Seismic intensity is a key output of earthquake early warning(EEW)system.How to quickly predict the seismic intensity at the target site is a core issue of EEW related techniques.This paper proposes a Long Short-Term Memory(LSTM)neural network-based instrumental seismic intensity prediction model(LSTM-I).The model takes the sequential features of ground motion parameters observed at a station as model input,and continuously predicts the potential maximum intensity at the station.The model was trained using 5103 strong ground motion acceleration recordings of 102 earthquakes from K-NET in Japan.The generalization of the model was tested with the 3781 recordings of 89 earthquakes.The performance of the LSTM-I model is evaluated by three metrics:precision rate,false alarm rate and missed alarm rate.The results indicate that if the first 3.0 s time series segment after the P wave onsets is employed,the probability of the missed alarm is 46.78％,and the probability of false alarm is 1.25％;While the first 10.0 s time series segment is employed,the probability of missed alarm significantly reduces to 17.6％,and the probability of false alarm reduces to 1.14％.The results verify that LSTM-I model has well captured hidden characteristic of the ground motion sequential time series.Furthermore,the lead-time at station with potential Ⅵ degree intensity is evaluated,and the lead-time is close to that of P-S wave arrival time difference,which indicates high timeliness of LSTM-I model.
关键词
地震预警/时间序列特征/LSTM神经网络/仪器地震烈度/预测
Key words
Earthquake early warning/Time series characteristics/LSTM neural network/Instrumental seismic intensity/Prediction
维普
万方数据