Water inrush warning system of deep limestone in Panxie Mining Area based on multi-source data mining
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摘要: 深层灰岩水在长时间水岩耦合作用下各含水层的水化学成分有所不同，但随着地壳运动、采动影响等因素导致不同含水层产生水力联系。重大的突水事故都是深层高压灰岩水以浅层灰岩水为通道突入矿井发生的。依据对淮南煤田潘谢矿区9对矿井2015—2018年182个地面水文观测孔的水位数据及潘二矿突水后各水文观测孔水位变化的时空规律，得出水文观测孔的水位变化数据比水位高程数据更灵敏，潘谢矿区深层灰岩水由下向上对浅层灰岩水进行补给，通过聚类分析算法识别出矿井与深层灰岩水存在补给关系的浅层灰岩含水层区域；另一方面基于改进的随机森林算法对收集的7 000多条矿井水质化验资料进行分析，基于错分数据识别出与深层灰岩水水力联系紧密的各矿含水层信息。综合分析水位变化数据聚类分析结果，得出各矿井的突水风险区域。基于含水层分类显著因子、水化学空间分布特征，结合温度、流量、水位、水质等参数的高精度传感器，构建快速准确突水预警系统，对矿井出水点进行智能监测，为实施防治水措施提供快速、可靠的依据，可以极大地避免矿井发生突水事故和减少突水事故产生的损失。Abstract: Under the long-term water-rock interaction, the hydrochemical composition of deep limestone water in each aquifer is different. Such factors as crustal movement and mining influence have led to the hydraulic connection between aquifers. Major water inrush accidents often occur when the deep high-pressure limestone water bursts into the mine with the shallow limestone water as a channel. On the basis of water level data of 182 surface hydrological observation holes in nine coal mines in Panxie Mining Area of Huainan Coalfield from 2015 to 2018, and the temporal and spatial law of water level change of each hydrological observation hole after the water inrush occurred in Pan’er Mine, we found that the water level change data of hydrological observation holes are more sensitive than the water level elevation data, and the deep limestone water in Panxie Mining Area recharged from bottom to top to the shallow limestone water. The shallow limestone water area with a recharge relationship between aquifer and deep limestone water is identified by clustering analysis algorithm. On the other hand, the water quality test data of more than 7 000 mines are analyzed based on the improved random forest algorithm, and the information on aquifers closely related to the water and hydraulic of deep limestone is identified based on the misclassification data. With the results of clustering analysis of water level change data being analyzed comprehensively, the water inrush risk area of each mine is obtained. Based on the significant classification factors and the hydrochemical characteristics of spatial distribution of each aquifer, we constructed a fast and accurate water inrush warning system by using high precision sensors of temperature, pressure, water level and water quality and other high-precision sensors, as so to monitor the water inrush point of a mine on-line. It provides a fast and reliable basis for water prevention and control measures in construction, and can greatly avoid water inrush accidents in mines, reducing the losses arising from sudden water accidents.
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图 1 潘谢矿区地面水文观测孔布置及突水后水位变化
Fig. 1 Water level changes of hydrological observation holes in Ordovician and Cambrian after water inrush
图 2 灰岩水文观测孔突水前后水位变化与距突水点距离散点图
Fig. 2 Scatter plots of water level changes and distances from water inrush point before and after water inrush in the limestone hydrological observation holes
图 5 灰岩水突水危险识别与预警系统
Fig. 5 Risk identification and early warning system for limestone water inrush
表 1 突水前后地面水文观测孔水位变化统计
Table 1 Statistical table of water level changes in surface hydrological observation holes before and after water inrush
煤矿 新生界松散层含水层 太原组灰岩含水层 奥陶系、寒武系灰岩含水层 水位变化/m 水位高程/m 水位变化/m 水位高程/m 水位变化/m 水位高程/m 潘二矿 –0.1~1.7/0.4 7.9 –20~84/49 –253 313.0~377.0/346.0 –40 潘一矿 –0.4~1.3/0.3 6.2 10~146/58 –193 353.0 –33 潘北矿 0.4~0.4/0.4 11.0 22~83/58 –71 77.0~185.0/101.0 –25 潘三矿 0~1.0/0.4 –4.3 0~72/5 –145 –5.8~77.0/55.0 –29 丁集矿 –0.2~1.4/0.7 11.0 0~1 27 26.0 –13 顾桥矿 –0.1~2.4/0.7 3.7 4~5 –8 4.0~5.1/4.6 –8 顾北矿 0.2~2.7/1.2 14.0 0~6/2 2 1.9~2.6/2.2 2 张集矿 0.1~3.4/1.4 11.0 –5~6/1 –102 0.1~4.7/2.4 –7 谢桥矿 0~3.2/1.2 11.0 –16~2/–2 –82 0.8~2.3/1.7 2 注：–0.1~1.7/0.4表示最小~最大值/平均值，其他同。 
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表 2 水样各参数对于分类的贡献值
Table 2 Contribution rate of each parameter of water samples to classification
项目 贡献值 Cl− 矿井类别 Na++K+ Ca2+ 采样年限 SO4 2− HCO3 − Mg2+ CO3 2− pH 奥灰水 0.20 0.27 0.18 0.20 0.17 0.26 0.14 0.12 0.07 0.07 砂岩水 0.09 0.08 0.05 0.07 0.04 0.04 0.05 0.06 0.01 0.01 松散层水 0.16 0.10 0.23 0.11 0.10 0.10 0.16 0.08 0.05 0.04 太灰水 0.24 0.19 0.19 0.11 0.15 0.11 0.11 0.07 0.04 0.05 MDA 0.15 0.13 0.12 0.10 0.09 0.09 0.09 0.07 0.03 0.03 MDG 115 105 101 74 64 71 76 58 32 30
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表 3 随机森林分类结果混淆矩阵
Table 3 Confusion matrix of random forest classification results
项目 奥灰水 砂岩水 太灰水 松散层水 奥灰水 143 5 0 10 砂岩水 2 594 9 21 太灰水 0 13 146 3 松散层水 18 3 2 264
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