Abstract Adopting natural earthquakes and artificial explosion waveform record events of Fujian region, through one dimensional discrete wavelet transform(DWT) and 4-layer wavelet packet transform(WPT) for signal decomposition, we use the extract to identify four waveform little potter characters: the wavelet energy than characteristics, wavelet packet energy than features, wavelet packet Shannon entropy, logarithmic of wavelet packet energy entropy.Inaddition, we extract the original waveform P/S seismic phase amplitude ratio.We use BP neural network to test the recognition effect of four kinds of wavelet characteristics and add the characteristics of P/S seismic phase amplitude ratio respectively. The results show that the wavelet energy ratio feature recognition is effective. The combination of P/S seismic phase amplitude ratio and wavelet packet logarithmic energy entropy has the best recognition effect, which can be considered as the identification criterion for the online automatic identification system of natural earthquake and artificial explosion.
CAI Xinghui,ZHANG Yanming,CHEN Huifang et al. Automatic Identification of Earthquake and Explosion Based on Wavelet Transform and Neural Network[J]. jgg, 2020, 40(6): 634-639.
CAI Xinghui,ZHANG Yanming,CHEN Huifang et al. Automatic Identification of Earthquake and Explosion Based on Wavelet Transform and Neural Network[J]. jgg, 2020, 40(6): 634-639.