An automatic recognition method of microseismic signals based on EEMD-SVD and ELM

Abstract

The recognition of microseismic and blasting signals is important for the prediction of geological disasters. Feature parameters for pattern recognition are usually designed manually, which is inconvenient to adopt. A new method combining ensemble empirical mode decomposition (EEMD), singular value decomposition (SVD) and extreme learning machine (ELM) was proposed. The method applied EEMD to decompose microseismic signals into multiple intrinsic mode functions (IMF) and used SVD to extract eigenvalues from matrices ‘composed of these IMF components. After getting the feature vectors, ELM was introduced to establish a classification model to automatically identify 500 signals of Houziyan hydropower station in Southwest China. Experimental results suggested that the singular values obtained by the EEMD-SVD method could effectively reflect the key characteristics of the signals. Furthermore, the recognition result of eliminating noise components and false components was better than that of other vector combinations. Compared to the outputs of other machine learning algorithms, ELM performed better than back-propagation neural network, neural network optimized by genetic algorithm and support vector machine. The prediction accuracy and the Matthew's Correlation Coefficient of the model reached as high as 93.85%, and 87.70%, respectively, while the training time was only 0.152s.