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A Modified Algorithm to identify the strongest velocity pulse in three orthogonal components of ground motions
Soil Dynamics and Earthquake Engineering ( IF 4.2 ) Pub Date : 2021-04-08 , DOI: 10.1016/j.soildyn.2021.106749
Tianci Zhao , Boming Zhao

An algorithm for the identification of the strongest velocity pulse is proposed based on wavelet analysis, which considers three orthogonal components of the ground motion. According to the NGA-West2 database, 6288 strong motion records were analyzed, and further, the classification results were compared with those obtained from the existing identification method of Shahi and Baker (2014) [1]. Among the results, our algorithm classified more records as pulse-like ground motion and has expanded the pulse identifications. In other words, the proposed algorithm can reflect the three orthogonal characteristics of the original ground motion records. Besides, the relationships of pulse amplitude, period with magnitude and fault distance were analyzed. Based on the analysis of near-fault ground motions containing velocity pulses, the following conclusions can be made: (1) The peak of the strongest velocity pulse gradually reduces with the increase of the fault distance, while gradually rises with the increase of the magnitude. Comparing with the fault normal or fault parallel orientation component, the strongest velocity pulse component has larger energy and smaller peak velocity. (2) For the relationship between the velocity pulse period and magnitude, results show that when Mw < 7, the predicted value of our model is larger than that of the Shahi and Baker (2014) [1] model; however when Mw ≥ 7, the prediction results from these two models are fairly close. The difference between the two models decreases as magnitude increases. That is because the strongest velocity pulses occupied more in the horizontal direction under a large magnitude.



中文翻译:

一种在地面运动的三个正交分量中识别最强速度脉冲的改进算法

提出了一种基于小波分析的最强速度脉冲识别算法,该算法考虑了地震动的三个正交分量。根据NGA-West2数据库,分析了6288个强运动记录,并将其分类结果与通过Shahi和Baker(2014)的现有识别方法获得的分类结果进行了比较[1]。在结果中,我们的算法将更多记录归类为脉冲状地面运动,并扩展了脉冲识别。换句话说,该算法可以反映原始地面运动记录的三个正交特征。此外,分析了脉冲幅度,周期与幅度和故障距离之间的关系。根据对包含速度脉冲的近断层地面运动的分析,可以得出以下结论:(1)最强速度脉冲的峰值随着故障距离的增加而逐渐减小,而随着幅值的增加而逐渐增大。与故障法向或故障平行取向分量相比,最强的速度脉冲分量具有较大的能量和较小的峰值速度。(2)对于速度脉冲周期与幅度之间的关系,结果表明,当M w  <7,我们模型的预测值大于Shahi and Baker(2014)[1]模型的预测值;但是,当M w≥7时 ,这两个模型的预测结果相当接近。两个模型之间的差异随着幅度的增加而减小。这是因为最强的速度脉冲在水平方向上在较大的幅度上占据更多。

更新日期:2021-04-09
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