Abstract
Rock bursts have become one of the most severe risks in underground coal mining and its forecasting is an important component in the safety management. Subsurface microseismic (MS) monitoring is considered potentially as a powerful tool for rock burst forecasting. In this study, a methodology for rock burst forecasting involving the use of a fuzzy comprehensive evaluation model was developed, which allows for a more quantitative evaluation of the likelihood for the occurrence of a rock burst incident. In the fuzzy model, the membership function was built using Gaussian shape combined with the exponential distribution function from the reliability theory. The weight of each index was determined utilising the performance metric F score from the confusion matrix. The comprehensive forecasting result was obtained by integrating the maximum membership degree principle (MMDP) and the variable fuzzy pattern recognition (VFPR). This methodology has been applied to a coal mine in China to forecast rock bursts. To select MS indices for rock burst forecasting using the fuzzy evaluation model, laboratory acoustic emission (AE) measurements of coal samples collected from the mine were performed. The model parameters were first calibrated using historical MS data over a period of four months, during which six rock burst incidents were observed. This calibrated model was able to forecast the occurrence of a subsequent rock burst incident in the mine.
Published Version
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