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
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.
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.
[1]
Beacher, G. B., 1982, Statistical examination of reservoir induced seismicity: Bull. Seismol. Soc. Am, 72, 552 - 569.
[2]
Chang, B., 1992, Preliminary study on the prediction of reservoir earthquakes: Induced Seismicity, Balkema, P. K., Ed., Rotterdam, 213 - 230.
[3]
Chang, B., and Liang, J., 1992, Prediction about the maximum magnitude of reservoir induced earthquake: South China Journal of Seismology, 12(1), 74 - 79.
[4]
Chen, T., Morris, J., and Martin, E., 2007, Gaussian process regression for multivariate spectroscopic calibration: Chemometrics and Intelligent Laboratory Systems, 83(1), 59 - 71.
[5]
Cortes, C., and Vapnik, V., 1995, Support vector networks: Machine Learning, 20, 273 - 297.
[6]
Cristianini, N., and Shawe-Taylor, J., 2000, An introduction to support vector machines: Cambridge University Press, England.
[7]
Feng, D. Y., 1984, Assessment of earthquake hazard by simultaneous use of the statistical method and the method of fuzzy mathematics: Pure and Applied Geophysics, 126, 982 - 987.
[8]
Habibagahi, G., 1998, Reservoir induced earthquakes analyzed via radial basis function networks: Soil Dynamics and Earthquake Engineering, 17(1), 53 - 56.
[9]
He, W. M., Qin, J. Z., and Liu, M. J., et al., 2001, Forecast on induced earthquakes for Xiaolangdi reservoir: Northwestern Seismological Journal, 23(2), 164 - 168.
[10]
Kim, K. J., 2003, Financial time series forecasting using support vector machines: Neurocomputing, 55, 307 - 319.
[11]
Kim, K. J., and Ahn, H., 2012, A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach: Computers and Operations Research, 39(8), 1800 - 1811.
[12]
Li, K., and Cao, L., 1995, Applying hierarchy analysis method to predicting reservoir induced earthquake: Seismological Research of Northeast China, 11(2), 39 - 45.
[13]
Likar, B., and Kocijan, J., 2007, Predictive control of a gas-liquid separation plant based on a Gaussian process model: Comput. Chem. Eng., 31(3), 142 - 152.
[14]
Liu, Y., Gan, Z., and Sun, Y., 2008, Static hand gesture recognition and its application based on support vector machines: in The Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 517 - 521.
[15]
Rasmussen, C. E., and Williams, C. K. I., 2005, Gaussian processes for machine learning: MIT Press.
[16]
Sonavane, S., and Chakrabarti, P., 2010, Prediction of active site cleft using support vector machines: J. Chem. Info. Modeling, 50(12), 2266 - 2273.
[17]
Suetani, H., Ideta, A. M., and Morimoto, J., 2011, Nonlinear structure of escape-times to falls for a passive dynamic walker on an irregular slope: Anomaly detection using multi-class support vector machine and latent state extraction by canonical correlation analysis: IEEE International Conference on Intelligent Robots and Systems, 604843, 2715 - 2722.
[18]
Vapnik, V., 1998, Statistical learning theory: Springer, New York.
[19]
Yazdi, H. S., Effati, A. S., and Saberi, Z., 2009, Recurrent neural network-based method for training probabilistic support vector machine: Internat. J. Signal Imaging Systems Eng., 2(1 - 2), 57 - 65.
[20]
Yuan, J., Wang, K., Yu, T., and Fang, M., 2008, Reliable multi-objective optimization of high speed WEDM process based on Gaussian process regression: International Journal of Machine Tools and Manufacture, 48, 47 - 60.