彭朝勇,郑钰,徐志强,姜旭东,杨建思. 2021. 面向地震烈度仪的现地地震动预测模型的构建与验证. 地震学报,43(5):643−655. doi: 10.11939/jass.20210075
引用本文: 彭朝勇,郑钰,徐志强,姜旭东,杨建思. 2021. 面向地震烈度仪的现地地震动预测模型的构建与验证. 地震学报,43(5):643−655. doi: 10.11939/jass.20210075
Peng C Y,Zheng Y,Xu Z Q,Jiang X D,Yang J S. 2021. Construction and verification of onsite ground motion prediction models for seismic intensity instrument. Acta Seismologica Sinica43(5):643−655. doi: 10.11939/jass.20210075
Citation: Peng C Y,Zheng Y,Xu Z Q,Jiang X D,Yang J S. 2021. Construction and verification of onsite ground motion prediction models for seismic intensity instrument. Acta Seismologica Sinica43(5):643−655. doi: 10.11939/jass.20210075

面向地震烈度仪的现地地震动预测模型的构建与验证

Construction and verification of onsite ground motion prediction models for seismic intensity instrument

  • 摘要: 利用初期P波预警参数构建现地地震动预测模型,使其在达到设定阈值时快速发出报警信息,是现地地震预警系统面临的一个关键问题,直接关系到发布信息的准确性和及时性。针对地震烈度仪基于微机电系统传感器记录到的数据质量较差,通过两次积分获取的位移存在较大偏差,会引起更多的误报和漏报,本文采用不同阶数(1—4阶)的巴特沃斯滤波器,分别构建了基于P波3 s和全P波段数据的位移幅值PD、速度幅值PV和加速度幅值PA与地震动峰值速度PGV和峰值加速度PGA的现地地震动预测模型,然后利用收集到的川滇示范预警网地震事件记录进行验证。结果表明,对于地震烈度仪微机电系统传感器的记录,采用1阶巴特沃斯滤波器处理、基于全P波段波形拟合获取到的PV与PGV的相关性和PA与PGA的相关性为两种最优现地地震动预测模型。具体应用时,应同时利用两种或两种以上的统计关系进行现地地震动预测,并将实际地震动观测值作为额外的判定条件,以降低误报率和漏报率。

     

    Abstract: Using the initial P-wave early warning parameters to construct onsite ground motion prediction models, so as to quickly release an alarm message when it reaches the predefined threshold, is a key issue of the onsite earthquake early warning system, which is directly related to the accuracy and timeliness of the early warning information. For micro-electro-mechanical-systems-based seismic intensity instrument with poor data quality, the obtained displacement record after two integrations has a large deviation, which will lead to more false and missed alarms. Therefore, for waveforms recorded by seismic intensity instrument, in this paper, we adopted Butterworth filters of different orders (1−4) to build up several onsite ground motion prediction models based on the P-wave 3 seconds data and the whole P-wave window. These models are the relationships between displacement amplitude PD and peak ground velocity PGV, PD and peak ground acceleration PGA, velocity amplitude PV and PGV, PV and PGA, acceleration amplitude PA and PGV, PA and PGA, respectively. The models are then verified using the collected micro-electro-mechanical-systems-based seismic event records from the Sichuan-Yunnan Demonstration Early Warning Network. The results show that for the seismic intensity instrument records, the two optimal onsite ground motion prediction models are the relationship between PV and PGV and the one between PA and PGA obtained by the first-order Butterworth filter processing and derived from the whole P-wave window. In specific applications, two or more statistical relationships should be simultaneously adopted to predict onsite ground motion, and observed ground motion values should be used as additional judgment conditions to reduce the probability of false and missed alarms.

     

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