems, nuclear research facilities, critical chemical factories, petroleum facilities and high-rise buildings. In this study, different algorithms based on PGA, CAV and various definitions of instrumental intensity will be discussed and triggering threshold levels of these parameters will be studied. More complex algorithms based on artificial neural networks (ANN) can also be used [Boese et al., 2003]. ANN approach considers the problem of earthquake early-warning as a pattern recognition task. The seismic patterns can be defined by the shape and frequency content of the parts of accelerograms that are available at each time step. ANN can extract the engineering parameters PGA, CAV and instrumental intensity from these patterns, and map them to any location in the surrounded area. Boese M., Erdik, M., Wenzel, F. (2003), Artificial Neural Networks for Earthquake Early Warning, Proceedings AGU2003 Abstracts, S42B-0155
DE: 7212 Earthquake ground motions and engineering seismology
DE: 7215 Earthquake source observations (1240)
DE: 7294 Seismic instruments and networks (0935, 3025)
SC: Seismology [S]
MN: 2007 Fall Meeting