LSTM neural network-based onsite seismic intensity real-time prediction model:taking the JMA intensity as an example
The accurate and timely estimation of ground motion at target sites is a long-term and arduous task in earthquake early warning research.Commonly employed methods in earthquake early warning,such as ground motion prediction equation-based methods and on-site methods,suffer from large uncertainties as a result of the limited amount of information used in estimations.Here,we propose a long short-term memory(LSTM)neural network method for the continuous prediction of the intensity at a single station.The proposed model inputs four sequential features(the energy,energy increase rate,predominant period of seismic waves,and hypocentral distance of the station)and outputs the predicted maximum seismic intensity at the station.The proposed model was trained with records from 101 earthquake events and tested with records from 94 earthquake events.The results show that when using a 3-second sequence length for prediction,the overestimation ratio is 1.51%and the underestimation ratio is 4.00%;Moreover,as the sequence length increases,the proportion of overestimation and underestimation also decreases.The timeliness of the proposed model is evaluated by the lead-time that high intensity(intensity great than 4.5)is correctly predicted before the intensity 4.5 is observed.The result show that the lead-time increases with the increase of hypocentral distance.For large earthquakes,the proposed model can provide at least 20 s for end users located in far-field.
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