Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Support Vector Machines and MLP for automatic classification of seismic signals at Stromboli volcano
Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
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In this work, the applicability of the circular statistics to feature extraction on seismic signals is presented. The seismic signals are captured from Llaima Volcano, located in Southern Andes Volcanic Zone at 38°40'S 71°40'W. Typically, the seismic signals can be divided in long-period, tremor, and volcano-tectonic earthquakes. The seismic signals are time-segmented using a rectangular window of 1 minute of duration. In each segment, the instantaneous phase is calculated using the Hilbert Transform, and then, one feature is obtained. Thus, the principal hypothesis of this work is that the instantaneous phase can be assumed as a circular random variable in [0, 2π) interval. A second feature is obtained using the wavelet transform due to the fact that seismic signals present high energy located in low frequency. Then, in the range 1.55 and 3.11 Hz the wavelet coefficients were obtained and their mean energy is calculated as the second feature. Real seismic data represented using this two features are classified using a linear discriminant with a 92.5% of correct recognition rate.