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Entropy 2013, 15(9), 3892-3909; doi:10.3390/e15093892
Article

Analysis and Visualization of Seismic Data Using Mutual Information

1,*  and 2
Received: 26 July 2013 / Revised: 10 September 2013 / Accepted: 12 September 2013 / Published: 16 September 2013
(This article belongs to the Special Issue Dynamical Systems)
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Abstract

Seismic data is difficult to analyze and classical mathematical tools reveal strong limitations in exposing hidden relationships between earthquakes. In this paper, we study earthquake phenomena in the perspective of complex systems. Global seismic data, covering the period from 1962 up to 2011 is analyzed. The events, characterized by their magnitude, geographic location and time of occurrence, are divided into groups, either according to the Flinn-Engdahl (F-E) seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Two methods of analysis are considered and compared in this study. In a first method, the distributions of magnitudes are approximated by Gutenberg-Richter (G-R) distributions and the parameters used to reveal the relationships among regions. In the second method, the mutual information is calculated and adopted as a measure of similarity between regions. In both cases, using clustering analysis, visualization maps are generated, providing an intuitive and useful representation of the complex relationships that are present among seismic data. Such relationships might not be perceived on classical geographic maps. Therefore, the generated charts are a valid alternative to other visualization tools, for understanding the global behavior of earthquakes.
Keywords: seismic events; mutual information; clustering; visualization seismic events; mutual information; clustering; visualization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Machado, J.A.T.; Lopes, A.M. Analysis and Visualization of Seismic Data Using Mutual Information. Entropy 2013, 15, 3892-3909.

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