Seismic Nowcasting Using Shannon Information Entropy with Copula Models and Artificial Neural Networks

Volume 3 - Issue 2

Moatafa Allameh Zadeh*

Received: July 21, 2020;   Published: August 21, 2020

DOI: 10.26717/CTBB.MS.ID.000156

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Abstract

Recent advances made in Nowcasting Earthquakes using clustering analysis techniques are being run by numerical simulations. In this paper, Shannon Information Entropy and Self-Organizing Maps Neural Networks (SOFM) with Copula Models are used to obtain Earthquakes cluster patterns such as doughnut patterns shapes. 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 of earthquake patterns to observe and synthetic seismic catalogue with Shannon Information technique. Earthquakes are first classified into different clusters by using Shannon Information Entropy, and then, patterns are discovered before large earthquakes with Copula 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 map is used on the seismic silence and the Doughnut pattern before large earthquakes.

Abstract| Introduction| Copula Method| Examples for Visualization Earthquake Patterns| Conclusion| References|