Optimization algorithm of earthquake early warning based on multi-station high frequency GPS
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摘要: 随着精密定位技术的发展，高频GPS已能够精确记录地表位移数据，研究高频GPS能为地震预警工作做出一定补充. 针对目前地震预警中单站预警误报率高的问题引入深度学习技术，利用长短期记忆网络(LSTM)联合周边区域台站对单台站进行预警以达到减少误报的目的. 首先通过对新西兰南部地区1 Hz高频GPS数据进行解算得到多个台站无震时间序列，再利用该数据训练网络得到融合区域特征的高精度模型. 该模型可以对无震时间序列进行预测并动态制定阈值区间，当实际观测值超出置信区间则判定异常. 通过与传统短时窗平均/长时窗平均算法(STA/LTA)及未融合区域特征的单站模型进行对比，结果表明：融合区域特征的单站模型可有效减少误报，在多个台站的无震长序列上较传统方法表现优异，具有一定的应用价值.Abstract: 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.
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表 1 高频GPS台站信息表
台站 经度/(°) 纬度/(°) 震中距/km MRBL 172.7752 –42.6618 24.665 WITH 173.9843 –41.5606 149.984 WEST 171.8062 –41.7447 150.339 
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表 3 不同方法检测所用时间及误报数
方法 台站误报数 所用时间/ms MRBL WITH WEST HANM KAIK LKTA 本文模型
(引入区域特征)0 0 1 0 0 0 31.71 单台站模型
(未引入区域特征)1 2 2 2 1 1 23.59 STA/LTA 9 4 5 5 4 4 0.73
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表 4 网络架构及参数表
方法 GPS台站 网络架构 优化算法 依赖时间/s batch size RMSE 本文模型
(引入区域特征)MRBL
WITH
WESTLSTM : 128
Dropout : 0.2
Dense : 1
Linear ActivationAdam 30 64 0.002 989
0.003 018
0.003 056单台站模型
(未引入区域特征)MRBL
WITH
WESTLSTM : 64
Dropout : 0.5
LSTM : 32
Dropout : 0.5
Dense : 1
Linear ActivationAdam 30 128 0.003 258
0.003 345
0.003 261
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