Three-dimensional coal seam modeling of fully mechanized working face based on transparent geology
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摘要: 基于透明地质的三维煤层建模方法是间接解决煤岩识别难题的有效途径。现有三维煤层建模方法的研究大多集中于对空间三维实体的表达，对开采过程中煤层顶底板动态变化的过程缺乏研究，对于复杂地质条件的煤层顶底板高程的预测精度不高，难以满足采煤实际需求。针对上述问题，提出了一种基于透明地质的综采工作面三维煤层建模方法。基于进回风巷地质数据、钻孔测量数据、工作面切眼数据及利用三维地震再解释技术、槽波地震勘探技术与无线电磁波透视技术获得的煤层地质数据，应用离散平滑插值（DSI）算法预测煤层顶底板高程，构建综采工作面静态三维煤层模型。为提高工作面静态三维煤层模型精度，通过切眼开采新揭露的地质信息和DSI算法对其进行动态更新，获得更为精确的工作面动态三维煤层模型，基于更新后的三维煤层模型动态规划采煤机截割曲线，指导采煤机进行自动调高控制，从而实现自适应割煤。将该方法应用于黄陵一号煤矿810综采工作面，结果表明：DSI算法对煤层顶底板高程预测效果优于克里金插值算法和样条函数插值算法，插值平均绝对误差为0.015 5 m；每截割5 m对三维煤层模型更新1次，煤层顶底板高程预测误差≤6.3 cm，满足采煤机截割轨迹精确规划要求。Abstract: The three-dimensional coal seam modeling method based on transparent geology is an effective way to indirectly solve the problem of coal rock identification. Most of the existing three-dimensional coal seam modeling methods focus on the expression of spatial three-dimensional entities. There is a lack of research on the dynamic change of coal seam roof and floor in the mining process. And the prediction precision of coal seam roof and floor elevation under complex geological conditions is not high, which is difficult to meet the actual needs of coal mining. In order to solve the above problems, this paper proposes a three-dimensional coal seam modeling method of fully mechanized working face based on transparent geology. Based on the geological data of air inlet and return roadway, borehole measurement data, open-off cut data of working face and the coal seam geological data obtained by using three-dimensional seismic re-interpretation technology, in-seam seismic exploration technology and wireless electromagnetic wave perspective technology, the discrete smooth interpolation (DSI) algorithm is applied to predict the elevation of coal seam roof and floor. And the static three-dimensional coal seam model of fully mechanized working face is constructed. In order to improve the precision of the static three-dimensional coal seam model of the working face, the geological information newly revealed by open-off cut and DSI algorithm are used to dynamically update the model to obtain a more accurate dynamic three-dimensional coal seam model of the working face. Based on the updated three-dimensional coal seam model, the cutting curve of the shearer is dynamically planned to guide the shearer to automatically adjust height so as to achieve adaptive coal cutting. The method is applied to 810 fully mechanized working face of Huangling No.1 Coal Mine, the results show that the DSI algorithm is better than Kriging interpolation algorithm and spline function interpolation algorithm in the prediction of coal seam roof and floor elevation. The mean absolute error of interpolation is 0.015 5 m. The three-dimensional coal seam model is updated once every 5 m of cutting, and the elevation prediction error of coal seam roof and floor is ≤ 6.3 cm, which meets the requirements for precise planning of the cutting track of the shearer.
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图 1 基于透明地质的综采工作面三维煤层建模
Figure 1. Three-dimensional coal seam modeling of fully mechanized working face based on transparent geology
图 2 3种插值算法插值计算结果对比
Figure 2. Comparison of interpolation calculation results of three interpolation algorithms
图 3 黄陵一号煤矿810综采工作面静态三维煤层模型
Figure 3. Static three-dimensional coal seam model of 810 fully mechanized working face of Huangling No.1 Coal Mine
图 4 黄陵一号煤矿810综采工作面剖面平面图
Figure 4. Section plan of 810 fully mechanized working face of Huangling No.1 Coal Mine
图 5 黄陵一号煤矿810综采工作面6个剖面的顶底板曲线
Figure 5. Roof and floor curves of 6 sections in 810 fully mechanized working face of Huangling No.1 Coal Mine
图 6 黄陵一号煤矿 810综采工作面动态更新后的三维煤层模型
Figure 6. Dynamic updated three-dimensional coal seam model of 810 fully mechanized working face in Huangling No.1 Coal Mine
图 7 距离更新点不同距离时煤层顶板高程预测误差
Figure 7. The prediction error of coal seam roof elevation at different distances from the update point
表 1 3种插值算法MAE的对比
Table 1. Comparison of mean absolute error （MAE） of three interpolation algorithms
m 克里金插值算法 DSI算法 样条函数插值算法 0.022 5 0.015 5 0.231 2 
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表 2 顶板高程预测误差
Table 2. Roof elevation prediction error
m 采样点 MAE 采样点 MAE 采样点 MAE 1 0.053840 16 0.910000 31 0.501333 2 0.091417 17 0.617000 32 0.562667 3 0.337333 18 0.114767 33 0.497667 4 0.468333 19 0.178380 34 0.431667 5 0.885000 20 0.420667 35 0.250667 6 1.010000 21 0.684667 36 0.108667 7 1.030000 22 0.742000 37 0.103667 8 1.050667 23 0.879333 38 0.155333 9 0.416667 24 1.036000 39 0.003667 10 0.051093 25 0.980000 40 0.004000 11 0.271667 26 0.890333 41 0.153333 12 0.507333 27 0.357183 42 0.286000 13 0.473333 28 0.140887 43 0.362667 14 0.619333 29 0.019613 44 0.552333 15 0.920000 30 0.287333 45 0.229667
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