Realtime Selection of Optimal Source Parameters Using Ground Motion Envelopes

Dario Jozinović, John Clinton, F. Massin, Maren Böse, C. Cauzzi
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Abstract

It is increasingly common for seismic networks to operate multiple independent automatic algorithms to characterise earthquakes in real-time, such as in earthquake early warning (EEW) or even standard network practice. Commonly used methods to select the best solution at a given time are simple and use ad hoc rules. An absolute measure of how well a solution (event origin and magnitude) matches the observations by the goodness-of-fit between the observed and predicted envelopes is a robust and independent metric to select optimal solutions. We propose such a measure that is calculated as a combination of amplitude and cross-correlation fit. This metric can be used to determine when a preferred solution reaches an appropriate confidence level for alerting, or indeed to compare two (or more) different event characterisations directly. We demonstrate that our approach can also be used to suppress false alarms commonly seen at seismic networks. Tests using the 10 largest earthquakes in Switzerland between 2013 and 2020, and events that caused false alarms demonstrate that our approach can effectively prefer solutions with small errors in location and magnitude, and can clearly identify and discard false origins or incorrect magnitudes, at all time scales, starting with the first event characterisation.
利用地动包络实时选择最佳震源参数
在地震预警(EEW)甚至标准网络实践中,地震网络采用多种独立自动算法实时描述地震特征的做法越来越普遍。在给定时间内选择最佳解决方案的常用方法很简单,使用的是临时规则。通过观测包络与预测包络之间的拟合优度来绝对衡量解决方案(事件起源和震级)与观测结果的匹配程度,是选择最佳解决方案的可靠且独立的衡量标准。我们提出了这样一个指标,它是振幅和交叉相关拟合度的组合。该指标可用于确定首选解决方案何时达到适当的置信度以发出警报,或直接比较两个(或多个)不同的事件特征。我们证明,我们的方法还可用于抑制地震网络中常见的误报。使用 2013 年至 2020 年间瑞士发生的 10 次最大地震以及引起误报的事件进行的测试表明,我们的方法可以有效地优先选择位置和震级误差较小的解决方案,并能从第一次事件特征描述开始,在所有时间尺度上清晰地识别并摒弃错误的起源或不正确的震级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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