地震分布非均匀性指标Kcv值及其在亚洲大地震预测中的应用研究

地震 ›› 2011, Vol. 31 ›› Issue (3): 27-36.

地震分布非均匀性指标K_cv值及其在亚洲大地震预测中的应用研究

邱玉荣¹, 王晓青¹, 郑友华¹, 丁香¹, 李智²

1.中国地震局地震预测研究所, 北京 100036;
2.辽宁省地震局, 辽宁沈阳 110031

收稿日期:2010-10-21 修回日期:2011-01-28 发布日期:2021-09-09
作者简介:邱玉荣(1986-), 女, 宁夏中卫人, 在读硕士研究生, 主要从事地震中长期预测研究。
基金资助:
国家科技支撑计划重点课题(2008BAC44B02)资助

Heterogeneity Parameter K_cv of Earthquake Spatial Distribution and its Preliminary Application in Forecasting Great Earthquakes in Asia

QIU Yu-rong¹, WANG Xiao-qing¹, ZHENG You-hua¹, Ding Xiang¹, Li Zhi²

1. Institute of Earthquake Science, CEA, Beijing 100036, China;
2. Earthquake Administration of Liaoning Province, Shenyang 110031, China

Received:2010-10-21 Revised:2011-01-28 Published:2021-09-09

摘要/Abstract

摘要： 理论研究和实际预测表明地震空间分布非均匀性指标C_v值是一种有效的预测指标。但目前使用的C_v值在同一信度水平下, 其置信区间大小与空间分布的事件样本数大小有关, 不便于结果的分析比较。本文在C_v值基础上定义了一个新的预测指标K_cv, 并基于强震时空概率增益预测模型及其单项预测方法预测效能检验的方法, 依据亚洲地震重点研究区近20年来的地震活动空间分布非均匀性的研究与震例统计, 建立了该方法预测大地震的经验概率增益谱与预测效能谱。在建立K_cv值预测地震的方法基础上, 通过对2000—2009年研究区M_W6.9以上地震发生概率和概率增益值预测并与实际地震发生情况的回溯性检验, 探讨了本方法预测大地震的实际效果, 在此基础上预测了2010—2019年亚洲重点研究区M_W6.9以上地震发生的概率和概率增益。

关键词: K_cv值, Monte Carlo法, 预测效能, 概率增益, 亚洲地区

Abstract: The heterogeneity parameter C_v of spatial distribution of earthquake is an effective predictor of earthquakes. However, comparative analysis of their results is not very easy due to the uncertain relationship between the size of confidence interval and the size of the sample events of spatial distribution of C_v-value during the same level of reliability, which can impair its utility. In the present study, we defined a new parameter K_cv based on the parameter C_v. Further, according to the spatial-temporal probability gain model and the assessment method of efficiency of earthquake prediction methods, the empirical earthquake probability gain spectrum and efficiency spectrum of this novel method were calculated using the heterogeneity research and earthquake case statistics over the past 20 years. The robustness and effectiveness of K_cv-value method were further evaluated by analyzing its probability gain values, the probability of ≥M_W6.9 earthquake occurrences from 2000 to 2009 and their actual test of retrospective predictions. Our approach provides a way of predicting the ≥M_W6.9 earthquake probability and probability gain from 2010 to 2019, and providing a reference to determine Asian earthquake risk zones in the next decade.

Key words: K_cv-Value, Monte Carlo, Prediction performance, Probability gain, Asia

中图分类号:

P315.7

邱玉荣, 王晓青, 郑友华, 丁香, 李智. 地震分布非均匀性指标K_cv值及其在亚洲大地震预测中的应用研究[J]. 地震, 2011, 31(3): 27-36.

QIU Yu-rong, WANG Xiao-qing, ZHENG You-hua, Ding Xiang, Li Zhi. Heterogeneity Parameter K_cv of Earthquake Spatial Distribution and its Preliminary Application in Forecasting Great Earthquakes in Asia[J]. EARTHQUAKE, 2011, 31(3): 27-36.

参考文献

[1] 王晓青, 陈学忠, 李志雄, 等. 地震前兆群体空间非均匀性指标C_v值研究[J]. 中国地震, 1999, 15(3): 199-209.
[2] 陈学忠, 王晓青, 李志雄, 等. 强震前短临地震前兆时空分布非均匀性变化特征[J]. 地震学报, 2000, 22(1): 27-34.
[3] 杨明波, 宋治平, 王力, 等. 水化异常空间非均匀性指标C_v值的映震效能[J]. 华南地震, 2000, 20(1): 24-27.
[4] 鲁跃, 王晓青, 王科英. 短临地震前兆空间非均匀性的定量研究[J]. 地震地磁观测与研究, 2000, 21(3): 44-50.
[5] 王凯英, 马胜利, 刘力强, 等. 地震前兆时空非均匀性指标C_v值的实验检验[J]. 地震学报, 2002, 24(1): 82-89.
[6] 王晓青, 傅征祥. 中长期时空增益综合预测模型及其初步应用[J]. 地震学报, 2000, 22(1): 45-53.
[7] Wang X, Fu Z, Zhang L, et al. Spatial and temporal synthesized probability gain for middle and long-term earthquake forecast and its preliminary application[J]. Acta Seismologica Sinica, 2000, 13(1): 50-60.
[8] 王晓青. R值用于地震预测效能评估中的问题与改进[J]. 中国地震, 2000, 16(3): 256-262.
[9] 王晓青, 吕金霞, 丁香. 强震时-空综合概率增益模型与中长期预测方法效能研究[J]. 中国地震, 2002, 18(4): 346-355.
[10] 王晓青, 邵成辉, 丁香. 地震预测模型优化方法研究[J]. 地震, 2004, 24(2):
[11] 王晓青, 吕金霞, 丁香. 地震预测效能谱、增益谱的建立及其意义[A]. 中国地球物理学会第二十一届年会论文集[C]. 北京: 地震出版社, 2005. 112-116.
[12] 王晓青, 张国民, 傅征祥, 等. 2006—2020年中国大陆强震危险区综合预测研究[A]. 2006—2020年我国地震危险性与灾害损失预测研究[C]. 北京: 地震出版社, 2007. 219-226.
[13] Vere-Jones D. Stochastic models for earthquake occurrence[J]. Journal of the Royal Statistical Society. Series B (Methodological), 1970, 32(1): 1-62.
[14] Ogata Y. Statistical models for earthquake occurrences and residual analysis for point processes[J]. Journal of the American Statistical Association, 1988, 83(401): 9-27.
[15] NEIC: http://earthquake.usgs.gov/regional/neic