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Some Applications of Point Processes in Seismicity Modeling and Prediction
https://ir.soken.ac.jp/records/758
https://ir.soken.ac.jp/records/758bf85af23500e445998338ae746fc7183
名前 / ファイル  ライセンス  アクション 

要旨・審査要旨 / Abstract, Screening Result (271.5 kB)

Item type  学位論文 / Thesis or Dissertation(1)  

公開日  20100222  
タイトル  
タイトル  Some Applications of Point Processes in Seismicity Modeling and Prediction  
タイトル  
タイトル  Some Applications of Point Processes in Seismicity Modeling and Prediction  
言語  en  
言語  
言語  eng  
資源タイプ  
資源タイプ識別子  http://purl.org/coar/resource_type/c_46ec  
資源タイプ  thesis  
著者名 
庄, 建倉
× 庄, 建倉 

フリガナ 
ジュアン, ジャンカン
× ジュアン, ジャンカン 

著者 
ZHUANG, Jiancang
× ZHUANG, Jiancang 

学位授与機関  
学位授与機関名  総合研究大学院大学  
学位名  
学位名  博士（学術）  
学位記番号  
内容記述タイプ  Other  
内容記述  総研大甲第648号  
研究科  
値  数物科学研究科  
専攻  
値  15 統計科学専攻  
学位授与年月日  
学位授与年月日  20030324  
学位授与年度  
値  2002  
要旨  
内容記述タイプ  Other  
内容記述  The idea of probability prediction was quite difficult to be accepted at the beginning by geophysicists and physicists. People become more and more interested in probability prediction, because they found after so many year's research that the causes of earthquakes are very complicated and the occurrences of precursors and earthquakes are not a simple onetoone relationship. Some earthquakes occurred after we observed a certain kind of anomaly phenomena, but some earthquakes occurred without these precursors observed, or no earthquakes occurred after we observed the same anomalies.<br /> In this paper, the central topic is on the development of point process models to describe and to predict the earthquake risk based on the previous observation, including earthquake occurrences and precursors.<br /> The conditional intensity is used for the model specification, which has a natural form for prediction. Several models are given in the paper including the Poisson model, the stress release model, the self and mutualexcitation process and the epidemic type aftershock sequence (ETAS) model.<br /> The ETAS model is discussed in more detail. First, its properties, its criticality and moments are analyzed.<br /> The technique, called stochastic declustering, is concerned with an objective estimation of the spatial intensity function of the background earthquake occurrences from an earthquake catalogue which includes numerous clustered events in space and time, and with an algorithm of producing declustered catalogues from it, and also the reconstruction of the catalogue into the explicit clusters. It is shown that the background intensity function can be evaluated if the total spatial seismicity intensity and the branching structure are estimated. Or, the whole spacetime process can be practically split into two subprocesses, the background and the clustered ones. Specifically, in the paper, a spacetime epidemic type aftershock sequence (ETAS) model is adopted to describe the branching structure of earthquake occurrences. This algorithm combines a parametric maximum likelihood estimation for the clustering structures in the spacetime ETAS model and a nonparametric approach to the density estimation of the background seismicity, which is called a weighted variable kernel method. As a demonstration of the presented methods, we estimate the background seismic activities in the central New Zealand region and in the central western Japan region to produce the catalogues of background events.<br /> For the prediction purposes, we give the simulation procedures for Poisson models and ETAS models. Besides Ogata's thinning method, we propose another faster method, called the composition method, for the simulation of the ETAS model.<br /> An example is also given for modeling the relationship between precursors and an earthquake. The precursor data are the signals from four stations monitoring the ultralow frequency components electric field in the vicinity of Beijing, and are used as explanatory variables in forecasting the occurrence of events with magnitude M〓4 within 300 km circle centered on Beijing. The model used is a version of Ogata's LINLIN algorithm for examining the influence of an explanatory signal on the occurrence of events in a stochastic point process. The explanatory effect is shown to be highly significant, and greatly superior to the explanatory effect of the same signals applied to a randomized version of the earthquake data. All four stations show significant explanatory power, although in combination the two most effective tend to dominate the forecasts. The results are stable against perturbations in the time period or region of observation. The predictions appear to be most effective for events with M 〓 5, and for events closer to the observing stations, although some of the smaller events appear to produce detectable signals at distances of over 100 km from the source. Probability gains over the simple Poisson process are in the region up to 3  4 for the events of magnitude 5 or larger. A special study is made of predicted and unpredicted events in the region around the M 7.8 Tangshan earthquake of 1976, to reveal the common spatial pattern of the classified events corresponding to all individual stations.  
所蔵  
値  有 