Mine microseismic events classification based on improved wavelet decomposition and ELM

The widely used mine micro-seismic monitoring system generates a large number of micro-seismic signals and has a variety of complex background noise disturbances, making it difficult to identify mine micros-eismic events.However, the existing identification methods have problems such as low noise reduction efficiency, obvious delay, and poor accuracy.In the process of wavelet decomposition, the wavelet threshold and wavelet decomposition level are determined firstly, and then the wavelet coefficients are quantized by using the improved wavelet threshold function to get the optimized wavelet coefficients.Finally, the wavelet coefficients are reconstructed to get the de-noising signal.This method effectively improves the current soft and hard threshold function of the existence of pseudo Gibbs phenomenon and discontinuity, large error defects.Secondly, the characteristics of the de-noised micro-seismic signal are extracted and the number of nodes in the ELM hidden layer is trained, and the improved ELM solves the problem that the number of nodes in the hidden layer could not be correctly selected in the training data using traditional ELM, which improves the identification accuracy of micro-seismic events.Finally, the improved ultimate learning machine can identify mine micro-seismic events more effectively.The results show that the classification accuracy of the mine microseismic event recognition method based on improved wavelet decomposition and ELM is 91.1%, which verifies the effectiveness and accuracy of the proposed method, and the method can further improve the identification accuracy by adding micro-seismic signal data. © 2020, Editorial Office of Journal of China Coal Society. All right reserved.