Journal : Acta Geophysica
Article : Applicability of artificial intelligence to reservoir induced earthquakes

Authors :
Shiuly, A
Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, India,
Kumar, V
Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, India,
Narayan, J.P.
Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, India, jaypnfeq@iitr.ernet.in,
Mousavian, R.
Department of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran, R_mousavian@yahoo.com,
Hossainali, M.M.
Department of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran, Hossainali@kntu.ac.ir,
Wiszniowski, J.
Institute of Geophysics, Polish Academy of Sciences, Warszawa, Poland, jwisz@igf.edu.pl,
Plesiewicz, B.M.
Institute of Geophysics, Polish Academy of Sciences, Warszawa, Poland,
Trojanowski, J.
Institute of Geophysics, Polish Academy of Sciences, Warszawa, Poland,
Agh-Atabai, M.
Department of Geology, Faculty of Sciences, Golestan University, Gorgan, Iran, maryamataby@yahoo.com,
Mirabedini, M.S.
Department of Geology, Faculty of Sciences, Golestan University, Gorgan, Iran, m_mirabedini89@yahoo.com,
Rashed, M.
Department of Geophysics, Faculty of Earth Sciences, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia; Geology Department, Faculty of Science, Suez Canal University, Ismailia, Egypt, rashedmohamed@gmail.com,
Muduli, P.K
Department of Civil Engineering, National Institute of Technology, Rourkela, India, pradyut.muduli@gmail.com,
Das, S.K.
Department of Civil Engineering, National Institute of Technology, Rourkela, India, saratdas@rediffmail.com,
Liu, H.
School of Information Engineering, China University of Geosciences, Beijing, China, Jerryliu1103@gmail.com,
Lei, X
School of Information Engineering, China University of Geosciences, Beijing, China,
Mao, C
Research Institute of Exploration and Development, DianQianGui Oil Company, Sinopec Group, Kunming, Yunnan, China,
Li, S.
Research Institute of Exploration and Development, QingHai Oil Company, CNPC, Dunhuang, Gansu, China,
Chou, P.-Y
Geotechnical Engineering Research Center, Sinotech Engineering Consultants, Inc., Taipei, Taiwan, poyi.chou@sinotech.org.tw,
Hsu, S.-M
Geotechnical Engineering Research Center, Sinotech Engineering Consultants, Inc., Taipei, Taiwan, shihmeng@sinotech.org.tw,
Chen, P.-J
Geotechnical Engineering Research Center, Sinotech Engineering Consultants, Inc., Taipei, Taiwan, ray@sinotech.org.tw,
Lin, J.-J.
Geotechnical Engineering Research Center, Sinotech Engineering Consultants, Inc., Taipei, Taiwan, jjlin@sinotech.org.tw,
Lo, H.-C
Geotechnical Engineering Research Center, Sinotech Engineering Consultants, Inc., Taipei, Taiwan, jaylo@sinotech.org.tw,
Zamani, A
Department of Earth Sciences, College of Sciences, Shiraz University, Shiraz, Iran, zamani_a_geol@yahoo.com,
Azar, A.P
Department of Earth Sciences, College of Sciences, Shiraz University, Shiraz, Iran, kolahiazar@gmail.com,
Safavi, A.A.
School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran, safavi@shirazu.ac.ir,
Samui, P.
Centre for Disaster Mitigation and Management, VIT University, Vellore, India, pijush.phd@gmail.com,
Kim, D.
Department of Civil Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea, kim2kie@chol.com,
Abstract : This paper proposes to use least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of the magnitude (M) of induced earthquakes based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are used as input variables of the LSSVM and RVM. The output of the LSSVM and RVM is M. Equations have been presented based on the developed LSSVM and RVM. The developed RVM also gives variance of the predicted M. A comparative study has been carried out between the developed LSSVM, RVM, artificial neural network (ANN), and linear regression models. Finally, the results demonstrate the effectiveness and efficiency of the LSSVM and RVM models.

Keywords : trzęsienie ziemi, prognoza, maszyna wektorów nośnych, earthquake, prediction, support vector machine, least square,
Publishing house : Instytut Geofizyki PAN
Publication date : 2014
Number : Vol. 62, no. 3
Page : 608 – 619

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DOI :
Qute : Shiuly, A ,Kumar, V ,Narayan, J.P. ,Mousavian, R. ,Hossainali, M.M. ,Wiszniowski, J. ,Plesiewicz, B.M. ,Trojanowski, J. ,Agh-Atabai, M. ,Mirabedini, M.S. ,Rashed, M. ,Muduli, P.K ,Das, S.K. ,Liu, H. ,Lei, X ,Mao, C ,Li, S. ,Chou, P.-Y ,Hsu, S.-M ,Chen, P.-J ,Lin, J.-J. ,Lo, H.-C ,Zamani, A ,Azar, A.P ,Safavi, A.A. ,Samui, P. ,Kim, D. ,Kim, D. , Applicability of artificial intelligence to reservoir induced earthquakes. Acta Geophysica Vol. 62, no. 3/2014
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