Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China.
Sensors (Basel). 2021 May 18;21(10):3519. doi: 10.3390/s21103519.
The largest possible earthquake magnitude based on geographical characteristics for a selected return period is required in earthquake engineering, disaster management, and insurance. Ground-based observations combined with statistical analyses may offer new insights into earthquake prediction. In this study, to investigate the seismic characteristics of different geographical regions in detail, clustering was used to provide earthquake zoning for Mainland China based on the geographical features of earthquake events. In combination with geospatial methods, statistical extreme value models and the right-truncated Gutenberg-Richter model were used to analyze the earthquake magnitudes of Mainland China under both clustering and non-clustering. The results demonstrate that the right-truncated peaks-over-threshold model is the relatively optimal statistical model compared with classical extreme value theory models, the estimated return level of which is very close to that of the geographical-based right-truncated Gutenberg-Richter model. Such statistical models can provide a quantitative analysis of the probability of future earthquake risks in China, and geographical information can be integrated to locate the earthquake risk accurately.
在地震工程、灾害管理和保险中,需要根据地理位置特征确定特定重现期内的最大可能地震震级。基于地面观测和统计分析的地震预测可以提供新的见解。在这项研究中,为了详细研究不同地理区域的地震特征,我们使用聚类方法根据地震事件的地理位置对中国大陆进行地震分区。结合地理空间方法,使用统计极值模型和右截断古登堡-里希特模型对聚类和非聚类情况下中国大陆的地震震级进行了分析。结果表明,与经典极值理论模型相比,右截断峰超阈值模型是一种相对最优的统计模型,其估计的重现期水平非常接近基于地理的右截断古登堡-里希特模型。这些统计模型可以对中国未来地震风险的概率进行定量分析,并可以整合地理信息以准确定位地震风险。