基于贝叶斯方法的首都圈地区完整性震级评估  

Bayesian estimation of completeness magnitude in the Capital Circle Region

作　　者：张帆[1,3] 韩晓明[2] 包金哲[2] 杨晓忠 Zhang Fan;Han Xiaoming;Bao Jinzhe;Yang Xiaozhong(College of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China;Earthquake Agency of Inner Mongolia Autonomous Region,Hohhot 010051,China;College of Mathematics and Physics,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学控制与计算机工程学院,北京102206 [2]内蒙古自治区地震局,呼和浩特010051 [3]华北电力大学数理学院,北京102206

出　　处：《地震学报》2025年第1期107-121,共15页Acta Seismologica Sinica

基　　金：内蒙古自治区地震局局长基金(2023GG02);内蒙古自治区自然科学基金(2020MS04004)联合资助。

摘　　要：地震目录的完整性评估是地震活动性分析的基础性工作,常用的基于地震目录的评估方法未能考虑台站信息,对于少震地区无法给出评估结果,并且受到主观选取的计算参数的影响。本文采用基于贝叶斯统计的完整性震级(BMC)评估方法,对2010年至2023年期间中国地震台网记录的首都圈地区的地震目录进行分析,通过迭代优化获得计算最小完整性震级M_(C)的最优扫描半径和先验的MC模型,根据数据误差的高斯分布特征推导出M_(C)的先验和似然的概率分布,最后得到M_(C)的后验估计。BMC将地震台站分布的先验信息与局部观测值相结合,权重由各自的不确定性决定,给出了少震区域的MC估计值,并且降低了结果的不确定性。评估结果显示,2010年至今首都圈地区地震监测能力较强,但M_(C)的空间分布不均匀,监测能力最强的地区其2023年整体的完整性震级的变化,结果显示首都圈的地震监测能力逐渐提升,2010年后提升较显著。此外,本文还对比了最大曲率法(MAXC)、拟合优度法(GFT)和中位数分段斜率方法(MBASS)在研究区域的结果,认为方法的选择和计算参数对评估结果有不同程度的影响。Earthquake catalog completeness,crucial for seismicity analyses,is defined by the completeness magnitude(MC):The lowest magnitude at which all earthquakes are reliably detected.Accurate MC estimation is essential for seismic hazard assessments and earthquake studies.Traditional methods often solely rely on the frequency-magnitude distribution(FMD)and Gutenberg-Richter(G-R)law,neglecting station coverage,making them unsuitable for regions with low seismicity and susceptible to the subjective selection of calculation parameters.This study utilizes the Bayesian magnitude of completeness(BMC)method to analyze the Capital Circle Region of China’s(37°N—42°N,114°E—120°E)earthquake catalog from 2010 to 2023,a period marked by significant network upgrades.Initially,we assessed the overall catalog completeness from 1966 to 2023 using the maximum curvature(MAXC)method,and the results revealed improved monitoring capabilities,especially after 2010,with MC consistently between 0.5 and 1.5.Focusing on 2010—2023(23546 events,153 stations),we employed the BMC method with a two-step process:①Optimizing spatial resolution and prior model parameters based on the relationship between MC and station density(distance to the k-th nearest station);②Integrating prior information with observed M_(C) values using Bayesian inference.Iterative optimization yielded the optimal scanning radius and prior M_(C) model,from which assuming Gaussian-distributed errors,prior and likelihood distributions were derived,leading to a posterior M_(C) estimate;the BMC method integrates station distribution priors with local MC observations,weighted by their uncertainties,enabling M_(C) estimation in low-seismicity regions and reducing overall uncertainty.The optimized scanning radius R varied spatially,smaller in densely instrumented areas like Beijing.The prior model of Capital Circle Region differed significantly from that of Taiwan,highlighting the need for region-specific models.Posterior M_(C) estimates from BMC showed reduced uncertain

关 键 词：完整性震级 首都圈 BMC方法 贝叶斯估计 

分 类 号：P315.01[天文地球—地震学]