An early earthquake alarming method using seismic P-wave double-parameter based on Bayesian theory
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摘要: 为了及时、准确、可靠地发出地震预警信息，本文提出了一种基于贝叶斯理论的地震P波双参数预警方法。选取中国地震台网中心记录的四川地区的355条地震数据，统计P波触发后前3 s的P波平均周期τc和位移幅值Pd，结合贝叶斯理论建立震级和峰值加速度的预测模型，并以震级M4.5和峰值加速度为120 cm/s2为预警阈值，建立了地震危害性判别模型。与传统拟合方法进行对比仿真分析，并以汶川MS8.0地震为震例，进行地震危害性判别实验与分析。实验结果表明：本文提出的基于贝叶斯理论的地震 P 波双参数预警方法比传统拟合方法地震漏报率低 15.15%，可以快速、准确地估计震级与峰值加速度，并有效地评估地震的危害性，能够为地震监测预警提供数据支持和决策依据。Abstract: In order to send out earthquake alarm information timely, accurately and reliably, an early earthquake alarm method using P-wave double-parameter alarm method based on Bayesian theory is proposed in this paper. Based on 355 earthquake data recorded at Sichuan Province of China Earthquake Networks Center, the median period τc and ground motion amplitudes Pd in the first 3 seconds after P-wave triggering are calculated. Combined with Bayesian theory, the prediction model of magnitude and peak ground acceleration is established. Taking the magnitude M＝4.5 and PGA＝120 cm/s2 as the alarm threshold, the seismic hazard discrimination model is established. Compared with the traditional fitting method, the simulation analysis of earthquake early warming based on Bayesian theory, and then the Wenchuan MS8.0 earthquake is taken as an example to carry out the earthquake hazard identification. The experimentalresults show that early earthquake alarming method using seismic P-wave double-parameter based on Bayesian theory is 15.15% lower than the traditional fitting method, which can can quickly and accurately estimate the magnitude and peak acceleration, and effectively assess the hazard of earthquake, which can provide data support and decision-making basis for earthquake monitoring and alarming.
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Key words:
- earthquake alarm /
- Bayesian theory /
- magnitude prediction /
- PGA prediction
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图 1 选取地震的震中（a）和震中距、震级及地震记录数（b）的分布
Figure 1. Locations of earthquakes （a） and the number of earthquakes corresponding to different magnitudes and epicentral distances （b）
图 3 不同PGA的lgPGA概率密度分布
Figure 3. Probability density distributions of lgPGA with different PGA
图 5 实际震级与传统方法预测震级Mf和基于贝叶斯理论的预测震级Mb的比较（a）及其误差分布（b）
Figure 5. Comparison of value M with fitting predicted values Mf and Bayesian predictors Mb （a） and the error distribution （b） of predicted magnitude
图 6 真实值PGA与传统拟合预测值PGAf （a）和基于贝叶斯理论的预测值PGAb（b）的比较
Figure 6. Comparison of real values PGA with fitting predicted values PGAf （a） and Bayesian predictors PGAb （b）
图 7 基于贝叶斯理论的预测方法（a）和传统拟合方法（b）所得结果与真实数据判别结果的对比
Figure 7. Comparison of discrimination results by Bayesian theory method （a） and fitting method （b） with real data
表 1 实验数据与结果
Table 1. Experimental data and results
台站位置 贝叶斯理论预测结果 地震危害性判别结果 东经/° 北纬/° MS PGA/（cm·s−2） 贝叶斯方法 传统拟合方法 真实 102.20 29.90 7.03 79.43 大震远震 大震远震 大震远震 102.90 30.20 6.35 104.71 大震远震* 大震近震 大震近震 103.80 30.90 7.71 173.78 大震近震 大震近震 大震近震 102.90 30.10 7.11 107.15 大震远震 大震近震# 大震远震 102.20 28.30 6.91 53.70 大震远震 大震远震 大震远震 102.60 29.50 6.88 75.86 大震远震 大震远震 大震远震 102.10 29.40 6.02 69.18 大震远震 大震远震 大震远震 102.20 29.70 4.52 83.18 小震近震 小震近震 大震远震 103.50 30.60 3.00 138.04 小震近震* 小震近震* 大震近震 103.60 30.30 7.77 123.03 大震近震# 大震近震# 大震远震 102.40 29.30 7.94 85.11 大震远震 大震远震 大震远震 注：表中地震危害性判别结果中右上角标准为*的表示漏报；标注为#的表示误报。 
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