Fractal Analysis, Information-Theoretic Similarities and SVM Classification for Multichannel, Multi-Frequency Pre-Seismic Electromagnetic Measurements
- *Corresponding Author:
- Dimitrios Nikolopoulos
Department of Electronic Computer Systems Engineering
Piraeus University of Applied Sciences
Petrou Ralli & Thivon 250, GR-12244 Aigaleo
E-mail: [email protected]
Received date: July 11, 2016; Accepted date: July 29, 2016; Published date: August 04, 2016
Citation: Cantzos D, Nikolopoulos D, Petraki E, Yannakopoulos PH, Nomicos C (2016) Fractal Analysis, Information-Theoretic Similarities and SVM Classification for Multichannel, Multi-Frequency Pre-Seismic Electromagnetic Measurements . J Earth Sci Clim Change 7: 367. doi: 10.4172/2157-7617.1000367
Copyright: © 2016 Cantzos D, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
A multichannel, multi-frequency approach on the analysis of critical electromagnetic (EM) signatures prior to earthquakes is presented. The algorithm employed is based on single-channel techniques for the identification of longmemory trends in fractal processes and attempts to take advantage of the increased information content that is provided by multichannel EM recordings. The EM measurements consist of four channels which correspond to four distinct EM radiation frequencies. Two of these frequencies lie in the kHz range and the other two in the MHz range. Our analysis of a three-month EM activity period shows that there exists some degree of similarity between EM channels that are close in frequency, in terms of an information theoretic measure. More importantly, the multichannel-based detection results seem to be in close agreement with the main earthquake occurrences of the three-month period. The combined output of the multiple channels is used to train a Support Vectors Machine (SVM) classifier in order to identify precursory EM signal segments of forthcoming seismic events and a high accuracy rate is reported.