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地震 ›› 2000, Vol. 20 ›› Issue (1): 45-52.

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强震前中短期地震活动异常动态图像的表示方法及其预测意义

陈虹   

  1. 中国地震局地壳应力研究所,北京100085
  • 收稿日期:1999-03-04 修回日期:1999-04-15 出版日期:2000-01-31 发布日期:2022-09-26
  • 作者简介:陈虹(1963-),女,副研究员,1988年获硕士学位,主要从事地震预报及利用地震波研究地球内部速度结构方面的研究。
  • 基金资助:
    中国地震局“九五”科研攻关项目(95-4-4-02-3); 中国地震局地壳应力研究所论著1999A0002

A method to express Pattern of seismieity anomaly before strong earthquakes and its Predietive value

CHEN Hong   

  1. Institute of Crustal Dynamies,CSB,Beijing 100085,China
  • Received:1999-03-04 Revised:1999-04-15 Online:2000-01-31 Published:2022-09-26

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摘要： 提出了二个描述中强震发生前中短期阶段地震活动异常时空演化图像的参量δNδE。分别将其用于华北地区、新疆地震区及南北地震带,并对其预报效能进行检验,对其异常的空间分布图像与强震发生地点进行了分析。结果表明,该二参量能较好地反应强震前中短期阶段孕震区及其周围地区地震活动的异常平静及丛集的现象。二参量异常的时空分布图像上示出在中强震发生前3个月至1年在震中周围地区有明显的异常分布,且随着时间逼近发震时间,异常面积的收缩和扩大与震级大小有关。不考虑动态图像,只对出现异常后l年发生地震为限,则3个地震区的预报评分均在0.5以上。如再对具体图像动态变化过程进行具体分析,则还能降低虚报率。

关键词: 地震预测, 地震活动, 时空动态图像

Abstract: Two parameters δN,δE, deseribing the pattern of seisrnieity anomaly before stronge arthquake were proposed in this paper,and it was used in North China,Xinjiang and South-North seismic zones.The predietability of those two parameters was tested;the relation between the anomalous distribution Pattern and the Position of stronge arth-quakes was studied.The results show that those two parameters could better express the anomal ous seismic quiescence and anomalous seismic clustering within the seismic zonebe-for estrongearthquakes.From the time-space anomalous pattern of δN,δE we can see that the anomaly distributed around the epicentral area from three months to one year before moderately stronge arthquakes obviously,and with the time approaehing to the origin time of an earthquake,the size of anomalous area is relevant to the earthquake magnitude.Re-gardless of the variation of pattern with time,we just regard the earthquake oeeurred in one year after anomaly appearing as corrcet foreeasting,the foreeast evaluation marks R for three seismice zones are greater than 0.5。

Key words: Earthquake predietion, Seismieity, Temporal-spatial dynamic pattern

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