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地震 ›› 2022, Vol. 42 ›› Issue (3): 111-123.doi: 10.12196/j.issn.1000-3274.2022.03.008

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逻辑树在辽宁地区地震危险性分析中的应用

王岩, 邵媛媛, 张博, 郭晓燕, 翟丽娜, 杨士超   

  1. 辽宁省地震局, 辽宁 沈阳 110034
  • 收稿日期:2021-03-04 修回日期:2022-02-19 出版日期:2022-07-31 发布日期:2023-03-29
  • 作者简介:王岩(1983-), 女, 辽宁本溪人, 高级工程师, 主要从事地震活动性与综合预测研究。
  • 基金资助:
    地震科技星火计划攻关项目(XH23010A)

Application of Logic Tree in Seismic Risk Analysis of Liaoning Province

WANG Yan, SHAO Yuan-yuan, ZHANG Bo, GUO Xiao-yan, ZHAI Li-na, YANG Shi-chao   

  1. Earthquake Administration of Liaoning Province, Shenyang 110034, China
  • Received:2021-03-04 Revised:2022-02-19 Online:2022-07-31 Published:2023-03-29

摘要: 地震预测研究的主要依据是震前观测到的各类地震信息的异常变化, 观测、 计算得出的异常变化越多, 地震危险性研究的可用信息越完善, 同时, 越来越多的不同异常对地震危险性判定结果的不一致, 增加了综合分析的复杂度。 本文以辽宁地区为研究区域, 引入逻辑树方法, 按照地震孕育过程以长、 中、 短、 临的时间尺度分层, 建立地震危险性逻辑树模型, 扫描全域得到辽宁地区地震危险性相对概率分布和辽宁地区地震危险性指数分布, 定量化表现异常对区域地震危险性的综合影响, 简化分析过程。 通过1999年岫岩5.4级地震和2013年灯塔5.1级地震两个典型震例的计算发现, 异常信息的完整性、 异常时间尺度的精确性对地震事件的预测判定准确度影响较大。

关键词: 地震危险性, 地震异常特征, 逻辑树方法, 不确定性

Abstract: The main basis of earthquake prediction research is the abnormal changes of various seismic information observed before the earthquake. The more abnormal changes, the more complete the available information for seismic risk research. However, more and more different anomalies are inconsistent with the seismic risk judgment results, increasing the complexity of comprehensive analysis. In this paper, taking Liaoning region as the research area, the logic tree method is introduced, and the seismic risk logic tree model is established according to the long, medium, short and imminent time scales of the earthquake preparation process. Then we scan the whole region to obtain the relative probability distribution of seismic risk in Liaoning and the distribution of seismic risk index in Liaoning. The introduction of logic tree method quantifies the comprehensive influence of anomalies on regional seismic risk, and simplifies the analysis process. The Xiuyan M5.4 earthquake in 1999 and the Dengta M5.1 earthquake in 2013 were analyzed as typical examples, the results showed that if we could collect more earthquake anomalies, and the influence time of the anomalies were more accurate, then we would get more accurate earthquake prediction results.

Key words: Seismic risk, Characteristics of uncertainty, Seismic anomalies, Logic tree method

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