利用支持向量机和高斯过程回归测定水库诱发的地震(英文)
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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)模型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)。将本文开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。
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.
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.
引文
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Chen,T.,Morris,J.,and Martin,E.,2007,Gaussian processregression for multivariate spectroscopic calibration:Chemometrics and Intelligent Laboratory Systems,83(1),59-71.
Cortes,C.,and Vapnik,V.,1995,Support vector networks:Machine Learning,20,273-297.
Cristianini,N.,and Shawe-Taylor,J.,2000,An introductionto support vector machines: Cambridge University Press,England.
Feng,D.Y.,1984,Assessment of earthquake hazard bysimultaneous use of the statistical method and the methodof fuzzy mathematics: Pure and Applied Geophysics,126,982-987.
Habibagahi,G.,1998,Reservoir induced earthquakesanalyzed via radial basis function networks: SoilDynamics and Earthquake Engineering,17(1),53-56.
He,W.M.,Qin,J.Z.,and Liu,M.J.,et al.,2001,Forecaston induced earthquakes for Xiaolangdi reservoir:Northwestern Seismological Journal,23(2),164-168.
Kim,K.J.,2003,Financial time series forecasting usingsupport vector machines: Neurocomputing,55,307-319.
Kim,K.J.,and Ahn,H.,2012,A corporate credit ratingmodel using multi-class support vector machines with anordinal pairwise partitioning approach: Computers andOperations Research,39(8),1800-1811.
Li,K.,and Cao,L.,1995,Applying hierarchy analysismethod to predicting reservoir induced earthquake:Seismological Research of Northeast China,11(2),39-45.
Likar,B.,and Kocijan,J.,2007,Predictive control of a gas-liquid separation plant based on a Gaussian processmodel: Comput.Chem.Eng.,31(3),142-152.
Liu,Y.,Gan,Z.,and Sun,Y.,2008,Static hand gesturerecognition and its application based on support vectormachines: in The Ninth ACIS International Conferenceon Software Engineering,Artificial Intelligence,Networking,and Parallel/Distributed Computing,517-521.
Rasmussen,C.E.,and Williams,C.K.I.,2005,Gaussianprocesses for machine learning: MIT Press.
Sonavane,S.,and Chakrabarti,P.,2010,Prediction ofactive site cleft using support vector machines: J.Chem.Info.Modeling,50(12),2266-2273.
Suetani,H.,Ideta,A.M.,and Morimoto,J.,2011,Nonlinear structure of escape-times to falls for a passivedynamic walker on an irregular slope: Anomaly detectionusing multi-class support vector machine and latentstate extraction by canonical correlation analysis: IEEEInternational Conference on Intelligent Robots andSystems,604843,2715-2722.
Vapnik,V.,1998,Statistical learning theory: Springer,NewYork.
Yazdi,H.S.,Effati,A.S.,and Saberi,Z.,2009,Recurrentneural network-based method for training probabilisticsupport vector machine: Internat.J.Signal ImagingSystems Eng.,2(1-2),57-65.
Yuan,J.,Wang,K.,Yu,T.,and Fang,M.,2008,Reliablemulti-objective optimization of high speed WEDM processbased on Gaussian process regression: InternationalJournal of Machine Tools and Manufacture,48,47-60.
Chang,B.,1992,Preliminary study on the prediction ofreservoir earthquakes: Induced Seismicity,Balkema,P.K.,Ed.,Rotterdam,213-230.
Chang,B.,and Liang,J.,1992,Prediction about themaximum magnitude of reservoir induced earthquake:South China Journal of Seismology,12(1),74-79.
Chen,T.,Morris,J.,and Martin,E.,2007,Gaussian processregression for multivariate spectroscopic calibration:Chemometrics and Intelligent Laboratory Systems,83(1),59-71.
Cortes,C.,and Vapnik,V.,1995,Support vector networks:Machine Learning,20,273-297.
Cristianini,N.,and Shawe-Taylor,J.,2000,An introductionto support vector machines: Cambridge University Press,England.
Feng,D.Y.,1984,Assessment of earthquake hazard bysimultaneous use of the statistical method and the methodof fuzzy mathematics: Pure and Applied Geophysics,126,982-987.
Habibagahi,G.,1998,Reservoir induced earthquakesanalyzed via radial basis function networks: SoilDynamics and Earthquake Engineering,17(1),53-56.
He,W.M.,Qin,J.Z.,and Liu,M.J.,et al.,2001,Forecaston induced earthquakes for Xiaolangdi reservoir:Northwestern Seismological Journal,23(2),164-168.
Kim,K.J.,2003,Financial time series forecasting usingsupport vector machines: Neurocomputing,55,307-319.
Kim,K.J.,and Ahn,H.,2012,A corporate credit ratingmodel using multi-class support vector machines with anordinal pairwise partitioning approach: Computers andOperations Research,39(8),1800-1811.
Li,K.,and Cao,L.,1995,Applying hierarchy analysismethod to predicting reservoir induced earthquake:Seismological Research of Northeast China,11(2),39-45.
Likar,B.,and Kocijan,J.,2007,Predictive control of a gas-liquid separation plant based on a Gaussian processmodel: Comput.Chem.Eng.,31(3),142-152.
Liu,Y.,Gan,Z.,and Sun,Y.,2008,Static hand gesturerecognition and its application based on support vectormachines: in The Ninth ACIS International Conferenceon Software Engineering,Artificial Intelligence,Networking,and Parallel/Distributed Computing,517-521.
Rasmussen,C.E.,and Williams,C.K.I.,2005,Gaussianprocesses for machine learning: MIT Press.
Sonavane,S.,and Chakrabarti,P.,2010,Prediction ofactive site cleft using support vector machines: J.Chem.Info.Modeling,50(12),2266-2273.
Suetani,H.,Ideta,A.M.,and Morimoto,J.,2011,Nonlinear structure of escape-times to falls for a passivedynamic walker on an irregular slope: Anomaly detectionusing multi-class support vector machine and latentstate extraction by canonical correlation analysis: IEEEInternational Conference on Intelligent Robots andSystems,604843,2715-2722.
Vapnik,V.,1998,Statistical learning theory: Springer,NewYork.
Yazdi,H.S.,Effati,A.S.,and Saberi,Z.,2009,Recurrentneural network-based method for training probabilisticsupport vector machine: Internat.J.Signal ImagingSystems Eng.,2(1-2),57-65.
Yuan,J.,Wang,K.,Yu,T.,and Fang,M.,2008,Reliablemulti-objective optimization of high speed WEDM processbased on Gaussian process regression: InternationalJournal of Machine Tools and Manufacture,48,47-60.
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