Mine-Microseismic-Signal Recognition Based on LMD–PNN Method

Abstract

The effective recognition of microseismic signal is related to the accuracy of mine-dynamic-disaster precursor-information processing, which is a difficult method of microseismic-data processing. A mine-microseismic-signal-identification method based on LMD energy entropy and the probabilistic neural network (PNN) is proposed. First, the Local-Mean-Decomposition (LMD) method is used to decompose the mine microseismic signal. Considering the problem of vector redundancy, combined with the correlation-coefficient method, the energy entropy of the effective product-function component (PF) is extracted as the feature vector of mine-microseismic-signal classification. Furthermore, the probabilistic neural network (PNN) is used for learning and training, and the blasting-vibration signal and the coal–rock-mass-rupture signal are effectively identified. The test results show that the recognition accuracy of the PNN is up to 90%, the calculation time and classification effect of the PNN are better, and the recognition accuracy is increased by 15% and 7.5%, respectively, compared with the traditional PBNN and GRNN. This method can accurately and effectively identify the microseismic signals of mines and has good generalization performance.

Keywords

• microseismic-signal recognition
• local mean decomposition (LMD)
• energy entropy
• probabilistic neural network (PNN)
• correlation analysis