Bi Jinmeng, Song Cheng, Cao Fuyang
Tianjin Earthquake Agency, Tianjin, 300201, China.
Institute of Geophysics, China Earthquake Administration, Beijing, 100081, China.
Sci Rep. 2024 Sep 27;14(1):22170. doi: 10.1038/s41598-024-73815-9.
The accurate and impartial identification of background seismic activity is an important foundation for earthquake model construction and probabilistic seismic hazard analysis (PSHA). We use the Gardner and Knopoff, Reasenberg, nearest-neighbor and stochastic declustering methods to decluster in the North China Plain seismic belt, analyzing the effect of declustering. We optimize the input parameters of the algorithms through Kolmogorov-Smirnov Poisson distribution tests (e.g., the clustering threshold of the nearest-neighbor method is set to 0.6). Four algorithms demonstrate good declustering effects, removing the impact of the strong seismicity in Tangshan while retaining similar trends to all events. The occurrence of major earthquake events can significantly impact the declustering characteristics in terms of time, space and magnitude. There is a difference in the overall hazard between the four declustering algorithms and the Fifth Generation Seismic Ground Motion Parameters Zonation Map of China. The difference in seismic hazard curves is mainly influenced by the annual average occurrence rate (background rates) and the Gutenberg-Richter b value, with the effect of the b value being more pronounced. The analysis of the effect of algorithms and their impact on PSHA can provide reference for earthquake risk assessment, engineering seismic design and disaster research.
准确且公正地识别背景地震活动是地震模型构建和概率地震危险性分析（PSHA）的重要基础。我们使用加德纳和诺波夫方法、雷森伯格方法、最近邻方法以及随机去簇方法对华北平原地震带进行去簇处理，并分析去簇效果。我们通过柯尔莫哥洛夫 - 斯米尔诺夫泊松分布检验来优化算法的输入参数（例如，将最近邻方法的聚类阈值设置为0.6）。四种算法均展现出良好的去簇效果，消除了唐山强震活动的影响，同时保留了所有事件的相似趋势。重大地震事件的发生在时间、空间和震级方面会显著影响去簇特征。四种去簇算法与《中国地震动参数区划图（第五代）》的总体危险性存在差异。地震危险性曲线的差异主要受年平均发生率（背景发生率）和古登堡 - 里希特b值影响，其中b值的影响更为显著。算法效果及其对PSHA影响的分析可为地震风险评估、工程抗震设计和灾害研究提供参考。
