| Joint multivariate statistical model and its applications to synthetic earthquake predic-tion |
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| 作者姓名： | 韩天锡 蒋淳 魏雪丽 韩梅 冯德益 |
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| 作者单位： | Tianjin University of Technology，Tianjin 300191，China，Earthquake Administration of Tianjin，Tianjin 300201，China，Tianjin University of Technology，Tianjin 300191，China，Tianjin University of Technology，Tianjin 300191，China，Earthquake Administration of Tianjin，Tianjin 300201，China |
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| 基金项目： | KeyProjectoftheTenthFive-yearPlanofStateScientificCommission(2001BA601B01-010506). |
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| 摘 要： | IntroductionSeismologistsinChinahaveestablishedmanyseismologicalmethodsandearthquakepredic-tionfactorsintheearthquakeforecastpracticesinthelastthirtyyears.Atpresent,morethanonehundredseismometricfactorsareusedinthemedium-termandmedium-short-termearthquakepredictions,suchastheb-valuetomirrorthestressstateandthedistributiondegreeofthemedium,themf-valuetoexpressiftheseismicactivityisincreasedornot(WANG,etal,1994),thequanti-tativeparameterA(b)todescribetheseismicactivitiesinadistrict(WU,CAO,19…
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| 收稿时间： | 3 March 2003 |
| 修稿时间： | 12 April 2004 |
Joint multivariate statistical model and its applications to synthetic earthquake prediction |
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| Han Tian-xi , Jiang Chun , Wei Xue-li , Han Me and Feng De-yi.Joint multivariate statistical model and its applications to synthetic earthquake predic-tion[J].Acta Seismologica Sinica(English Edition),2004,17(5):578-584. |
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| Authors: | Han Tian-xi Jiang Chun Wei Xue-li Han Me and Feng De-yi |
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| Institution: | (1) Tianjin University of Technology, 300191 Tianjin, China;(2) Earthquake Administration of Tianjin, 300201 Tianjin, China |
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| Abstract: | Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component analysis with discriminatory analysis. Principal component analysis and discriminatory analysis are very important theories in multivariate statistical analysis that has developed quickly in the late thirty years. By means of maximization information method, we choose several earthquake prediction factors whose cumulative proportions of total sam-ple variances are beyond 90% from numerous earthquake prediction factors. The paper applies regression analysis and Mahalanobis discrimination to extrapolating synthetic prediction. Furthermore, we use this model to charac-terize and predict earthquakes in North China (30~42N, 108~125E) and better prediction results are obtained. |
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| Keywords: | joint multivariate statistical model principal component analysis discriminatory analysis syn-thetic earthquake predication |
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