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地震 ›› 2002, Vol. 22 ›› Issue (3): 2-14.

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关于地震丛集特征、成因及临界状态的讨论

罗灼礼1, 孟国杰2   

  1. 1.中国地震局,北京 100036;
    2.中国地震局分析预报中心,北京 100036
  • 收稿日期:2002-05-09 修回日期:2002-05-15 出版日期:2002-07-31 发布日期:2021-12-21
  • 作者简介:罗灼礼(1957-),男,广东大埔人,研究员,1997年博士生导师,主要从事地震学、地震预报等研究。
  • 基金资助:
    “九五”中国地震局科研攻关项目(95-04-05)

Discussion on the Characteristics of Earthquake Clustering, Its Causes and Critical State

LUO Zhuo-li1, MENG Guo-jie2   

  1. 1. Science and Technology Committee, CSB, Beijing 100036, China;
    2. Center for Analysis and Prediction, CSB, Beijing 100036, China
  • Received:2002-05-09 Revised:2002-05-15 Online:2002-07-31 Published:2021-12-21

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摘要： 给出了研究地震临界丛集现象的最新方法,即时间变异诊断方法。讨论了不同类型的地震丛集活动特征和成因,结合中国大陆及邻区M≥ 8. 0大震序列和华北地区自1966年以来M≥6. 0地震序列,研究了丛集状态的自相似性及临界时间分支现象。对发震系统的内部时间给出了定义,并研究了地震丛集的非线性时间结构特征。研究认为: ① 事件的发生意味着系统现存的能量一次跳跃式的释放和状态的一次转换。只有当系统以某种足够随机方式动作时,系统状态才会转换,事件才会发生;② 涨落是系统状态的探测器,在临界状态时系统出现很大的涨落,时间变异系数W= 1。临界丛集是由系统内在随机性决定的,事件发生时间是随机地出现的,未来可能出现不止一次的W= 1的临界点;③ 相继发生的事件(M≥ M0) 可以反映系统的演化过程和特征,丛集是系统演化过程中的有序性和无序性的综合反映;④ 事件丛集和丛集的事件具有自相似性和分形结构,也具有耗散系统的自组织和映射特点;⑤ 根据时间变异系数,系统内部时间(年龄),时间间隔与所经历时间非线性关系式以及映射和迭代方法可以用来预测未来地震趋势和未来M≥ M0 地震发生的可能时间点。

关键词: 地震丛集, 非线性, 自相似性, 时间分支, 临界状态

Abstract: The newest method to study earthquake clustering at critical point, namely the time-fram e coefficient method is introduced in this paper, and the characteristics of earthquake clustering of different types and their causes are discussed. On the basis of study on the large earthquakesequence with M≥ 8. 0 in China's mainl and and the sequence with M≥ 6. 0 in North China, the features of self-similarity and timediversity phenomenon in critical state are consequently proposed. In addition, internal time of earthquake generation system isdefined and the nonlinear characteristics of earthquake clustering are studied. The followingresults are obtained: ① An event occurrence means alarge quantity of energy release and state conversionone time. Only when the behavior of earthquake generation system is of enough r and omness, the state conversion and the events may occur. ② Fluctuation is thedetector of earthquake gen-eration system. When the system is in the critical state, the magnitude of fluctuation gets greater and time-framecoefficient equals 1. The clustering in the critical state isdetermined by intrinsicr and om feature and thus seismic events occur stochastically. There w on't be onlyone critical point with coefficientW= 1. ③ Seismic events (M≥ M0) occurred sequentially reflect the process and its feature of earthquake generation system; Clusteringreflects theordering and disordering feature of the system. ④ Ev ent clustering and clustered events are of the fea-ture of self-similarity and fractal form and also of the feature of self-organization and mapping. ⑤ Time-fram e coefficient, internal time (or age) of the earthquakesystem, the linear equation of interval and past time, mapping and the method of repeating calculation can be used to pre-dict the trend of earthquakes and critical points in the future for the selected earthquakese-quence with M≥ M0.

Key words: Earthquake clustering, Non-linearity, Self-similarity, Timediversity phe-nomenon, Critical state

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