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Quantifying nuisance ground motion thresholds for induced earthquakes
Earthquake Spectra ( IF 3.1 ) Pub Date : 2021-01-25 , DOI: 10.1177/8755293020988025
Ryan Schultz 1 , Vince Quitoriano 2 , David J Wald 2 , Gregory C Beroza 1
Affiliation  

Hazards from induced earthquakes are a growing concern with a need for effective management. One aspect of that concern is the “nuisance” from unexpected ground motions, which have the potential to cause public alarm and discontent. In this article, we borrow earthquake engineering concepts to quantify the chance of building damage states and adapt them to quantify felt thresholds for induced earthquakes in the Central and Eastern United States. We compare binary data of felt or not-felt reports from the “Did You Feel It” database with ShakeMap ground motion intensity measures (IM) for ∼360 earthquakes. We use a Monte Carlo logistic regression to discern the likelihood of perceiving various degrees of felt intensity, given a particular IM. These best-fit nuisance functions are reported in this article and are readily transferable. Of the shaking types considered, we find that peak ground velocity tends to be the best predictor of a felt earthquake. We also find that felt thresholds tended to decrease with increasing earthquake magnitude, after M ∼3.9. We interpret this effect as related to the duration of the event, where events smaller than M 3.9 are perceived as “impulsive” to the human senses. Improved quantification of the nuisance from induced earthquake ground motions could be utilized in management of the public perception of their causal operations. Although aimed at anthropogenic earthquakes, thresholds we derive could be useful in other realms, such as establishing best practices and protocols for earthquake early warning.



中文翻译:

量化诱发地震的有害地面运动阈值

诱发地震的危害越来越引起人们的关注,需要进行有效管理。这种担忧的一个方面是意外地面运动的“滋扰”,它有可能引起公众的警觉和不满。在本文中,我们借用地震工程学概念来量化建筑物破坏状态的机会,并使其适应量化以量化美国中部和东部引发地震的感觉阈值。我们将“ Did You Feel It”数据库中的有毛毡或无毛毡报告的二进制数据与ShakeMap约360次地震的地面运动强度度量(IM)进行了比较。在给定特定IM的情况下,我们使用Monte Carlo logistic回归来识别感知不同程度的感觉强度的可能性。这些最合适的烦人功能已在本文中进行了报告,并且易于转让。在考虑的振动类型中,我们发现峰值地面速度往往是毡状地震的最佳预测指标。我们还发现,在M〜3.9之后,感觉阈值倾向于随地震强度的增加而降低。我们认为这种影响与事件的持续时间有关,其中小于M 3.9的事件被认为对人类感官“具有冲动”。可以更好地量化由诱发的地震地面运动引起的干扰,以管理公众对其因果关系的看法。尽管针对人为地震,我们得出的阈值可能在其他领域也很有用,例如为地震预警建立最佳实践和协议。我们还发现,在M〜3.9之后,感觉阈值倾向于随地震强度的增加而降低。我们认为这种影响与事件的持续时间有关,其中小于M 3.9的事件被认为对人类感官“具有冲动”。可以更好地量化由诱发的地震地面运动引起的干扰,以管理公众对其因果关系的看法。尽管针对人为地震,我们得出的阈值可能在其他领域也很有用,例如为地震预警建立最佳实践和协议。我们还发现,在M〜3.9之后,感觉阈值倾向于随地震强度的增加而降低。我们认为这种影响与事件的持续时间有关,其中小于M 3.9的事件被认为对人类感官“具有冲动”。可以更好地量化由诱发的地震地面运动引起的干扰,以管理公众对其因果关系的看法。尽管针对人为地震,我们得出的阈值可能在其他领域也很有用,例如为地震预警建立最佳实践和协议。可以更好地量化由诱发的地震地面运动引起的干扰,以管理公众对其因果关系的看法。尽管针对人为地震,我们得出的阈值可能在其他领域也很有用,例如为地震预警建立最佳实践和协议。可以更好地量化由诱发的地震地面运动引起的干扰,以管理公众对其因果关系的看法。尽管针对人为地震,我们得出的阈值可能在其他领域也很有用,例如为地震预警建立最佳实践和协议。

更新日期:2021-01-25
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