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应用地球物理  2013, Vol. 10 Issue (2): 229-234    DOI: 10.1007/s11770-013-0381-5
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利用支持向量机和高斯过程回归测定水库诱发地震
Pijush Samui1,2, and Dookie Kim2
1. Centre for Disaster Mitigation and Management, VIT University, Vellore-632014, India
2. Department of Civil Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea
Determination of reservoir induced earthquake using support vector machine and gaussian process regression
Pijush Samui1,2, and Dookie Kim2
1. Centre for Disaster Mitigation and Management, VIT University, Vellore-632014, India
2. Department of Civil Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea
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摘要 水库诱发地震震级(M)的预测是在地震工程中的一项重要任务。本文采用支持向量机(SVM)和高斯过程回归(GPR)方法根据水库的参数预测了水库诱发地震震级(M)。综合参数(E)和最大的水库深度(H)作为支持向量机和高斯过程回归模型的输入参数。我们给出一个方程确定水库诱发地震震级(M). 将本文研发的支持向量机和建立的高斯过程回归方法与人工神经网络(ANN)方法相比。结果表明,本文研发的支持向量机和高斯过程回归方法是预测水库诱发地震震级(M)的有效工具。
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Pijush Samui
and Dookie Kim
关键词
关键词:水库诱发地震
   地震震级   支持向量机   高斯过程回归   预测     
Abstract: The prediction of magnitude (M) of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine (SVM) and Gaussian Process Regression (GPR) for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are considered as inputs to the SVM and GPR. We give an equation for determination of reservoir induced earthquake M. The developed SVM and GPR have been compared with the Artificial Neural Network (ANN) method. The results show that the developed SVM and GPR are efficient tools for prediction of reservoir induced earthquake M.
Key wordsReservoir induced earthquake   earthquake magnitude   Support Vector Machine   Gaussian Process Regression   prediction   
收稿日期: 2013-03-08;
引用本文:   
Pijush Samui,and Dookie Kim. 利用支持向量机和高斯过程回归测定水库诱发地震[J]. 应用地球物理, 2013, 10(2): 229-234.
Pijush Samui,and Dookie Kim. Determination of reservoir induced earthquake using support vector machine and gaussian process regression[J]. APPLIED GEOPHYSICS, 2013, 10(2): 229-234.
 
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