Data Mining Technology and Its Applications in Coal and Gas Outburst Prediction

Abstract

Coal and gas outburst accidents seriously threaten mine production safety. To further improve the scientific accuracy of coal and gas outburst risk prediction, a system software (V1.2.0) was developed based on the C/S architecture, Visual Basic development language, and SQL Server 2000 database. The statistical process control (SPC) method and logistic regression analyses were used to assess and develop the critical value of outburst risk for a single index, such as the S value of drill cuttings and the K1 value of the desorption index. A multivariate information coupling analysis was performed to explore the interrelation of the outburst warning, and the prediction equation of the outburst risk was obtained on this basis. Finally, the SPC and logistic regression analysis methods were used for typical mines. The results showed that the SPC method accurately determined the sensitivity value of a single index for each borehole depth, and the accuracy of the logistic regression method was 94.7%. These methods are therefore useful for the timely detection of outburst hazards during the mining process.