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Mathematical Problems in Engineering
Volume 2014, Article ID 456818, 6 pages
Research Article

A New Classification Method of Infrasound Events Using Hilbert-Huang Transform and Support Vector Machine

1School of Information Engineering, University of Geosciences (Beijing), Beijing 100083, China
2Comprehensive Nuclear-Test-Ban Treaty Beijing National Data Center, Beijing 100085, China
3School of Water Resources and Environment, University of Geosciences (Beijing), Beijing 100083, China

Received 27 March 2014; Revised 31 May 2014; Accepted 23 June 2014; Published 6 July 2014

Academic Editor: Alkiviadis Paipetis

Copyright © 2014 Xueyong Liu et al. 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.


Infrasound is a type of low frequency signal that occurs in nature and results from man-made events, typically ranging in frequency from 0.01 Hz to 20 Hz. In this paper, a classification method based on Hilbert-Huang transform (HHT) and support vector machine (SVM) is proposed to discriminate between three different natural events. The frequency spectrum characteristics of infrasound signals produced by different events, such as volcanoes, are unique, which lays the foundation for infrasound signal classification. First, the HHT method was used to extract the feature vectors of several kinds of infrasound events from the Hilbert marginal spectrum. Then, the feature vectors were classified by the SVM method. Finally, the present of classification and identification accuracy are given. The simulation results show that the recognition rate is above 97.7%, and that approach is effective for classifying event types for small samples.