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

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

As part of the total solution of seismic hazard mitigation, the on-site earthquake early warning system(EEWS) is 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 resulted to a high nonlinear function. The neural networks were used to model these nonlinearities and the learning techniques were developed for the analysis of earthquake seismic signal. This warning system was designed to analyze the first-arrival from the three components of the earthquake signals in as early as 3 seconds after the ground motion is felt by the sensors. Then, the EEWS instantaneously provide a profile consists of parameters including the time until peak ground acceleration(PGA) and maximum seismic intensity. The neural network based system was 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 hazard caused by earthquake ground motion.