Zonal prediction of the heights of water-conducting fracture zones under varying overburden types in North China-type coalfields
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摘要:背景
我国华北型煤田煤炭资源开采深度及开采强度逐渐增加，进而导致覆岩移动破坏及裂隙演化等热点问题，引起极大关注。
方法收集华北型煤田导水裂隙带高度实测数据共117组，其中不同覆岩类型(坚硬、中硬和软弱)数据分别为17、42和58组。在此基础上探究不同覆岩类型控制下不同采高、不同采深以及不同工作面斜长对导水裂隙带发育高度的影响。根据华北型煤田含煤地层的沉积特点，将其划分为北带、中带和南带3个区域，分别采用卷积神经网络、贝叶斯公式及《“三下”规范》3种方法，分区域开展北带、中带和南带地区的导水裂隙带高度精细化研究。
结果和结论结果表明：随着采高、采深或工作面斜长等因素的改变，不同覆岩类型条件下的导水裂隙带高度分布出现明显差异，从坚硬、中硬到软弱覆岩，裂采比依次降低，其中坚硬覆岩的裂采比是中硬覆岩的1.59倍，是软弱覆岩的1.77倍；中硬覆岩的裂采比是软弱覆岩的1.11倍。预测结果显示，3个地区卷积神经网络和贝叶斯公式的RMSE(ERMS)值分别为6.62和21.84、2.20和8.09、2.60和6.12，明显小于《“三下”规范》中的经验公式的RMSE(ERMS)值(45.91、13.40和21.99)，说明卷积神经网络和贝叶斯公式的预测结果均优于经验公式，其中卷积神经网络预测结果更为贴近实测结果，具有良好的适用性，可为华北型煤田不同覆岩类型下的导水裂隙带高度预测提供依据。
Abstract:BackgroundThe gradual increase in the exploitation depth and intensity of coal resources in the North China-type coalfields has caused problems such as overburden movement and damage, as well as fracture evolution. These hot topics have attracted considerable attention.
MethodsA total of 117 sets of measured data on the heights of water-conducting fracture zones in the North China-type coalfields were collected, consisting of 17 sets from hard overburden, 42 sets from moderately hard overburden, and 58 sets from soft overburden. Using these data, this study explored the effects of varying mining heights, mining depths, and lengths of the mining face along its dip direction on the heights of water-conducting fracture zones under varying overburden types. Given the sedimentary characteristics of their coal-bearing strata, the North China-type coalfields were divided into three regions: the northern, middle, and southern belts. This study thoroughly investigated the heights of water-conducting fracture zones in the three regions using the convolutional neural network, the Bayes' formula, and empirical formulas for coal mining under buildings, water bodies, and railways.
Results and ConclusionsThe results indicate that as the mining height, mining depth, or the length of the mining face along its dip direction varied, the height distributions of water-conducting fracture zones differed significantly under different overburden types. From hard, to medium-hard, and then to soft overburden types, the ratio of the height of water-conducting fracture zones to the mining height (also referred to as the fracture-to-mining ratio) decreased sequentially. Specifically, the fracture-to-mining ratio of hard overburden was 1.59 times that of moderately hard overburden and 1.77 times that of soft overburden, while the fracture-to-mining ratio of moderately hard overburden was 1.11 times that of soft overburden. The prediction results indicate that the prediction results of the northern, middle, and southern belts calculated using the convolutional neural network yielded root mean square errors (RMSEs) of 6.62, 2.20, and 2.60, respectively, while those calculated using Bayes’ formula yielded RMSEs of 21.84, 8.09, and 6.12, respectively. These values were much less than those derived using the empirical formulas for coal mining under buildings, water bodies, and railways (45.91, 13.40, and 21.99, respectively). This suggests that the convolutional neural network and Bayes’ formula outperform the empirical formulas. Notably, the prediction results obtained using the convolutional neural network are closer to the measured results, suggesting the high suitability of the convolutional neural network. This study can provide a basis for predicting the heights of water-conducting fracture zones under different overburden types in the North China-type coalfields.
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图 2 华北地区典型矿区代表性地层综合柱状图
Fig. 2 Composite stratigraphic column of representative strata in typical mining areas in North China
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图 3 不同覆岩类型条件下裂采比范围分布
Fig. 3 Distribution of the fracture-to-mining ratios under different overburden types
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图 6 导水裂隙带高度与工作面斜长关系
Fig. 6 Height of water-conducting fracture zone vs. the length of the mining face along its dip direction
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图 8 卷积神经网络与其他模型预测结果对比
Fig. 8 Comparison of the prediction results of the northern belt between the convolutional neural network and other models
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图 9 卷积神经网络与其他模型预测结果对比
Fig. 9 Comparison of the prediction results of the middle belt between the convolutional neural network and other models
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图 10 卷积神经网络与其他模型预测结果对比
Fig. 10 Comparison of the prediction results of the southern belt between the convolutional neural network and other models
表 1 各煤田导水裂隙带数据汇总
Table 1 Summary of data on water-conducting fracture zones in various coalfields
煤田 矿井 覆岩
类型采高/m 采深/m 工作
面斜长/m导水裂隙带
实测高度/m煤田 矿井 覆岩
类型采高/m 采深/m 工作面
斜长/m导水裂隙带
实测高度/m神府煤田 大柳塔 软弱 4 49 135 45 东胜煤田 乌兰木伦 中硬 2.2 101 158 63 榆阳矿 中硬 3.5 126 212 96.3 乌兰木伦 3.2 56 158 63 榆阳矿 3.5 105 200 85 补连塔 4.8 260 208 92 杭来湾 4.5 122 215 93.87 上湾矿 8.8 102 300 94 柠条塔 4.35 116 295 98.68 塔然高勒 4.5 76 279.5 58 榆神矿 6 86 140 60 补连塔矿 坚硬 6 146 311 125 金鸡滩 坚硬 5.5 134 220 108.59 上湾矿 8.8 150 300 120.4 榆树湾 5 156 240 130.5 晋城煤田 赵固一矿 软弱 3.5 180 141 41.6 榆树湾 5 153 242 137.3 常村矿 3.5 123 100 31.26 榆树湾 5 161 246 138.9 赵屋矿 4.19 385 130 32.05 榆树湾 5 145 238 117.8 七一矿 1.7 247 126 30.74 杭来湾 4.5 129 226 108.32 晋城某矿 3.6 156 110 25.7 杭来湾 4.5 142 236 114.38 盖州矿 1.92 220 100 36 杭来湾 4.5 126 220 107.83 古汉山 中硬 3 240 152 71 曹家滩 6 330 350 167 宁汶煤田 鲁西矿 软弱 2.5 350 135 20 曹家滩 6 260 280 207 鲁西矿 3.42 420 160 41.51 柠条塔 4.8 150.91 110 158 大封矿 1.45 106 215 17.6 柠条塔 5.8 171 286 148 太平矿 1.8 330 129 23.85 柠条塔 5.46 186.1 269 144 太平矿 2.2 320 122 25.6 柠条塔 5.46 188.91 273 140 东滩矿 4 400 30 35 淮南煤田 丁集矿 软弱 3.7 315 245 40.8 阳城矿 中硬 7.5 665 222 53.7 丁集矿 2.6 420 253.4 50 济宁煤田 新河矿 软弱 2.5 426 110 44.75 新集矿 3.8 356 193 53.4 新河矿 中硬 5 450 160 55.3 潘三矿 3 310 250 35 济三矿 6.1 475 170 64.6 潘三矿 3.9 350 240 50 济二矿 2.94 568.4 180.4 57 昌恒矿 中硬 9.2 450 200 80.4 济二矿 2.95 516 206.1 54.5 兖州煤田 鲍店矿 软弱 6.9 482 135 64.5 淮北煤田 童亭矿 软弱 2 230 85 52.5 鲍店矿 5.6 563 188 50.18 杨庄矿 1.7 320 65 27.5 兴隆庄 2.8 264.5 156 44.34 五沟矿 4.48 360.7 180 38.65 兴隆庄 2.6 265 147 43.34 五沟矿 5.17 335 135 44.3 兴隆庄 2.8 264.5 148.5 40.35 五沟矿 3.94 330.91 180 42.8 兴隆庄 2.6 290 168 46.22 五沟矿 4.07 321 175 35.6 兴隆庄 2.6 290 168 38.41 五沟矿 4.53 310.21 180 47.5 兴隆庄 2.6 290 168 39.14 五沟矿 3.2 335 182.8 41 兴隆庄 2.5 265 192 40.21 五沟矿 3.1 226.2 137.5 44.3 兴隆庄 2.7 265 192 42.81 谢桥矿 2.6 280 112 49 兴隆庄 2.6 295 185 40.5 祁东矿 3 410.5 125.2 37.3 兴隆庄 5.3 312 145.7 44.2 祁东矿 1.7 422.3 182.7 39 鲍店矿 中硬 7.53 357 170 61.9 祁东矿 1.9 441.7 173.8 45 鲍店矿 7.52 367 190 61.77 祁东矿 2.7 441.8 56.3 54.9 南屯矿 5 320 122 67.7 祁东矿 2.9 384.9 168.7 48.6 南屯矿 4.8 485 175 62.5 桃园矿 3 336.1 158.6 53.4 兴隆庄 2.8 269 156 50.34 祁南矿 2.8 310.5 173.8 32 兴隆庄 7 433 168 70.3 祁南矿 1.3 357.3 195.8 27 兴隆庄 7.4 331 160 64.25 孙瞳矿 2.2 341.5 192.4 34 兴隆庄 5.7 283.9 177.9 51.4 袁二矿 4.1 300.8 201.8 45.1 鲍店矿 7.5 367 173.5 75.5 袁二矿 3.5 401.2 144.9 43 西山煤田 官庄河 软弱 2.9 402 140 39.7 祁东矿 中硬 2.4 667 153.4 65 曙光矿 3 408 120 41.3 祁东矿 2.7 457.2 181 72 东曲矿 中硬 4.83 428 226 55.5 祁东矿 2.3 493.1 184 59 官庄河 3.1 487 220 53.7 祁东矿 2.6 428.9 127.3 67 恒昇矿 5 523 200 60.6 青东矿 6.5 360 200 92.5 石嘴山煤田 红柳矿 中硬 5.28 95 306 81.66 板集矿 4 255 262 73 麦垛山 3.6 74 250 75.25 滕县煤田 留庄矿 软弱 1.23 320 90 31.98 红柳矿 5 82 302 62.53 高庄矿 中硬 4.6 400 170 55.9 宁夏某矿 5 73 306 62.53 高庄矿 4.6 86.1 170 53.9 准格尔煤田 门克庆矿 软弱 4.35 64 106 46.8 平顶山煤田 云盖山二矿 软弱 4 322 133 50 红庆河 中硬 6 85 246 62.7 平煤股份八矿 2 150 174 58.4 母杜柴登 坚硬 4.82 146 280 122.57 
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表 2 华北型煤田划分
Table 2 Division of North China-type coalfields
分区 煤田 沉积环境 北带 神府煤田、石嘴山煤田、东胜煤田、准格尔煤田 滨海冲积平原和山前冲积平原 中带 宁汶煤田、兖州煤田、济宁煤田、滕县煤田、平顶山煤田、晋城煤田、西山煤田 滨海平原和滨海冲积平原等过渡地带 南带 淮南煤田、淮北煤田 滨海环境
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覆岩类型 单轴极限抗压强度σc /MPa 软弱 <20 中硬 20~70 坚硬 >70
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表 4 不同覆岩类型下导水裂隙带高度和裂采比
Table 4 Heights and fracture-to-mining ratios of water-conducting fracture zones under varying overburden types
覆岩类型 采高/m 导水裂隙带高度/m 裂采比 坚硬 $\dfrac{4.50 \sim 8.80}{5.42} $ $\dfrac{107.83 \sim 207.00}{135.03} $ $\dfrac{13.68 \sim 34.50}{25.34} $ 中硬 $\dfrac{2.20 \sim 9.20}{4.88} $ $\dfrac{50.34 \sim 98.68}{60.07} $ $\dfrac{7.16 \sim 28.64}{15.92} $ 软弱 $\dfrac{1.23 \sim 6.90}{3.11} $ $\dfrac{17.60 \sim 64.50}{40.77} $ $\dfrac{7.14 \sim 29.20}{14.34} $ 注：4.50~8.80表示最小~最大值；横线下侧数据5.42表示平均值，其他同。
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表 5 覆岩类型数值量化
Table 5 Quantization of overburden types
变量类型 变量名称 变量取值 覆岩类型 坚硬 0.75 中硬 0.50 软弱 0.25
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表 6 《“三下”规范》经验公式
Table 6 Empirical formulas for coal mining under buildings, water bodies, and railways
岩性 公式一 公式二 坚硬 $ {H_{{\text{li}}}} = \dfrac{{100\displaystyle\sum M }}{{1.2\displaystyle\sum M + 2.0}} \pm 8.9 $ $ {H_{{\text{li}}}} = 30\sqrt {\displaystyle\sum M } + 10 $ 中硬 $ {H_{{\text{li}}}} = \dfrac{{100\displaystyle\sum M }}{{1.6\displaystyle\sum M + 3.6}} \pm 5.6 $ $ {H_{{\text{li}}}} = 20\sqrt {\displaystyle\sum M } + 10 $ 软弱 $ {H_{{\text{li}}}} = \dfrac{{100\displaystyle\sum M }}{{3.1\displaystyle\sum M + 5.0}} \pm 4.0 $ $ {H_{{\text{li}}}} = 10\sqrt {\displaystyle\sum M } + 5 $
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表 7 实测值与其他模型预测值对比
Table 7 Comparison of measured values with predicted values from different models
单位：m 样本序号 实测值 卷积神经网络 贝叶斯模型 《“三下”规范》经验公式 预测值 绝对误差 预测值 绝对误差 预测值 绝对误差 1 122.57 122.38 −0.19 139.88 17.31 70.82 −51.75 2 81.66 83.35 1.69 91.57 9.91 49.42 −32.24 3 46.80 43.04 −3.76 32.97 −13.83 27.53 −19.27 4 137.30 124.73 −12.57 139.19 1.89 71.40 −65.90 最小误差 0.19 1.89 19.27 最大误差 12.57 17.31 65.90 RMSE (ERMS) 6.62 21.84 45.91 MAE (EMA) 4.55 17.57 42.29 R2 0.97 0.96
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表 8 实测值与其他模型预测值对比
Table 8 Comparison of measured values with predicted values from different models
单位：m 样本序号 实测值 卷积神经网络 贝叶斯模型 《“三下”规范》经验公式 预测值 绝对误差 预测值 绝对误差 预测值 绝对误差 1 62.50 60.07 −2.43 59.74 −2.76 48.15 −14.35 2 50.34 53.90 3.56 53.74 3.40 40.25 −10.09 3 38.41 37.80 −0.61 38.89 0.48 23.91 −14.50 4 30.74 31.65 0.91 35.52 4.78 20.55 −10.19 5 40.21 38.12 −2.09 39.09 −1.13 23.61 −16.60 最小误差 0.61 0.48 10.09 最大误差 3.56 4.78 16.60 RMSE (ERMS) 2.20 8.09 13.40 MAE (EMA) 1.92 5.97 13.15 R2 0.93 0.97
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表 9 实测值与其他模型预测值对比
Table 9 Comparison of measured values with predicted values from different models
单位：m 样本序号 实测值 卷积神经网络 贝叶斯模型 《“三下”规范》经验公式 预测值 绝对误差 预测值 绝对误差 预测值 绝对误差 1 40.80 42.08 1.28 43.49 2.69 26.47 −14.33 2 41.00 42.17 1.17 43.25 2.25 25.45 −15.55 3 73.00 76.60 3.60 70.95 −2.05 45.60 −27.40 4 65.00 68.34 3.34 68.40 3.40 37.86 −27.14 最小误差 1.17 2.05 14.33 最大误差 3.60 3.40 27.40 RMSE (ERMS) 2.60 7.22 21.99 MAE(EMA) 2.35 6.12 21.11 R2 0.97 0.97
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