Water-inrush Assessment Using a GIS-based Bayesian Network for the 12-2 Coal Seam of the Kailuan Donghuantuo Coal Mine in China
Water-inrush Assessment Using a GIS-based Bayesian Network for the 12-2 Coal Seam of the Kailuan Donghuantuo Coal Mine in China
复制标题
基于 GIS 的贝叶斯网络对中国开滦东环沱煤矿 12-2 煤层突水评估
DOI：
10.1007/s10230-012-0178-4
复制
发表时间：
2012-06-01
影响因子：
2.8
通讯作者：
Xi Sha
中科院分区：
文献类型：
--
作者：
Dong Donglin;Sun Wenjie;Xi Sha
东欢坨煤矿地质异常，发育正断层60条，逆断层18条，向斜1条。煤层底板高度破裂，裂缝充当地下水的管道，地下水从Ordina石灰岩含水层流入12号煤层。2005年至2010年，该煤层底板发生过7次突水灾害。最大的突水事件超过63 mA（3）/min;有五个突水点持续超过1.0 mA（3）/min。需要对底板突水概率进行全面建模，以降低此类事件的可能性和严重性。突水情况进行了评估，使用基于GIS的贝叶斯网络（BN）。在开发的BN-GIS模型中，煤矿工作面的几何形状被纳入适当的细节和分辨率。模拟结果与现场突水观测结果进行了比较。根据记录的突水事件，模型数据的拟合精度为83.4%，做出错误预测的概率小于0.5，这意味着使用该方法可以显着提高矿井的煤炭产量。
The Donghuantuo coal mine is geologically unusual, with 60 normal faults, 18 reverse faults, and 1 syncline. The coal seam floor is highly fractured and the fractures act as conduits for groundwater, which flows from the Ordina limestone aquifer into the no. 12 coal seam. From 2005 to 2010, there were 7 water-inrush disasters through the floor of this coal seam. The largest water-inrush event exceeded 63 mA(3)/min; there are five points where the water-inrush continues to exceed 1.0 mA(3)/min. Comprehensive modeling of the probability of water-inrush through the floor is required to reduce the likelihood and severity of such events. The water-inrush situation was assessed using a GIS-based Bayesian network (BN). In the developed BN-GIS model, the geometry of the coal mine working face was incorporated in suitable detail and resolution. The results of the modeling compared well with field water-inrush observations. Based on documented water-inrush events, the accuracy of the fit of the model data is 83.4 %, and the probability of making an incorrect prediction is less than 0.5, which means that using this method could significantly enhance coal production at the mine.