Application of 3D sonar seepage detection technology in deep mining engineering
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摘要: 水文地质参数的准确获取是制约深部开采矿山水害防控的关键因素之一。以广西盘龙铅锌矿区为研究对象，采用三维声纳渗流探测技术，获取了矿区东西两侧测孔内的地下水渗流速度、渗流方向、渗流量、渗透系数等水文地质参数，分析了水文地质参数的孔内空间分布特征，揭示了矿区地下水的主要来源。研究结果表明：声纳渗流探测技术能够准确探明深埋岩溶含水层沿深度方向上的渗流速度、渗流方向、渗透系数、渗透流量等参数的变化特征，并准确预测矿区东侧水22测孔在−25 m—−78 m及−85 m高程以下，矿区西侧SK4测孔在−90 m高程以下存在地下水径流通道；矿区已探明的总渗透流量为
6494.31 m3/d，仅占矿区日常排水量的1/3，其中矿区东侧和西侧的地下水渗透流量分别占总渗透流量的58%和42%；声纳渗流探测技术受限于测孔位置的布设，采用关键测孔（揭示主要径流通道的钻孔）开展探测工作有助于提高矿区涌水量预测的精准度。Abstract:In regions characterized by extensive karst landform development, the extraction of deep mineral resources faces significant challenges in preventing and controlling water hazards. Hydrogeological parameters, which are essential for understanding groundwater movement patterns and assessing water hazard risks, critically influence the effectiveness of prevention measures in mining areas. Inaccurate acquisition of these parameters can easily lead to disasters such as water inrushes and sudden water surges during mining operations. These events not only jeopardize the safety of underground personnel but also pose risks to equipment, disrupt mining progress, result in substantial economic losses, and cause ecological damage. Currently, traditional methods for obtaining hydrogeological parameters on-site primarily include pumping tests, water injection tests, and water pressure tests. These methods, which rely on direct interaction with groundwater systems, can accurately determine key parameters of aquifers and have long been regarded as essential in the field of hydrogeological research. However, they are often associated with tedious on-site testing and high costs in practical applications. Furthermore, the process—from experimental design and on-site implementation to stable data collection and final analysis—can take several months or even years. This lengthy timeline significantly lags behind the demands of mining area development and construction, making it challenging to address the urgent need for dynamic water hazard prevention and control. Geophysical methods have become a widely utilized approach for obtaining hydrogeological parameters in the industry due to their distinct advantages. Unlike traditional experimental methods, geophysical techniques do not require large-scale destruction of geological formations, significantly reduce operational costs, and facilitate the preliminary exploration of extensive areas within a short timeframe, thereby enhancing efficiency. However, as detection depth increases, deep strata are influenced by various factors, including complex geological structures, the degree of rock weathering, and the chemical composition of groundwater. Consequently, the signal experiences significant attenuation and distortion during propagation, leading to a marked decline in detection accuracy. This challenge is particularly pronounced for deep karst aquifers, where intricate cave and fissure systems complicate the interpretation of geophysical signals, making it difficult to accurately represent the true hydrogeological parameters. This complexity poses potential risks for water hazard prevention and control in deep mining areas. In response to various technological challenges, 3D sonar seepage detection technology has emerged as an innovative method for obtaining hydrogeological parameters of deep karst aquifers. 3D sonar seepage detection technology effectively mitigates the issue of reduced detection accuracy with depth. Whether dealing with shallow weathered fissure aquifers or deep karst conduit systems extending hundreds or even thousands of meters, high-precision parameter determination is achievable. Additionally, its high-resolution imaging capability can visually represent the three-dimensional motion state of water flow within the borehole, providing a powerful tool for a deeper understanding of groundwater seepage dynamics. Based on this background, this study focuses on the Panlong Lead-Zinc Mine's deep mining area in Guangxi. Situated in the western region of Guangxi, which is characterized by intense karst development, the mine features a vigorous subsurface karst system that poses significant water hazard threats during deep mining operations. This study employs 3D sonar seepage detection technology, strategically deploying monitoring boreholes on both the eastern and western sides of the mining area to achieve a comprehensive and detailed characterization of groundwater seepage within the boreholes. Through prolonged and high-frequency data acquisition, a substantial volume of accurate seepage parameters—including seepage velocity, direction, flow rate, and permeability coefficient—was obtained. Building upon this data foundation, the spatial distribution patterns of these parameters were analyzed in depth to investigate the differences in groundwater seepage across various depths and regions. This analysis aims to reveal the water-conducting characteristics and karst development features of the aquifers on the eastern and western flanks of the mining area, thereby providing robust data support and a theoretical basis for the scientific formulation of water hazard prevention and control strategies for deep mining operations in the region. The research results indicate that sonar seepage detection technology can accurately evaluate the variation characteristics of parameters such as seepage velocity, seepage direction, permeability coefficient, and seepage flow rate with depth in deeply buried karst aquifers. It can also accurately predict the presence of groundwater runoff channels in the eastern part of the mining area at elevations between -25m and -78m, as well as below -85m at the water 22. Additionally, it can identify groundwater runoff channels in the western part of the mining area at elevations below -90m at the SK4. The total proven seepage flow in the mining area is 6,494.31m3/d, which represents only one-third of the daily drainage in the region. Groundwater seepage flow on the eastern and western sides of the mining area accounts for 58% and 42% of the total seepage flow, respectively. The sonar seepage detection technology is limited by the arrangement of measurement hole positions. Utilizing key measurement holes (holes revealing the main runoff channels) for detection significantly enhances the accuracy of predicting water inflow in mining areas. -
Key words:
- sonar seepage detection /
- deep mining /
- Panlong lead-zinc mine area /
- karst /
- permeability coefficient
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图 5 各测孔渗流速度沿高程分布曲线
Figure 5. Distribution curve of seepage velocity along the elevation
图 6 测孔剖面渗流速度矢量方向分布图
Figure 6. The direction distribution of seepage velocity vector in the measuring hole section
图 8 地下水渗流方向及径流通道路径示意图
Figure 8. Groundwater seepage direction and runoff channel path diagram
图 9 测孔渗透系数沿高程分布曲线
Figure 9. Distribution curve of permeability coefficient of measuring hole along the elevation
表 1 测孔水位
Table 1. Water level of measuring hole
编号 井口高
程/m测量时水位
埋深/m水位高
程/m黔江水位 / / 38.04 J2 62.33 0.65 61.68 SK5 60.72 5.7 55.02 SK3 69.67 46.5 23.17 KJ2 58.5 20 38.5 SK4 59.65 12 47.65 水62 73.96 44.5 29.46 DK2 65.1 31.7 33.4 水22 67.12 31.5 35.62 矿坑（降落漏斗中心点） 61.09 154.22 −93.13 
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表 2 渗流速度探测值（部分）
Table 2. Detection value of seepage velocity (part)
高程/m J2 SK5 SK3 KJ2 SK4 水62 DK2 水22 62 8.82E-05 / / / / / / / 52 9.00E-05 9.64E-05 / / / / / / 42 9.13E-05 8.55E-05 8.77E-05 / 9.68E-05 8.54E-05 / / 32 1.01E-04 9.18E-05 1.02E-04 / 9.22E-05 1.58E-04 8.33E-05 8.50E-05 22 1.13E-04 9.24E-05 / 7.93E-05 8.98E-05 / 8.04E-05 2.64E-04 12 1.15E-04 8.44E-05 / 8.67E-05 7.50E-05 / 9.37E-05 3.23E-04 2 1.14E-04 8.56E-05 / / 8.59E-05 / 9.37E-05 2.49E-04 −8 1.20E-04 8.46E-05 / / 9.02E-05 / 9.85E-05 3.48E-04 −18 1.42E-04 1.04E-04 / / 8.86E-05 / 1.00E-04 2.12E-04 −28 1.52E-04 9.70E-05 / / 8.29E-05 / 9.91E-05 2.96E-04 −38 / 8.99E-05 / / 7.95E-05 / 1.05E-04 2.38E-04 −48 / 9.29E-05 / / 1.00E-04 / 1.04E-04 1.97E-04 −58 / 1.06E-04 / / 1.48E-04 / 1.15E-04 2.29E-04 −68 / 9.27E-05 / / 8.90E-05 / 9.32E-05 2.16E-04 −78 / 1.09E-04 / / 6.48E-04 / 1.04E-04 1.88E-04 −88 / 9.75E-04 / / 1.11E-04 / 1.10E-04 1.66E-04 −98 / 1.01E-04 / / 1.83E-04 / 1.10E-04 5.92E-04 −108 / 1.07E-04 / / 3.83E-04 / 1.07E-04 8.23E-04 −118 / / / / / / 1.13E-04 8.29E-04
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表 3 水力梯度计算表
Table 3. Hydraulic gradient calculation table
矢量方向 水位差/m 水平距离/m 水力梯度 $J$ 水22 水62 6.16 217 0.02839 DK2 水62 3.94 166 0.02374 SK5 SK4 7.37 565 0.03396 SK5 SK3 31.85 217 0.14677 J2 SK5 6.66 369 0.01805
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