earthquakes probability density model in copula methods in earthquakes forecasting

Recent advances made in forecasting earthquakes using clustering analysis techniques are being run by numerical simulations. in this paper, the gaussian copula clustering technique and self-organizing neural networks are used toobtain earthquakes cluster patterns such as doughnut shapes. copulas methods and sofm 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 1970-2014 time intervals. this technique is based on applying cluster analysis ofearthquake patterns to observe and synthetic seismic catalogue. earthquakes are first classified into different clusters, and then, patterns are discovered before large earthquakes via copulas and sofm simulation. the results of the experiments show that recognition rates achieved within this system are much higher than those achieved only during the feature mapis used on the seismic silence and the doughnut pattern before large earthquakes.