Application of Copula Theory to Develop Techniques for Earthquakes Forecasting

Document Type : Seismology and Engineering Seismology


1 International Institute of Earthquake Engineering and Seismology (IIEES)

2 University of Hormozgan, Bandar Abbas


Recent advances made in forecasting Earthquakes using clustering analysis techniques are being run by numerical simulations. In this paper, the Gaussian Copula clustering technique is used to obtain Earthquake patterns such as the Doughnut Earthquake pattern to better predict medium and large events. Copulas methods can involve recognizing precursory seismic patterns before a large earthquake within a specific region occurs. The observed data represent seismic activities situated around IRAN in the 1980-2014 time intervals. This technique is based on applying cluster analysis of earthquake patterns to observe and synthetic seismic catalog. Earthquakes are first classified into different clusters, and then, patterns are discovered before large earthquakes via Copulas simulation. The results of the experiments show that recognition rates achieved within this system are much higher than those achieved only during the feature map is used on the seismic silence and the Doughnut pattern before large earthquakes.


  1. Field, E.H., Dawson, T.E., Felzer, K.R., Frankel, A.D., Gupta, V., Jordan, T.H., Parson, T., Petersen, M.D., Stein, R.S., Weldon II, R.J., and Wills, C.J. (2009) Uniform California eathquake rupture forecast, Version 2 (UCERF2). Bulletin of the Seismological Society of America , 99(4), 2053-2107.
  2. Allamehzadeh, M. and Mostafazadeh, M. (2014) Determination of concentration of earthquake clustering. 7th International Conference on Seismology of Earthquake Enginerign (SEE7).
  3. Nelsen, R.B. (2006) An Introduction to Copulas. Springer Series in Statistics.
  4. Joe, H. (1997) Multivariate models and dependence concepts. Vol. 73 of Monographs on Statistics and Applied Probability, Chapman & Hall, London, UK.
  5. Jones, L.M. and Molnar, P. (1976) Frequency of foreshocks. Nature, 62, 677-679.
  6. Mogi, K. (1968) Development of aftershock areas of great earthquakes. Bull. Earthq. Res. Inst., 46, 175-203.
  7. Allamehzadeh, M., Mostafazadeh, M., and Mahshadnia, L. (2013) Developed Sophisticated Pattern Recognition of Earthquake Location, Simulation Alborz Region. Report Project No. 9604-93-6 at IIEES.
  8. Pitt, M., Chan, D., and Kohn, R. (2006) Efficient bayesian inference for Gaussian Copula regression. Biometrika , 93, 537-554.