Prediction of coal and gas outbursts by a novel model based on multisource information fusion

Abstract

As one type of geologic disaster, coal and gas outbursts seriously threaten safe production in coal mines, restricting the sustainable development of the mining industry. However, coal and gas outbursts are difficult to forecast due to their uncertainty and the limitation of sample size, which affect the accuracy of the traditional prediction methods to some extent. Therefore, this study developed a novel model based on multisource information fusion to realize the predictive progress of coal and gas outburst disasters. Through the application of Dempster-Shafer theory, a method of multisource information fusion, the proposed model combined the results of different forecasting approaches, including conventional techniques and an emerging method based on artificial intelligence. To enhance the performance of the established model, this study improved Dempster-Shafer theory and verified its effectiveness in dealing with highly conflicting information. We then applied this model to the No. 3 coal seam of the Xinjing coal mine, Shanxi Province, China. The fused prediction accurately reflected the situation of outburst hazards and showed good compensation for false prediction. An analysis of the results concluded that the model based on multisource information fusion increases the credibility of the forecast, which might provide technical support for safe coal mine production.