Real-Time Continuous Estimation of Seismic Source Rupture Characteristics Considering P-Wave Early Warning Parameters
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摘要: 在地震预警系统中引入震源破裂特征实时持续估测方法，可有效克服传统基于点源模型估测目标预警烈度和潜在破坏区的不足. 现有方法的实时性通常只能达到分钟级，无法满足地震预警系统的高时效性要求. 基于地震台站实时观测数据，通过引入地震预警P波特征参数，开展有限破裂模板匹配技术研究，形成了一套时效性更强的震源破裂特征实时估测方法. 测试结果表明：利用本方法在震后同一时刻得到的结果相对于有限破裂探测器（FinDer）算法结果在速度上要快3 s左右，个别震例结果要快5 s；破裂初期，由于受到地震辐射多样性、场地、传播路径等因素的影响，走向θ会存在较大的波动. 随着破裂的延展，θ逐渐收敛至参考值；对于M7.0级以下地震，震后6~10 s即可获得较稳定的破裂特征参数结果，而对于M7.0+地震，则需要更长的时间，尤其是类似于汶川8.0级这种特大地震，其结果在台网较为稀疏的情况下需到震后40 s才能逐渐稳定.Abstract: By introducing the real-time estimation method of seismic source rupture characteristics in to an earthquake early warning system (EEWS), we can effectively overcome the shortcomings of the traditional point-source-model-based estimation of target warning intensity and potential damage zones, and improve the disaster mitigation effectiveness of an EEWS. The real-time performance of existing methods is usually only at the minute level, which cannot meet the high timeliness requirements of EEWSs. In this work, we developed a real-time method to continuously estimate source rupture characteristics considering P-wave warning parameters. This method is an improvement of the finite rupture template matching method, namely FinDer. The system test results show that the results obtained using this method are about 3 s faster compared to the FinDer algorithm results at the same moment after the earthquake, and the results of individual earthquake cases are 5 s faster. Additionally, at the early stage of rupture, there are large fluctuations in the obtained strike θ due to the influence of seismic radiation diversity, site, propagation path and other factors. As the rupture continues, θ will gradually converge to the reference value. Moreover, for earthquakes of magnitude less than M7.0, relatively stable rupture characteristic parameter results can be obtained 6-10 s after the earthquake origin, while for M7.0+ earthquakes, it takes longer time, especially for mega-earthquakes such as the Wenchuan M8.0 event, whose results need to be gradually stabilized only at 40 s after the origin with relatively sparse station coverage.
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图 1 全P波段垂直向加速度预警参数PAall与PGA的拟合曲线（彭朝勇等，2021）
Fig. 1. Fitting curve of the vertical acceleration early-warning parameter PAall obtained from the full P wave window with PGA(Peng et al., 2021)
图 3 震后不同时刻四川汶川8.0级地震模拟结果与FinDer算法结果对比
Fig. 3. Comparison of simulation results of M8.0 Wenchuan, Sichuan earthquake at different moments after the origin with those obtained by the FinDer algorithm
图 4 震后不同时刻四川汶川8.0级地震模拟结果
Fig. 4. Simulation results of M8.0 Wenchuan, Sichuan earthquake at different moments after the origin.
图 5 震后不同时刻四川芦山7.0级地震模拟结果与FinDer算法结果对比
Fig. 5. Comparison of simulation results of M7.0 Lushan, Sichuan earthquake at different moments after the origin with those obtained by the FinDer algorithm
图 6 震后不同时刻青海门源6.9级地震模拟结果与FinDer算法结果对比
Fig. 6. Comparison of simulation results of M6.9 Menyuan, Qinghai earthquake at different moments after the origin with those obtained by the FinDer algorithm
图 7 日本熊本7.4级地震震后不同时刻模拟结果与FinDer算法结果对比
Fig. 7. Comparison of simulation results of M7.4 Kumamoto, Japan earthquake at different moments after the origin with those obtained by the FinDer algorithm
表 1 线下模拟所用4次破坏性地震事件信息
Table 1. The four damaging earthquakes used for off-line simulation
事件名 发震时刻 震中经度
（ºN）震中纬度
（ºE）深度
（km）震级
Ms走向θ
（º）破裂长度L（km） 四川汶川地震 2008-05-12
14∶28∶00103.42 31.01 14 8.0 231/51 290 四川芦山地震 2013-04-20
08∶02∶48102.99 30.30 17 7.0 218/38 40 青海门源地震 2022-01-08
01∶45∶28101.26 37.77 10 6.9 284/104 31 日本熊本地震 2016-04-16
01∶25∶17130.76 32.75 12 7.4 226/46 65 
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