A decision support system for classification and recognition of earthquakes and explosions

This paper introduces current advances and some rudimental results of our ongoing research project. To discriminate between earthquakes and explosions, temporal and spectral features extracted from seismic waves, plus some seismological parameters (such as epicenter depth, location, magnitude) are c...

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Bibliographic Details
Published inThe 2nd International Conference on Software Engineering and Data Mining pp. 32 - 35
Main Authors Huang Hanming, Bi Ming Xia, Shi Xinhua, Zhou Haijun, Zhao Jing, Chen Yinyan, Bian Yin Ju
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Chirp
classification
Decision support systems
earthquake
Earthquakes
explosion
Explosions
Feature extraction
Fourier transforms
Seismic waves
Seismology
Static VAr compensators
SVC
tempo-spectral features
Wavelet transforms
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Summary:This paper introduces current advances and some rudimental results of our ongoing research project. To discriminate between earthquakes and explosions, temporal and spectral features extracted from seismic waves, plus some seismological parameters (such as epicenter depth, location, magnitude) are crux for rapid and correct recognizing event sources (earthquakes or explosions). Seismological parameters are used as the first step to screen out obvious earthquake events. Fourier transforms (FFT), chirp-Z transforms, wavelet transforms have been conducted and some prominent features are acquired by present experimental dataset. In some experiments, wavelet features plus support vector classification (SVC) have reached very high correct recognition rate (>95%). If more temporal and spectral features are utilized properly, more robust recognition result is surely possibility.
ISBN:1424473241
9781424473243