Abstract Due to the large amount of environmental noise in seismic signals, we establish the initial data set based on natural earthquake events and artificial blasting events, decompose and reduces the noise in waveform signals using the ensemble empirical mode decomposition(EEMD) technique, extract the purer intrinsic mode function(IMF) components of each order, and then calculate the distribution entropy for the first 10 order components separately to establish the neural network input matrix. The whale optimization algorithm(WOA) is applied to optimize the self-organizing feature mapping(SOM) neural network parameters(competitive layer dimensions and number of network training) to find the corresponding optimal parameter values for different training samples to improve the stability of pattern recognition, thus improving the seismic recognition rate. The results show that the EEMD multiscale distribution entropy combined with WOA-SOM model can effectively identify natural earthquakes and artificial blasting events.
PANG Cong,WANG Lei,MA Wugang et al. EEMD Multiscale Distribution Entropy Extraction and WOA-SOM Recognition between Seismic and Blast Waveform Signals[J]. jgg, 2022, 42(9): 980-984.
PANG Cong,WANG Lei,MA Wugang et al. EEMD Multiscale Distribution Entropy Extraction and WOA-SOM Recognition between Seismic and Blast Waveform Signals[J]. jgg, 2022, 42(9): 980-984.