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JACIII Vol.18 No.5 pp. 701-713
doi: 10.20965/jaciii.2014.p0701
(2014)

Paper:

Artificial Neural Networks for Earthquake Anomaly Detection

Aditya Sriram, Shahryar Rahanamayan, and Farid Bourennani

University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, Ontario L1H 7K4, Canada

Received:
October 29, 2013
Accepted:
March 6, 2014
Published:
September 20, 2014
Keywords:
artificial neural networks, ANN, earthquake anomalies detection, precursor, earthquake prediction
Abstract

Earthquakes are natural disasters caused by an unexpected release of seismic energy from extreme levels of stress within the earth’s crust. Over the years, earthquake prediction has been a controversial research subject that has challenged even the smartest ofminds. Because numerous seismic precursors and other factors exist that may indicate the potential of an earthquake occurring, it is extremely difficult to predict the exact time, location, and magnitude of an impending quake. Nevertheless, evaluating a combination of these precursors through advances in Artificial Intelligence (AI) can certainly increase the possibility of predicting an earthquake. The sole purpose for predicting a seismic event at a pre-determined locality is to provide substantial time for the citizens to take precautionary measures. With this in mind, Artificial Neural Networks (ANNs) have been promising techniques for the detection and prediction of locally impending earthquakes based on valid seismic information. To highlight the recent trends in earthquake abnormality detection, including various ideas and applications, in the field of Neural Networks, valid papers related to ANNs are reviewed and presented herein.

References
  1. [1] A. K. Jain, J. Mao, and K. M. Mohiuddin, “Artificial neural networks: A tutorial,” Computer, Vol.29, No.3, pp. 31-44, 1996.
  2. [2] M. Negnevitsky, “Artificial intelligence: a guide to intelligent systems,” Pearson Education, 2005.
  3. [3] K. Wang, Q. F. Chen, S. Sun, and A. Wang, “Predicting the 1975 Haicheng earthquake,” Bulletin of the Seismological Society of America, Vol.96, No.3, pp. 757-795, 2006.
  4. [4] C. H. Scholz, “A physical interpretation of the Haicheng earthquake prediction,” Nature, Vol.267, No.5607, pp. 121-124, 1977.
  5. [5] F. Zhu and G. Wu, “Haicheng earthquake in 1975,” 1982.
  6. [6] A. Jin and K. Aki, “Temporal change in coda q before the Tangshan earthquake of 1976 and the Haicheng earthquake of 1975,” J. of Geophysical Research: Solid Earth (1978-2012), Vol.91, No.B1, pp. 665-673, 1986.
  7. [7] M. Li, Z. Lieyuan, and S. Yaolin, “Attempts at using seismicity indicators for the prediction of large earthquakes by genetic algorithmneural network method,” 1998.
  8. [8] P. Lussou, P. Y. Bard, F. Cotton, and Y. Fukushima, “Seismic design regulation codes: contribution of k-net data to site effect evaluation,” J. of Earthquake Engineering, Vol.5, No.1, pp. 13-33, 2001.
  9. [9] I. M. Idriss, “Characteristics of earthquake ground motions,” Proc. of the ASCE Geotechnical Engineering Division Speciality Conf.: Earthquake Engineering and Soil Dynamics, Vol.3, pp. 1151-1265, 1978.
  10. [10] D. M. Boore and W. B. Joyner, “The empirical prediction of ground motion,” Bulletin of the Seismological Society of America, Vol.72, No.6B, pp. 43-60, 1982.
  11. [11] K. W. Campbell, “Strong motion attenuation relations: a ten-year perspective,” Earthquake Spectra, Vol.1, No.4, pp. 759-804, 1985.
  12. [12] J. Douglas, “Consistency of ground-motion predictions from the past four decades: peak ground velocity and displacement, arias intensity and relative significant duration,” Bulletin of Earthquake Engineering, Vol.10, No.5, pp. 1339-1356, 2012.
  13. [13] C. C. Lin and J. Ghaboussi, “Recent progress on neural network based methodology for generating artificial earthquake accelerograms,” ICSSD 2000: 1st Structural Conf. on Structural Stability and Dynamics, pp. 793-798, 2000.
  14. [14] A. T. Goh, “Probabilistic neural network for evaluating seismic liquefaction potential,” Canadian Geotechnical J., Vol.39, No.1, pp. 219-232, 2002.
  15. [15] B. Derras and A. Bekkouche, “Use of the artificial neural network for peak ground acceleration estimation,” Lebanese Science J., Vol.12, No.2, p. 101, 2011.
  16. [16] J. A. Meredith, R. H. Wilkens, and C. H. Cheng, “Evaluation and prediction of shear wave velocities in soft marine sediments,” Technical report, Massachusetts Institute of Technology, Earth Resources Laboratory, 1989.
  17. [17] K. Günaydn and A. Günaydn, “Peak ground acceleration prediction by artificial neural networks for northwestern turkey,” Mathematical Problems in Engineering, 2008.
  18. [18] N. N. Ambraseys and J. Douglas, “Near-field horizontal and vertical earthquake ground motions,” Soil dynamics and earthquake engineering, Vol.23, No.1, pp. 1-18, 2003.
  19. [19] T. Kerh and D. Chu, “Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion,” Advances in Engineering Software, Vol.33, No.11, pp. 733-742, 2002.
  20. [20] T. Kerh and S. B. Ting, “Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system,” Engineering Applications of Artificial Intelligence, Vol.18, No.7, pp. 857-866, 2005.
  21. [21] B. Derras, A. Bekkouche, and D. Zendagui, “Neuronal approach and the use of kik-net network to generate response spectrum on the surface,” 2013.
  22. [22] E. Bojórquez, J. Bojórquez, S. E. Ruiz, and A. Reyes-Salazar, “Prediction of inelastic response spectra using artificial neural networks,” Mathematical Problems in Engineering, 2012.
  23. [23] V. Kumar, K. Venkatesh, and R. P. Tiwari, “Application of ANN to predict liquefaction potential.”
  24. [24] A. Panakkat and H. Adeli, “Neural network models for earthquake magnitude prediction using multiple seismicity indicators,” Int. j. of neural systems, Vol.17, No.1, pp. 13-33, 2007.
  25. [25] H. Adeli and A. Panakkat, “A probabilistic neural network for earthquake magnitude prediction,” Neural Networks, Vol.22, No.7, pp. 1018-1024, 2009.
  26. [26] A. Panakkat and H. Adeli, “Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators,” Computer-Aided Civil and Infrastructure Engineering, Vol.24, No.4, pp. 280-292, 2009.
  27. [27] P. Nuannin, “The potential of b-value variations as earthquake precursors for small and large events,” Ph.D. thesis, Uppsala University, 2006.
  28. [28] T. Utsu, “Representation and analysis of the earthquake size distribution: a historical review and some new approaches,” Seismicity Patterns, their Statistical Significance and Physical Meaning, Springer, pp. 509-535, 1999.
  29. [29] S. Perez, “Apply genetic algorithm to the learning phase of a neural network.”
  30. [30] D. J. Montana and L. Davis, “Training feedforward neural networks using genetic algorithms,” IJCAI, Vol.89, pp. 762-767, 1989.
  31. [31] A. M. Esteban, F. M. Álvarez, A. Troncoso, J. L. Justo, and C. R. Escudero, “Pattern recognition to forecast seismic time series,” Expert Systems with Applications, Vol.37, No.12, pp. 8333-8342, 2010.
  32. [32] C. F. Richter, “An instrumental earthquake magnitude scale,” Bull. Seism. Soc. Am, Vol.25, No.1, pp. 1-32, 1935.
  33. [33] D. Schorlemmer, S. Wiemer, and M. Wyss, “Variations in earthquake-size distribution across different stress regimes,” Nature, Vol.437, No.7058, pp. 539-542, 2005.
  34. [34] Y. M. Htwe and S. WenBin, “Gutenberg-Richter recurrence law to seismicity analysis of southern segment of the sagaing fault and its associate components.”
  35. [35] A. Zollo, W. Marzocchi, P. Capuano, A. Lomax, and G. Iannaccone, “Space and time behavior of seismic activity at Mt. Vesuvius volcano, Southern Italy,” Bulletin of the Seismological Society of America, Vol.92, No.2, pp. 625-640, 2002.
  36. [36] Y. Shi and B. A. Bolt, “The standard error of the magnitudefrequency b value,” Bulletin of the Seismological Society of America, Vol.72, No.5, pp. 1677-1687, 1982.
  37. [37] J. Reyes, A. M. Esteban, and F. M. Álvarez, “Neural networks to predict earthquakes in Chile,” Applied Soft Computing, 2012.
  38. [38] S. Hainzl and D. Marsan, “Dependence of the Omori-Utsu law parameters on main shock magnitude: Observations and modeling,” J. of Geophysical Research: Solid Earth (1978-2012), Vol.113, No.B10, 2008.
  39. [39] A. M. Farahbod and M. Allamehzadeh, “Large aftershocks prediction results in eastern and central Iran using artificial neural networks (ANN’s),” 3rd Int. Conf. on Seismology and Earthquake Engineering, 1999.
  40. [40] V. Barrile, M. Cacciola, S. D’Amico, A. Greco, F. C. Morabito, and F. Parrillo, “Radial basis function neural networks to foresee aftershocks in seismic sequences related to large earthquakes,” Neural Information Processing, Springer, pp. 909-916, 2006.
  41. [41] S. Wiemer, M. Gerstenberger, and E. Hauksson, “Properties of the aftershock sequence of the 1999 mw 7.1 hector mine earthquake: Implications for aftershock hazard,” Bulletin of the Seismological Society of America, Vol.92, No.4, pp. 1227-1240, 2002.
  42. [42] G. Ranalli, “A statistical study of aftershock sequences,” Annals of Geophysics, Vol.53, No.1, pp. 43-58, 2010.
  43. [43] R. F. Holub and B. T. Brady, “The effect of stress on Radon emanation from rock,” J. of Geophysical Research: Solid Earth (1978-2012), Vol.86, No.B3, pp. 1776-1784, 1981.
  44. [44] V. I. Ulomov, A. I. Zakharova, and N. V. Ulomova, “Tashkent earthquake of April 26, 1966, and its aftershocks,” Akad. Nauk. SSSR, Geophysic, Vol.177, pp. 567-570, 1967.
  45. [45] M. Noguchi and H. Wakita, “A method for continuous measurement of Radon in groundwater for earthquake prediction,” J. of Geophysical Research, Vol.82, No.8, pp. 1353-1357, 1977.
  46. [46] C. Y. King, “Episodic Radon changes in subsurface soil gas along active faults and possible relation to earthquakes,” J. of geophysical research, Vol.85, No.B6, pp. 3065-3078, 1980.
  47. [47] R. C. Ramola, M. Singh, A. S. Sandhu, S. Singh, and H. S. Virk, “The use of Radon as an earthquake precursor,” The Int. j. of radiation applications and instrumentation. Nuclear geophysics, Vol.4, No.2, pp. 275-287, 1990.
  48. [48] B. Singh and H. S. Virk, “Investigation of Radon-222 in soil-gas as an earthquake precursor,” The Int. j. of radiation applications and instrumentation. Part E. Nuclear geophysics, Vol.8, No.2, pp. 185-193, 1994.
  49. [49] D. P. Loomis, “Radon-222 concentration and aquifer lithology in north Carolina,” Ground Water Monitoring & Remediation, Vol.7, No.2, pp. 33-39, 1987.
  50. [50] P. T. King, J. Michel, and W. S. Moore, “Ground water geochemistry of 228 Ra, 226 Ra and 222 Rn,” Geochimica et Cosmochimica Acta, Vol.46, pp. 1173-1182, 1982.
  51. [51] G. Igarashi, S. Saeki, N. Takahata, K. Sumikawa, S. Tasaka, Y. Sasaki, M. Takahashi, and Y. Sano, “Ground-water Radon anomaly before the Kobe earthquake in japan,” Science-New York Then Washington, pp. 60-60, 1995.
  52. [52] R. C. Ramola, Y. Prasad, G. Prasad, S. Kumar, and V. M. Choubey, “Soil-gas Radon as seismotectonic indicator in Garhwal Himalaya,” Applied Radiation and Isotopes, Vol.66, No.10, pp. 1523-1530, 2008.
  53. [53] A. Gregoric, B. Zmazek, S. Džeroski, D. Torkar, and J. Vaupotic, “Radon as earthquake precursor-methods for detecting anomalies,” Earthquake Research and Analysis, 2011.
  54. [54] B. Zmazek, M. Živcic, L. Todorovski, S. Džeroski, J. Vaupotic, and I. Kobal, “Radon in soil gas: how to identify anomalies caused by earthquakes,” Applied geochemistry, Vol.20, No.6, pp. 1106-1119, 2005.
  55. [55] B. Zmazek, S. Dzeroski, D. Torkar, J. Vaupotic, and I. Kobal, “Identification of Radon anomalies in soil gas using decision trees and neural networks.”
  56. [56] D. Torkar, B. Zmazek, J. Vaupotic, and I. Kobal, “Application of artificial neural networks in simulating Radon levels in soil gas,” Chemical Geology, Vol.270, No.1, pp. 1-8, 2010.
  57. [57] J. Planinic, V. Radolic, and Ž. Lazanin, “Temporal variations of Radon in soil related to earthquakes,” Applied Radiation and isotopes, Vol.55, No.2, pp. 267-272, 2001.
  58. [58] X. T. Feng and M. Seto, “Neural network dynamic modelling of rock microfracturing sequences under triaxial compressive stress conditions,” Tectonophysics, Vol.292, No.3, pp. 293-309, 1998.
  59. [59] A. Negarestani, S. Setayeshi, M. G. Maragheh, and B. Akashe, “Layered neural networks based analysis of Radon concentration and environmental parameters in earthquake prediction,” J. of environmental radioactivity, Vol.62, No.3, pp. 225-233, 2002.
  60. [60] J. L. Pinault and J. C. Baubron, “Signal processing of soil gas Radon, atmospheric pressure, moisture, and soil temperature data: A new approach for Radon concentration modeling,” J. of geophysical research, Vol.101, No.B2, pp. 3157-3171, 1996.
  61. [61] A. Negarestani, S. Setayeshi, M. G. Maragheh, and B. Akashe, “Estimation of the Radon concentration in soil related to the environmental parameters by a modified Adaline neural network,” Applied radiation and isotopes, Vol.58, No.2, pp. 269-273, 2003.
  62. [62] D. Gupta and D. T. Shahani, “Estimation of Radon as an earthquake precursor: A neural network approach,” J. of the Geological Society of India, Vol.78, No.3, pp. 243-248, 2011.
  63. [63] J. P. Toutain and J. C. Baubron, “Gas geochemistry and seismotectonics: a review,” Tectonophysics, Vol.304, No.1, pp. 1-27, 1999.
  64. [64] R. C. Ramola, “Relation between spring water Radon anomalies and seismic activity in Garhwal Himalaya,” Acta Geophysica, Vol.58, No.5, pp. 814-827, 2010.

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