Effect of Declustering Algorithm in Probabilistic Seismic Hazard Analysis for Kathmandu Valley

Pawan Chhetri, D. Chamlagain, R. Tiwari
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Abstract

An earthquake is a random event with uncertainty on time of occurrence, size of events, and location of rupture. So earthquake phenomenon is a model based on Poisson’s distribution. Thus, all the dependent event like foreshock and aftershock has to be removed before performing any Probabilistic Seismic Hazard Analysis (PSHA). Among the number of methodologies proposed for declustering methods, the process proposed by Gardener and Knopoff (1974) and Reasenberg (1985) was mostly used due to the availability of source code and reliability of declustering work. In this study, an attempt has been made to compare the effect of the declustering process at hazard level for the scenario of Kathmandu Valley. Probabilistic seismic hazard analysis of Kathmandu valley is performed using RCRISIS software with different seismicity parameters calculated for without declustering, declustering using Gardener and Knopoff (1974), and declustering using Reasenberg (1985). Calculated PGA value at bedrock level for 760 years return period for three declustering models compared with the measured square root of the sum of square (SRSS) PGA by 2015 Gorkha earthquake at Kathmandu valley at KTP station. Obtained results highlighted the suitability of the Gardener and Knopoff source code for the study area.
聚类算法在加德满都谷地概率地震灾害分析中的作用
地震是一种随机事件,在发生的时间、大小和破裂的位置上都具有不确定性。因此,地震现象是一个基于泊松分布的模型。因此,在进行任何概率地震危害分析(PSHA)之前,必须去除所有相关事件,如前震和余震。在众多提出的解聚方法中,由于源代码的可用性和解聚工作的可靠性,gardner和Knopoff(1974)和Reasenberg(1985)提出的过程被广泛使用。在本研究中,试图比较加德满都谷地情景在危险水平上的聚类过程的影响。使用RCRISIS软件对加德满都谷地进行概率地震危险性分析,在不进行聚类的情况下计算不同的地震活度参数,使用Gardener和Knopoff(1974)进行聚类,使用Reasenberg(1985)进行聚类。三种聚类模式760年回归期基岩水平PGA值与2015年廓尔喀地震在KTP站实测的平方和平方根(SRSS) PGA值的比较获得的结果强调了Gardener和Knopoff源代码对研究区域的适用性。
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