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RESEARCH TOWARDS REAL SEISMIC ACTIVITY MONITORING SYSTEM BASED ON STATISTICAL MODELING

基于统计模型的真实地震活动监测系统研究

基本信息

批准号：
17540395
负责人：
SHIMAZAKI Kunihiko
金额：
$ 2.18万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2006
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-17540395/
关键词：
Seismic activity Earthquake forecasting Anomaly detection Monitoring system

项目摘要

No real earthquake monitoring system is available, although various researches have been reported on anomalous seismic activity prior to a large earthquake. This study consists of three parts: 1) detection system of anomalies, 2) prior earthquake probability estimate, and 3) presentation system of results. The first of all, a monitoring system of seismic activity is constructed to detect anomalous seismicity. The most prominent feature of seismic activity is concentration in space and time, which is best described by the ETAS model proposed by Ogata in 1998. According to the model, all the activity can be divided into two: statistically independent background activity and statistically dependent aftershocks. Most of the previous analysis often avoided to separate the two objectively, thus judged ordinary activity as unusual, or could not detect anomaly prior to a large earthquake. We used background seismicity to detect unusual activity after stochastically separating independent and d … More ependent evens using the intensity function. We compare the results obtained by applying the detection system to the real seismicity data and also to synthetic seismicity data produced from the ETAS model. We use the difference in AIC (=SAIC) between two models: one assuming anomalous activity during a certain time-period and the other assuming normal activity throughout all the time period. Our analysis shows seismic quiescence showing SAIC smaller than -8 and high seismicity showing SAIC larger than 10 are significant anomalies. For the second subject, we constructed eight time-invariant probabilistic models of earthquake potential on the basis of various proposals made in the past. Then we test them against historical earthquake data and concluded that the spatially smoothed synthesized seismicity model based on active fault data is the most reliable. Thirdly we recommend to use animation movie to show the result of earthquake monitoring. As an example, the spatial distribution of b-value is shown to change in time in our report. Less

尽管对大地震前的异常地震活动有各种研究报告，但没有真实的地震监测系统。本研究由三部分组成：1）异常检测系统，2）地震前概率估计，3）结果显示系统。首先，建立了地震活动监测系统，对地震活动异常进行监测。地震活动最显著的特征是在空间和时间上的集中性，Ogata在1998年提出的ETAS模型最好地描述了这一点。根据该模型，所有的活动可以分为两个：统计独立的背景活动和统计相关的余震。以往的分析往往避免客观地将两者分开，从而将正常活动判断为异常，或无法检测到大震前的异常。我们使用背景地震活动来检测异常活动后，随机分离独立和d ...更多信息使用强度函数的独立事件。我们比较了通过将检测系统应用于真实的地震活动性数据以及ETAS模型产生的合成地震活动性数据所获得的结果。我们使用两个模型之间AIC（=SAIC）的差异：一个假设在某个时间段内的异常活动，另一个假设在整个时间段内的正常活动。分析表明，SAIC小于-8的地震平静和SAIC大于10的高地震活动是显著的异常。在第二个课题中，我们在前人研究的基础上，建立了八个时不变的地震潜势概率模型。然后，我们用历史地震数据对它们进行了检验，并得出结论，基于活断层数据的空间平滑合成地震活动性模型是最可靠的。第三，我们建议使用动画电影来展示地震监测的结果。例如，在我们的报告中，b值的空间分布随时间变化。少