关于地震丛集特征、成因及临界状态的讨论
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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 1 966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δ =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δ =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。
The newest method to study earthquake clustering at critical point, namely the time frame 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 earthquake sequence with M≥8.0 in China's mainland and the sequence with M≥6.0 in North China, the features of self similarity and time diversity phenomenon in critical state are consequently proposed. In addition, internal time of earthquake generation system is defined and the nonlinear characteristics of earthquake clustering are studied. The following results are obtained: ①An event occurrence means a large quantity of energy release and state conversion one time. Only when the behavior of earthquake generation system is of enough randomness, the state conversion and the events may occur.②Fluctuation is the detector of earthquake generation system. When the system is in the critical state, the magnitude of fluctuation gets greater and time frame coefficient equals 1. The clustering in the critical state is determined by intrinsic random feature and thus seismic events occur stochastically. There won't be only one critical point with coefficient δ=1.③Seismic events (M≥M 0) occurred sequentially reflect the process and its feature of earthquake generation system; Clustering reflects the ordering and disordering feature of the system. ④ Event clustering and clustered events are of the feature of self similarity and fractal form and also of the feature of self organization and mapping.⑤Time frame coefficient, internal time (or age) of the earthquake system, the linear equation of interval and past time, mapping and the method of repeating calculation can be used to predict the trend of earthquakes and critical points in the future for the selected earthquake sequence with M≥M 0.
The newest method to study earthquake clustering at critical point, namely the time frame 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 earthquake sequence with M≥8.0 in China's mainland and the sequence with M≥6.0 in North China, the features of self similarity and time diversity phenomenon in critical state are consequently proposed. In addition, internal time of earthquake generation system is defined and the nonlinear characteristics of earthquake clustering are studied. The following results are obtained: ①An event occurrence means a large quantity of energy release and state conversion one time. Only when the behavior of earthquake generation system is of enough randomness, the state conversion and the events may occur.②Fluctuation is the detector of earthquake generation system. When the system is in the critical state, the magnitude of fluctuation gets greater and time frame coefficient equals 1. The clustering in the critical state is determined by intrinsic random feature and thus seismic events occur stochastically. There won't be only one critical point with coefficient δ=1.③Seismic events (M≥M 0) occurred sequentially reflect the process and its feature of earthquake generation system; Clustering reflects the ordering and disordering feature of the system. ④ Event clustering and clustered events are of the feature of self similarity and fractal form and also of the feature of self organization and mapping.⑤Time frame coefficient, internal time (or age) of the earthquake system, the linear equation of interval and past time, mapping and the method of repeating calculation can be used to predict the trend of earthquakes and critical points in the future for the selected earthquake sequence with M≥M 0.
引文
王林瑛,前震地震平静及尺度不变性等地震活动特征在短临预报中的实用方法研究,2001。
[1] 国家地震局地球物理研究所.地震科学中的非线性问题[M].北京:地震出版社,1993.81,155.
[2] PeterCoveney,RogerHighfield.时间之箭[M].长沙:湖南科学出版社,1995.4.
[3] 张国民,李丽,黎凯武.强震成组活动与潮汐力调制触发[J].中国地震,2000,17(2):110-120.
[4] 朱传镇.地震预报的地震学方法研究[A].中国地震局科技发展司.第一届两岸地震学术讨论会论文集[D].北京:地震出版社,1992.38-62.
[5] SchwartsD P,CoppersmithK J.Fault behavior and characteristic earthquake:examples from theWasatch andSanAndress faultzones[J].JGeophysRes,1984,89:5681-5698.
[6] ShimazakiK,NakataT.Time- predictable recurrence model for earthquake[J].GeophysResLett,1980,7:279-282.
[7] KaganY Y,JacksonD D.Long- term earthquake clustering[J].GeophysJInt,1991,104:117-133.
[8] KaganY Y.Statistics of characteristic of active earthquakes[J].BullSeismSocAm,1993,83:7-24.
[9] CornellC A.Engineering seismic risk analysis[J].BullSeismSocAm,1968,58:1583-1606.
[10] 尼科里斯,普利高津.探索复杂性[M].罗久里等译.四川:四川教育出版社,1986.79,236,11.
[11] 罗灼礼,闻学泽,罗伟.中国大陆原地复发强震的基本特征及其预测[J].地震,1995,15(1):1-11.
[12] 闻学泽.活动断裂地震潜势的定量评估[M].北京:地震出版社,1996.
[13] 罗灼礼,王伟军,陈凌.海城岫岩地震序列非线性及判定前震序列的时间结构变异诊断法[J].地震,2000,20(增刊):18-27.
[14] 龙尼兹C.地震危险性分析中的统计计方法[M].北京:地震出版社,1988.3-25.
[15] 宇津德治.地震事典[M].北京:地震出版社,1990.231.
[16] 闻学泽.中国大陆活动断裂的段破裂地震复发行为[J].地震学报,1999,21(4):411-418.
[17] 张秋文,张培震.中国大陆若干地震构造带的地震准周期丛集复发行为[J].大地测量与地球动力学,2002,22(11):56-62.
[18] PxigonineI,StengersI.Order out ofChaos[M].BantamBooksInc,1984.50.
[19] 高安秀树.分数维[M].北京:地震出版社,1989.31.
[20] 以垂祥.非线性科学及其在地学中的应用[M].北京:气象出版社,1995.267.
[21] 国家地震局震害防御司.全球重大灾害性地震目录[M].北京:地震出版社,1996
[1] 国家地震局地球物理研究所.地震科学中的非线性问题[M].北京:地震出版社,1993.81,155.
[2] PeterCoveney,RogerHighfield.时间之箭[M].长沙:湖南科学出版社,1995.4.
[3] 张国民,李丽,黎凯武.强震成组活动与潮汐力调制触发[J].中国地震,2000,17(2):110-120.
[4] 朱传镇.地震预报的地震学方法研究[A].中国地震局科技发展司.第一届两岸地震学术讨论会论文集[D].北京:地震出版社,1992.38-62.
[5] SchwartsD P,CoppersmithK J.Fault behavior and characteristic earthquake:examples from theWasatch andSanAndress faultzones[J].JGeophysRes,1984,89:5681-5698.
[6] ShimazakiK,NakataT.Time- predictable recurrence model for earthquake[J].GeophysResLett,1980,7:279-282.
[7] KaganY Y,JacksonD D.Long- term earthquake clustering[J].GeophysJInt,1991,104:117-133.
[8] KaganY Y.Statistics of characteristic of active earthquakes[J].BullSeismSocAm,1993,83:7-24.
[9] CornellC A.Engineering seismic risk analysis[J].BullSeismSocAm,1968,58:1583-1606.
[10] 尼科里斯,普利高津.探索复杂性[M].罗久里等译.四川:四川教育出版社,1986.79,236,11.
[11] 罗灼礼,闻学泽,罗伟.中国大陆原地复发强震的基本特征及其预测[J].地震,1995,15(1):1-11.
[12] 闻学泽.活动断裂地震潜势的定量评估[M].北京:地震出版社,1996.
[13] 罗灼礼,王伟军,陈凌.海城岫岩地震序列非线性及判定前震序列的时间结构变异诊断法[J].地震,2000,20(增刊):18-27.
[14] 龙尼兹C.地震危险性分析中的统计计方法[M].北京:地震出版社,1988.3-25.
[15] 宇津德治.地震事典[M].北京:地震出版社,1990.231.
[16] 闻学泽.中国大陆活动断裂的段破裂地震复发行为[J].地震学报,1999,21(4):411-418.
[17] 张秋文,张培震.中国大陆若干地震构造带的地震准周期丛集复发行为[J].大地测量与地球动力学,2002,22(11):56-62.
[18] PxigonineI,StengersI.Order out ofChaos[M].BantamBooksInc,1984.50.
[19] 高安秀树.分数维[M].北京:地震出版社,1989.31.
[20] 以垂祥.非线性科学及其在地学中的应用[M].北京:气象出版社,1995.267.
[21] 国家地震局震害防御司.全球重大灾害性地震目录[M].北京:地震出版社,1996
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