Optimization algorithm of earthquake early warning based on multi-station high frequency GPS

With the development of precision positioning technology, high-frequency GPS has been able to accurately record surface displacement data. Research on high frequency GPS can make a certain supplement to earthquake early warning. In view of the high false alarm rate of single station in earthquake early warning, we introduce deep learning technology and use the long short-term memory (LSTM) neural network to combine with surrounding stations to give early warning to single station. First, the seismic-free time series of multiple stations are obtained by solving the 1 Hz high-frequency GPS data in the southern region of New Zealand Then the data is used to train the network to obtain a high-precision model that integrates regional features. The model can predict the seismic-free time series and dynamically formulate a threshold interval. When the actual observation value exceeds the confidence interval, an abnormality is determined. By comparing with the traditional short-time window averaging/long-time window averaging algorithm (STA/LTA) and the single station model without regional features, the results show that the single station model fusing regional features can effectively reduce false alarms. It performs better than traditional methods on seismic-free long sequences of multiple stations and has certain application values.