| Principal Component-Discrimination Model and Its Application |
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| 作者姓名： | 韩天锡 魏雪丽 蒋淳 张玉琍 |
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| 作者单位： | [1]TianjinUniversityofTechnology,Tianjin300191,China [2]TianjinEarthquakeBureau,Tianjin300201,China |
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| 基金项目： | SupportedbyKeyProjectoftheTenthFive YearPlanningofStateScientif icCommission (No.2 0 0 1BA60 1B0 1 0 1 0 50 6) |
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| 摘 要： | Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.
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| 关 键 词： | 知组分辨别分析 地震预测 相关分析 地震分析 模拟分析 |
Principal Component-Discrimination Model and Its Application |
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| HAN Tian-xi,WEI Xue-li,JIANG Chun,ZHANG Yu-li.Principal Component-Discrimination Model and Its Application[J].Transactions of Tianjin University,2004,10(4):315-318. |
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| Authors: | HAN Tian-xi WEI Xue-li JIANG Chun ZHANG Yu-li |
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| Institution: | 1. Tianjin University of Technology, Tianjin 300191, China
2. Tianjin Earthquake Bureau, Tianjin 300201, China |
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| Abstract: | Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results. |
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| Keywords: | principal component analysis discrimination analysis correlation analysis weighted method of principal factor coefficients |
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