Peak Ground Displacement Saturates Exactly When Expected: Implications for Earthquake Early Warning
Peak Ground Displacement Saturates Exactly When Expected: Implications for Earthquake Early Warning
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峰值地面位移恰好在预期时饱和：对地震早期预警的影响
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
E. Cochran
中科院分区：
文献类型：
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作者：
D. Trugman;M. Page;S. Minson;E. Cochran
破裂性质与震级的比例关系对于地震预警系统至关重要，因为地震预警系统依赖于使用有限的波形数据快照进行震源表征。ShakeAlert是一个正在为美国西部开发的原型地震预警系统，它根据地震触发的台站测量的P波峰值地面位移，提供真实的地震震级估计。ShakeAlert中使用的算法假设每个台站的位移测量值在统计上是独立的，并且在对数峰值地面位移和地震震级之间存在线性和时间无关的关系。在这里，我们使用迄今为止为此目的收集的最大数据集来挑战这一基本假设：1997年至2018年发生在日本附近的M4.5至M9地震的140，000多个垂直分量波形的综合数据库，并由K‐NET和KiK‐net强震网络记录。通过分析这些地震的P波峰值地面位移的时间演化，我们发现，在震级位移标度中存在一个中断或饱和，这取决于测量时间窗口的长度。我们证明，这种饱和发生的幅度很好地解释了一个简单的和不确定的地震破裂增长模型。然后，我们使用该饱和模型的预测来开发一个贝叶斯框架，用于估计真实的时间震级估计中的后验不确定性。
The scaling of rupture properties with magnitude is of critical importance to earthquake early warning systems that rely on source characterization using limited snapshots of waveform data. ShakeAlert, a prototype earthquake early warning system that is being developed for the western United States, provides real‐time estimates of earthquake magnitude based on P wave peak ground displacements measured at stations triggered by the event. The algorithms used in ShakeAlert assume that the displacement measurements at each station are statistically independent and that there exists a linear and time‐independent relation between log peak ground displacement and earthquake magnitude. Here we challenge this basic assumption using the largest data set assembled for this purpose to date: a comprehensive database of more than 140,000 vertical‐component waveforms from M4.5 to M9 earthquakes occurring near Japan from 1997 through 2018 and recorded by the K‐NET and KiK‐net strong‐motion networks. By analyzing the time evolution of P wave peak ground displacements for these earthquakes, we show that there is a break, or saturation, in the magnitude‐displacement scaling that depends on the length of the measurement time window. We demonstrate that the magnitude at which this saturation occurs is well‐explained by a simple and nondeterministic model of earthquake rupture growth. We then use the predictions of this saturation model to develop a Bayesian framework for estimating posterior uncertainties in real‐time magnitude estimates.