Rock burst early-warning for thick coal seam in deep mining based on Logistic regressio

Abstract

For difficult rock burst monitoring and early-warning for thick coal seam in deep mining, a rock burst early-warning model based on Logistic regression was established by use of conventional indexes (support resistance, borehole stress and gas concentration) as well as the one by use of comprehensive indexes including the conventional indexes (support resistance, borehole stress and gas concentration) and geophysical indexes (average focal distance, daily pulses and daily energy of microseism, and intensity of electromagnetic radiation). Quantitative expression between probability of rock burst occurrence and comprehensive indexes was obtained. Finally, the rock burst early-warning models were tested by use of measured data in Qianqiu Coal Mine. The research results show that forecasting accuracy rate of the rock burst early-warning model based on Logistic regression by use of comprehensive indexes achieves 89.2%, whose goodness of fit and forecasting accuracy rate is higher than the ones of the model by use of the conventional indexes.

Keywords

• coal mining
• thick coal seam in deep mining
• rock burst
• monitoring and early-warning
• logistic regressio