14 Development of the On-Site Earthquake Early Warning Systems for Taiwan Using Neural Networks

The on-site earthquake early warning system (EEWS), as part of the total solution of seismic hazard mitigation, was under development to provide a series of time related parameters such as the magnitude of the earthquake, the time until strong shaking begins, and the seismic intensity of the shaking (peak ground acceleration). Interaction of different types of earthquake ground motion and variations in the elastic property of geological media throughout the propagation path result in a highly nonlinear function. We use neural networks to model these nonlinearities and develop learning techniques for the analysis of earthquake seismic signal. This warning system is designed to analyze the first-arrival from the three components of the earthquake signals in as little as 3 sec after first ground motion is felt at the sensors at a rate of 50 samples per second. Then the EEWS instantaneously provide a profile consists of a magnitude related estimates of damage parameters, such as time until peak ground acceleration (PGA) and maximum seismic intensity. The neural network based system is trained using seismogram data from more than 1000 earthquakes recorded in Taiwan. The proposed EEWS can be integrated with distributed networks for site-specific applications. By producing accurate and informative warnings, the system has the potential to significantly minimize the hazards caused by catastrophe earthquake ground motion.