Predicting instrumental seismic intensity using deep learning and physical features
Rapidly and accurately predicting instrumental seismic intensity at recording sites is essential for providing timely and reliable alerts to potential users during earthquake early warning(EEW).This paper proposes an algorithm that combines deep learning with physics features to predict instrumental seismic intensity more reliably.A total of 3386 seismic events recorded by the K-NET and KiK-net strong-motion seismograph networks in Japan from 2001 to 2021 were collected and processed as research samples.25714 three-channel seismic waveform was obtained after pre-processing of these seismic events,which included interception,baseline correction and quality screening.Using 18000 seismic records(90％for training and 10％for validation),a 3 s window length instrumental seismic intensity prediction model(CNN-PP)was constructed,and 7714 seismic records were tested.The results show that the CNN-PP model outperforms the traditional single feature-parameter model in predicting instrumental seismic intensity.In addition,the offline earthquake case test results show that the CNN-PP model's successful alarm rate reaches 95.03％without false alarms,which provides a potential method for solving the problem of EEW instrumental seismic intensity determination.
万方数据
维普
