Classification of mine microseismic events based on wavelet-fractal method and pattern recognition

High-precision location is the focus of hot research about microseismic technology, and classification of microseismic events is the basis for the location. Based on the previous studies, the fractal characteristics of microseismic signals are studied, and the range and scale-free fractal box dimensions of the algorithm are established. Using the spectral differences about mine blasting vibration, rock fracture and electromagnetic interference signals, and based on the wavelet analysis and the fractal theory, the microseismic signals are decomposed into 5 layers to gain specified frequency bands using MATLAB software. Then the box fractal dimensions about those specified frequency band reconstructed signals can be calculated. The 23-dimensional values of pattern recognition feature vector can be established. Finally, the support vector machine SVM is adopted to train, classify and recognize 300 sets of data. The results show that three types of signals have obvious fractal characteristics in the specified frequency bands, especially the electromagnetic interference signals. The fractal dimension box of specified frequency bands is close to the whole signal dimension box. The SVM network model with 23 fractal dimension vectors can be well used to recognize microseismic events, and the correct identification rate is 94%, with can meet the needs of the project site, but the efficiency of identification still needs further improvement.