Huang H.,Guangji Normal University |
Bian Y.,China Earthquake Administration |
Lu S.,Guangji Normal University |
Jiang Z.,Guangji Normal University |
Li R.,Guangji Normal University
Acta Seismologica Sinica | Year: 2010
Research on how to extract seismic wave features from earthquakes and explosions and how to discriminate explosions from earthquakes based on these features. Firstly, the transform of 4-layer wavelet packet is performed on the wave records. Secondly, the last layer coefficients of wavelet packet from the transform are employed to extract 3 types of wave features: energy ratio, Shannon entropy and logarithmic energy entropy. Thirdly, these features are supplied to a classifier of v-SVC support vector machines for verifying the capabilities of these features. In order to weaken undesirable effect of event epicenter-distance and magnitude on the recognition, we tried to extract more essential features of the wave records gathered from different regions, different observatories and various events almost covering whole magnitude ranges. The results show that, among the above three features, the feature of Shannon entropy is the best candidate for discriminating explosions from earthquakes. This may be an effective criterion in explosion recognition.