Experimental investigation on acoustic emission precursor of rockburst based on unsupervised machine learning method

Abstract

The key to achieving rockburst warning lies in the understanding of rockburst precursors. Considering the correlation characteristics of rockburst acoustic emission (AE) parameters, a self-organizing map neural network (SOMNN) based method for rockburst precursor inversion was proposed. The feature of this method lies in a cyclic data segmentation iteration process based on the thinking of “interference signal screening”, “key signal extraction”, and “precursor signal inversion”. The rationality of this method has been verified in three groups of rockburst experiments. The results revealed that rockburst AE precursor signals consist of a series of signals characterized by long duration, high energy, low average frequency, high energy amplitude, and low peak frequency. Subsequently, potential value in long term rockburst warning of the precursor obtained in this study was shown via the comparison of conventional precursors. Finally, a preliminary interpretation for rockburst precursor was proposed under the framework of AE parameters physical significance, and it is revealed that AE precursor signals are likely linked to the creation of large-scale tensile cracks before rockburst.