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提出基于多源信息融合的瓦斯涌出量动态预测是一种传统矿井涌出量预测与现代计算机编程相结合的新的矿井瓦斯涌出量预测方法。这种方法通过矿井实测煤层瓦斯含量、地勘瓦斯含量、K1-p或△h2-p关系曲线、煤巷掘进瓦斯涌出反演煤体瓦斯含量等多源信息融合,得出煤层瓦斯赋存规律和较为准确的瓦斯含量分布图,结合瓦斯含量分布和分源预测法构建同等开采工艺条件下煤层瓦斯含量与瓦斯涌出量数学模型,利用新工作面瓦斯涌出数据和矿山统计法不断跟踪及修正瓦斯涌出量数学模型,形成融合后数学模型,实现对已采区域的瓦斯涌出量目标跟踪和未采区域的瓦斯涌出量动态预测。
It is proposed that the dynamic prediction of gas emission based on multi-source information fusion is a new prediction method of gas emission in coal mines combined with prediction of the amount of conventional mine emission and modern computer programming. In this method, multi-source information such as measured gas content in coal seam, gas content in ground surveys, K1-p or △ h2-p, and gas outburst in coal tunnels is used to obtain the gas occurrence in coal seams Law and more accurate gas content distribution map, combined with gas content distribution and sub-source prediction method to build the coal seam gas content and gas emission mathematical model under the conditions of the same mining technology, the use of new face gas emission data and mining statistical methods continue to track And to amend the mathematical model of gas emission to form a post-fusion mathematical model to realize the target tracking of gas emission in the mining area and the dynamic prediction of gas emission in the non-mining area.