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Abstract A new model and method of identification precursor information was presented based on the monitoring of ground sound to provide an early warning of rockburst. Using the actual monitored data of ground sound as the training samples，eleven multidimensional feature vectors characterizing the rockburst hazard precursors were obtained from the ground sound signals in a fixed time window by the method of time-frequency domain feature extraction. A new support vector machine(SVM) learning method was applied to solve the training problems with imbalanced data sets in the application of large-scale engineering practice and to improve the classification accuracy and the training speed of SVM. Adopting the measured data as the learning samples on SVMs for training and establishing the appropriate precursor identification model，the accuracy reached 93.87%. Experiments show that this method is effective and reliable，and which is capable of fast sample identification meeting the online monitoring requirements.
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Received: 28 May 2013
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2014, Vol. 33 
