Dynamic prediction of coal and gas outburst risk based on quantitative analysis of stress–energy evolution and adaptive correction

Abstract

Accurate identification of coal and gas outburst (CGO) risk zones in deep coal mining remains a critical challenge for disaster prevention. CGO formation is governed by the coupled effects of stress redistribution, coal–gas occurrence conditions, and mining‐induced disturbances. To address this problem, a quantitative stress–energy analysis model is developed for the region ahead of the working face, in which these governing factors are explicitly quantified as model input parameters. The coupled stress–energy evolution of the coal–rock system is systematically investigated, thereby elucidating the mechanical mechanisms underlying CGO risk formation. The results show that the peak stress ahead of the working face is positively correlated with elastic‐zone stress anomalies, burial depth, coal compressive strength, and gas pressure, while exhibiting no clear correlation with coal seam thickness. In contrast, the plastic zone length is positively correlated with elastic‐zone stress anomalies, coal seam thickness, burial depth, and gas pressure, and negatively correlated with coal compressive strength. Multi‐factor coupling significantly modifies peak stress and plastic zone development, resulting in nonlinear energy accumulation and dissipation that directly control CGO risk. Based on these findings, a dynamic and adaptive CGO risk prediction method is developed by introducing an energy‐based indicator R within a unified mechanical–energy framework that integrates stress evolution, coal‐gas occurrence conditions, coal seam geometry, and mining‐induced disturbances. Field validation demonstrates that the proposed method exhibits reliable predictive performance, stable behavior, and meaningful forecasting lead time under complex geological conditions, providing a practical approach for CGO risk prediction in deep coal mining.