alexa Identifying Long-Memory Trends in Pre-Seismic MHz Distu
ISSN: 2157-7617

Journal of Earth Science & Climatic Change
Open Access

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Research Article

Identifying Long-Memory Trends in Pre-Seismic MHz Disturbances through Support Vector Machines

Cantzos D1, Nikolopoulos D2*, Petraki E3, Nomicos C4, Yannakopoulos PH2and Kottou S5
1TEI of Piraeus, Department of Automation Engineering, Petrou Ralli & Thivon 250, GR-12244 Aigaleo, Greece
2TEI of Piraeus, Department of Electronic Computer Systems Engineering, Petrou Ralli & Thivon 250, GR-12244 Aigaleo, Greece
3Brunel University, Department of Engineering and Design, Kingston Lane, Uxbridge, Middlesex UB8 3PH, London, UK
4TEI of Athens, Department of Electronic Engineering, Agiou Spyridonos, GR-12243, Aigaleo, Greece
5University of Athens, Medical School, Department of Medical Physics, Mikras Asias 75, GR-11527 Athens, Greece
Corresponding Author : Nikolopoulos D
TEI of Piraeus
Department of Electronic
Computer Systems Engineering
Petrou Ralli and Thivon 250
GR-12244 Aigaleo, Greece
Tel: +0030-210-5381560
E-mail: [email protected]
Received December 30, 2014; Accepted February 18, 2015; Published February 28, 2015
Citation: Cantzos D, Nikolopoulos D, Petraki E, Nomicos C, Yannakopoulos PH, et al. (2015) Identifying Long-Memory Trends in Pre-Seismic MHz Disturbances through Support Vector Machines. J Earth Sci Clim Change 6:263. doi: 10.4172/2157-7617.1000263
Copyright: ©2013 Jung YG. 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.
 

Abstract

In this paper, a novel algorithm is introduced for the analysis of long-memory patterns hidden in electromagnetic (EM) readings prior to earthquakes. The algorithm builds upon previous work on long-memory detection in EM measurements by fusing Support Vector Machine (SVM) classifiers with well-deployed power law fit tests and Rescaled-Range (R/S) time-series variability methods. To apply the algorithm, fractal power law in the wavelet domain is assessed so as to identify fractional Brownian motion (fBm) segments of continuously monitored pre-earthquake EM activity. The selected segments are then further processed through R/S Analysis in order to further refine the detection of prominent fBm behaviour. The combined output of the two methods is used to train a SVM classifier which is subsequently employed to verify similar fBm states in existing EM data and to allow for rapid fBm detection in large data sequences of unprocessed or newly incoming EM readings. The SVM classifier is added in a modular fashion, on top of pre-earthquake monitoring algorithms, and can be trained with a small fraction of a huge available dataset of EM readings. Three earthquake events in Greece, corresponding to different time occurrences and geographic locations, were investigated. For each of the three earthquakes, data collected by a nearby EM measurement station one month prior to the peak event were analysed by the proposed method. The results yielded an overall accuracy rate of at least 90% for the detection of specific, prominent fBm segments despite the fact that the fBm profile in the three investigated earthquake sequences was very different.

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