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Associating earthquakes with faults using cluster analysis optimized by a fuzzy particle swarm optimization algorithm for Iranian provinces
Soil Dynamics and Earthquake Engineering ( IF 4.2 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.soildyn.2020.106433
Rezvan Ghasemi Nejad , Rahim Ali Abbaspour , Masoud Mojarab

Abstract Fault zones play an important role in seismic hazard analysis and are often selected in accordance with the judgments of experts, making them controversial and diverse in a specific region. The delineation of seismic sources has a great impact on the uncertainty in seismic hazard analysis. Applications of seismic hazard analysis demonstrate that a 3D approach, compared to a 2D one, may lead to more realistic hazard scenarios. Another problem with seismic hazard analysis is how to associate earthquakes with faults. This paper attempted to connect seismic events to faults by a fuzzy particle swarm optimization algorithm, an optimized version of the fuzzy clustering approach. In this algorithm, two objective functions were minimized: the distance of the events from the faults, and the distance to the centers of events’ clusters. The study areas included two provinces in southwestern and southeast Iran, namely Fars and Kerman, respectively. Ten main faults were recognized in the Fars Province and 13 main faults in the other study area, Kerman Province. Moreover, 1100 and 665 seismic events with the Mw ≥ 4 and a depth of 4 km–100 km (obtained by teleseismic depth assessment) were recorded from 1900 to 2011 in these two areas, respectively. Events were clustered and associated with the faults using the algorithm. A comparison of the results of the applied algorithm and those of the known and documented earthquakes revealed an accuracy of 85.3% and 75% for Fars and Kerman Provinces, respectively, when determining the associated fault.

中文翻译:

使用由伊朗省份的模糊粒子群优化算法优化的聚类分析将地震与断层相关联

摘要 断裂带在地震危险性分析中占有重要地位,往往根据专家的判断进行选择,使得断裂带在特定区域内具有争议性和多样性。震源的圈定对地震危险性分析的不确定性有很大影响。地震灾害分析的应用表明,与 2D 方法相比,3D 方法可能会导致更真实的灾害场景。地震危险性分析的另一个问题是如何将地震与断层联系起来。本文试图通过模糊粒子群优化算法(模糊聚类方法的优化版本)将地震事件与断层联系起来。在该算法中,两个目标函数被最小化:事件到断层的距离,以及到事件簇中心的距离。研究区域包括伊朗西南部和东南部的两个省,分别是法尔斯省和克尔曼省。在法尔斯省识别出 10 条主要断层,在另一个研究区克尔曼省识别出 13 条主要断层。此外,1900年至2011年这两个地区分别记录了1100次和665次Mw≥4、深度为4km~100km(远震深度评估获得)的地震事件。使用该算法将事件聚类并与故障相关联。在确定相关断层时,应用算法的结果与已知和记录的地震结果的比较显示,法尔斯省和克尔曼省的准确度分别为 85.3% 和 75%。在法尔斯省识别出 10 条主要断层,在另一个研究区克尔曼省识别出 13 条主要断层。此外,1900年至2011年这两个地区分别记录了1100次和665次Mw≥4、深度为4km~100km(远震深度评估获得)的地震事件。使用该算法将事件聚类并与故障相关联。在确定相关断层时,应用算法的结果与已知和记录的地震结果的比较显示,法尔斯省和克尔曼省的准确度分别为 85.3% 和 75%。在法尔斯省识别出 10 条主要断层,在另一个研究区克尔曼省识别出 13 条主要断层。此外,1900年至2011年这两个地区分别记录了1100次和665次Mw≥4、深度为4km~100km(远震深度评估获得)的地震事件。使用该算法将事件聚类并与故障相关联。在确定相关断层时,应用算法的结果与已知和记录的地震结果的比较显示,法尔斯省和克尔曼省的准确度分别为 85.3% 和 75%。分别。使用该算法将事件聚类并与故障相关联。在确定相关断层时,应用算法的结果与已知和记录的地震结果的比较显示,法尔斯省和克尔曼省的准确度分别为 85.3% 和 75%。分别。使用该算法将事件聚类并与故障相关联。在确定相关断层时,应用算法的结果与已知和记录的地震结果的比较显示,法尔斯省和克尔曼省的准确度分别为 85.3% 和 75%。
更新日期:2021-01-01
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