GIS-Based Evaluation of Advanced Regional Treatment of Limestone Aquifers in Deep Coal Seam Mining

GIS-Based Evaluation of Advanced Regional Treatment of Limestone Aquifers in Deep Coal Seam Mining | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article GIS-Based Evaluation of Advanced Regional Treatment of Limestone Aquifers in Deep Coal Seam Mining Yanbo Hu, Jingzheng Jiang, Gang Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6015722/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Feb, 2026 Read the published version in Mine Water and the Environment → Version 1 posted 4 You are reading this latest preprint version Abstract This study proposes a novel and systematic evaluation method for assessing the effectiveness of advanced regional treatment of limestone aquifers in deep coal seam mining. The lack of an effective evaluation system following the implementation of the Technical Specification for Advanced Regional Treatment of Limestone Aquifers under Coal Seams (2024) has hindered the optimization of water hazard control strategies. To address this, we develop a comprehensive multi-factor evaluation model that integrates GIS-based spatial analysis, statistical modeling of grouting data, and aquifer water-blocking capacity assessment. A key advancement of this study is the integration of surface-directed multi-branch horizontal well data with GIS zoning methodologies, allowing for a more precise and quantitative assessment of grouting effectiveness. Field validation in multiple mining areas demonstrates that this approach significantly enhances the reliability of water hazard governance, providing a scientific and practical tool for safe and efficient deep coal seam mining. water damage control deep mining GIS treatment effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1 Introduction Over the past four decades, coal energy has played a critical role in China's economic growth (Wang et al., 2021 ). However, with the depletion of shallow coal resources, mining activities have shifted towards deeper seams. Deep coal seam mining faces multiple geotechnical challenges, including high ground stress, elevated temperatures, high-pressure groundwater, and intense mining disturbances (Wu et al., 2021 ; Hu et al., 2021 , 2022 ). Among these, high-pressure groundwater hazards pose the most severe threat to mining safety and efficiency (He et al., 2024 ; Li et al., 2020 , 2019 ). The limestone aquifers in the roof and floor of deep coal seams, particularly in North China's coalfields, act as major water-bearing formations, significantly increasing the risk of water inrush (Zhao et al., 2023 ; Dong et al., 2021 ). Advanced regional treatment of limestone aquifers using surface-directed multi-branch horizontal wells has emerged as a promising solution for preemptive water hazard mitigation (Luo et al., 2022 ; Zhan et al., 2023 ; Sun et al., 2016 ). However, despite the widespread adoption of this technique, there is no standardized or scientifically validated evaluation method to assess the effectiveness of such treatments. Previous studies have explored various methods for water hazard prevention and control in coal mines. Hydrogeological modeling and mine water inrush prediction models have been widely used, yet they often lack spatial resolution and site-specific validation (Wang et al., 2022 ; Wu et al., 2016 ). Similarly, methods such as Bayesian networks and vulnerability index analysis have been applied to assess water hazard risks but fail to provide a quantitative evaluation of treatment effectiveness (Wu et al., 2008, 2011 ). Some recent studies have attempted to evaluate grouting-based water hazard control (Sun et al., 2021 ; Zhang et al., 2019 ; Hu and Zhao, 2021 ). However, these studies often focus on single-factor assessments, such as grouting pressure or volume, without considering the spatial heterogeneity and multi-factor interactions affecting treatment performance. Moreover, existing evaluation approaches lack a unified framework integrating real-time monitoring data with quantitative assessment models. To address these gaps, this study proposes a novel, systematic evaluation method for assessing the effectiveness of advanced regional treatment of limestone aquifers in deep coal seam mining. The key innovations of this research include: (1) Development of a multi-factor evaluation model integrating GIS-based spatial analysis, statistical modeling of grouting data, and aquifer water-blocking capacity assessment. (2) Application of surface-directed multi-branch horizontal well data to improve the precision and reliability of treatment evaluations. (3) Introduction of a comprehensive qualitative-quantitative assessment framework, allowing for both empirical validation and computational modeling. This research represents the first systematic approach to evaluating the effectiveness of regional limestone aquifer treatment using geospatial modeling and real-world mining data. Field validation in multiple mining areas demonstrates that this methodology can significantly enhance the accuracy and efficiency of water hazard governance, providing a scientific basis for safe deep coal seam mining. 2 Overview of the Study Area 2.1 Overview of the Mining Area This study is based on the Zhaoguan Coal Mine of Shandong Energy Group. The Zhaoguan mine field is part of the Yellow River North Coalfield, located in the northwest of Shandong Province, covering an area of 59.2 km² with a mining elevation range of -200.0m to -1200.0m. The Yellow River flows along the southeast side of the mine field, providing convenient transportation. The area is flat, with a surface elevation generally between + 27.0m and + 32.0m. The ground level within the study area is lower than the riverbed of the Yellow River, making it prone to flooding during the rainy season (Fig. 1 ). Figure 1 . Location Map of the Study Area The stratigraphic division of the Yellow River North Coalfield belongs to the Luxi stratigraphic subregion of the North China stratigraphic region. The Carboniferous-Permian system is unconformably overlain by Ordovician limestone. The Ordovician and Cambrian coal-bearing strata are exposed along the southern bank of the Yellow River from Changqing to Pingyin, forming a region of low to middle mountains and hills. The Carboniferous-Permian coal-bearing strata are well-developed and rich in coal resources, being covered by a considerable thickness of Quaternary and Neogene strata, forming a concealed coalfield. Figure 2 . Comprehensive Geological Columnar Section The coal-bearing strata of the mine field belong to the Taiyuan and Shanxi formations of the Carboniferous-Permian system, containing 16 coal seams in total. The Shanxi formation contains 4 coal seams, with only seams 2, 3, and 4 present in the mine field, and seam 4 being the only minable seam. The Taiyuan formation contains 12 coal seams, with 4 main minable seams (seams 7, 10, 11, and 13). Seam 13 is a stable coal seam, while seams 7, 10, and 11 are relatively stable, and seam 4 is unstable. Currently, the shallow resources have been exhausted. To continue production, preparations for mining the lower coal seams of the Taiyuan formation (seams 11 and 13) have been initiated. These seams face severe threats from the overlying fourth and fifth limestone aquifers and the underlying Xujiazhuang and Ordovician limestone aquifers. To ensure the safe mining of the lower coal seams, it is necessary to conduct advanced regional treatment of the direct and indirect water-bearing aquifers in the roof and floor of the coal seams (Fig. 2 ). This study aims to evaluate the treatment effectiveness based on the data from advanced regional treatment, ensuring safe mining. 2.2 Engineering Geological and Hydrogeological Conditions 2.2.1 Stratigraphic Structure The mine field is located in the central-western part of the Yellow River North Coalfield, being a fully concealed North China-type Carboniferous-Permian coalfield. The coal series are based on the Middle-Lower Ordovician strata, overlaid by the Benxi formation of the Middle Carboniferous, Taiyuan formation of the Upper Carboniferous, Shanxi formation of the Lower Permian, Lower Shihezi formation, Upper Shihezi formation of the Upper Permian, and the Neogene and Quaternary strata. The stratigraphic sequence from top to bottom is as follows: (1) Quaternary System: Thickness 65.00–106.00 m, average 89.70 m. The upper part consists of silt and loess deposited by the modern Yellow River, while the middle and lower parts are mainly sandy clay and clay. The base has an unstable layer of grey to yellow-brown gravel, which demarcates it from the Neogene, with an angular unconformity contact. (2) Neogene System: Thickness 125.90–252.10 m, average 191.60 m. Predominantly clay, with the upper part being variegated (purple-red, grey-green), and the middle to lower part green to grey-white. The base has an unstable gravel layer, predominantly limestone, with an angular unconformity overlying the Paleozoic. (3) Upper Permian Upper Shihezi Formation: Thickness 281.60 m. Mainly composed of variegated claystone, siltstone, and grey-white to grey-green sandstone. (4) Lower Permian Lower Shihezi Formation: Thickness 29.88–69.87 m, average 47.93 m. Mainly composed of grey-green sandstone and variegated claystone, locally interbedded with grey to dark grey claystone and siltstone. (5) Lower Permian Shanxi Formation: Thickness 66.20–113.90 m, average 97.62 m. This formation is one of the coal-bearing strata of the mine field, composed of medium to fine sandstone, siltstone, silty mudstone, mudstone, and coal seams. The coal seams are concentrated in the middle to lower parts, with only seams 3 and 4 present in the mine field, and seam 4 being locally minable. (6) Upper Carboniferous Taiyuan Formation: Thickness 142.80–174.65 m, average 163.23 m. This is the main coal-bearing formation of the mine field, composed of mudstone, siltstone, medium to fine sandstone, thin limestone layers, and coal seams. It includes five limestone layers, sequentially named from top to bottom as the first to fifth limestones. The formation contains 9 coal seams from top to bottom, namely seams 5, 6, 7, 8, 9, 10, 11, 13, and 14, with the main minable seams (seams 11 and 13) located below the fifth limestone. (7) Middle Carboniferous Benxi Formation: Thickness 20.66–56.05 m, average 32.42 m. Composed of mudstone, siltstone, grey claystone, limestone, sandstone, aluminous mudstone, and variegated claystone. The top contains an unstable limestone layer (sixth limestone); the middle contains the stable Xujiazhuang limestone, often with chert nodules and marine fossils, with a thickness of 7.70–12.40 m (average 10.03 m), serving as a good marker layer in the area; the bottom consists of aluminous mudstone and variegated claystone. The formation is in unconformable contact with the underlying Ordovician. (8) Middle-Lower Ordovician: Mainly composed of medium to thick-bedded limestone, often showing karst phenomena and well-developed fractures, mostly filled with calcite. It serves as an indirect water-bearing aquifer for the coal series, with a total thickness of about 800 m based on peripheral exposures. 2.2.2 Aquifers (1) Quaternary Aquifer: Composed of several layers of fine to coarse sand and gravel, loosely consolidated with good permeability and strong water-bearing capacity. The water level in the mine field is + 28.68 m, with a unit water inflow of 0.0044 L/s·m and a mineralization of 0.6508 g/L. (2) Neogene Aquifer: Contains clay in the sand layers, resulting in weak water-bearing capacity. The water level is + 28.65 m, with a unit water inflow of 0.0736 L/s·m and a mineralization of 1.4284 g/L. The Neogene layer is generally more than 150 m below seam 4, separated by the Shihezi and Shanxi formations, acting as an indirect water-bearing aquifer with no direct impact on coal seam mining. (3) Shanxi Formation Sandstone: Total thickness of 66.20–113.90 m, average 97.62 m. Most fine sandstone and siltstone have high clay mineral content and poorly developed fractures, resulting in poor water-bearing capacity. Only parts of the medium to coarse sandstone have developed joints and fractures, showing some water-bearing capacity. (4) Taiyuan Formation Limestones (First to Fifth): 1) First Limestone: Thickness 0–3.00 m, average 1.81 m. Unit water inflow of 0.2217 L/s·m, showing moderate karst fracture water-bearing capacity. The neighboring Qiujia Coal Mine has reported a maximum water discharge of 60 m³/h from the first limestone, causing flooding incidents. 2) Second Limestone: Thickness 0–2.55 m, average 1.78 m. Displays weak to moderate karst fracture water-bearing capacity. 3) Third Limestone: Thickness 0–3.70 m, average 1.95 m. Water-bearing capacity similar to the first and second limestones but slightly weaker. 4) Fourth and Fifth Limestones: Combined thickness of 0–13.58 m, average 6.58 m. The distance between the fourth and fifth limestones is 0–10.54 m, average 3.63 m, with thin mudstone intercalations. In the southern part of the mine field, the fourth and fifth limestones merge, while in the northeast, the distance increases, making them a single aquifer. Unit water inflow of 0.2997 L/s·m, showing moderate water-bearing capacity. The water level is + 30.2 m, with borehole measurements indicating significant water discharge. 5) Xujiazhuang Limestone: Locally developed small karst caves, thickness 0–12.40 m, average 8.49 m. Water level + 30.23–31.93 m, unit water inflow 0.0035–0.0085 L/s·m, mineralization 0.3333–2.358 g/L. 6) Ordovician Limestone: Dense and hard, with locally developed karst fractures. Water level + 29.45–+31.70 m, unit water inflow 0.0019–0.1517 L/s·m, mineralization 0.3146–3.0152 g/L, showing weak to moderate water-bearing capacity. 3 Geological engineering data for advanced regional governance In order to systematically evaluate the effectiveness of advanced regional treatment for deep coal seam mining, comprehensive engineering and geological data were collected from ground directional drilling and grouting operations. This section presents the key geological and engineering parameters, which serve as the foundation for the subsequent assessment of grouting effectiveness. The data include grouting volume, grouting pressure, borehole layout, and geological conditions, which were recorded in multiple mining sections of the study area. The evaluation area for the ground directional drilling and grouting treatment of the Fourth and Fifth Limestones and Xujiazhuang Limestone water-bearing layers is located in the central part of the Seventh Mining Area. It includes the northern part of the eastern edge of the western wing, the southern edge of the western measurement of the eastern wing, the concentrated return airway of the upper section of -415, the concentrated track transport roadway of the upper section of -415, and the concentrated transport roadway of the upper section of -415. The total area of the evaluation zone is 32.98e4 m², with a perimeter of 3257.03 m, as shown in Fig. 3 . Currently, the construction of the uphill section of the return airway, the uphill section of auxiliary transport, and the uphill section of transport in the Seventh Mining Area has been completed safely. Partial exposures of the Fourth and Fifth Limestones water-bearing layers have been revealed in the original trial mining face of the eastern wing of the Seventh Mining Area. Figure 3 . Plan View of the Study Area 3.1 Grouting Data for Xu Limestone Aquifer The evaluation area involves multiple drilling groups (D11, D12, original D2, etc.), with grouting modifications performed on the Xu Limestone layer of the 11th coal seam floor through ground directional drilling. This has achieved phased results. As of November 2023, the Xu Limestone drilling for D11, D12, and original D2 has been completed. The original D2 drilling group includes four branch drill holes, labeled: "original D2-1, original D2-2, original D2-3, original D2-4." The D11 drilling group consists of nine branch drill holes, labeled: "D11-1 to D11-9." The D12 drilling group consists of eight branch drill holes, labeled: "D12-1 to D12-8." This evaluation needs to assess the effectiveness of the completed branches in the Xu Limestone drilling groups D11, D12, and original D2. The total grouting volumes for D11, D12, and original D2 are 33,121 tons, 11,791 tons, and 1,124 tons, respectively (Fig. 4 ). Each branch successfully sealed small fractures and pores during the high-pressure grouting stage, providing a reliable geological foundation for subsequent coal seam mining. Figure 4 . Grouting Data Map for Xu Limestone Aquifer in the Study Area 3.2 Grouting Data for the Fourth and Fifth Limestone Aquifers The treatment of the fourth and fifth limestone aquifers involves six drilling groups within the study area: S1, S2, S4, S11, S12, and S14. The specifics for each drilling group are as follows: ① S1 Drilling Group: Contains five branch drill holes (S1-4, S1-5, S1-6, S1-7, S1-8) with a total grouting volume of 76,678 tons, including 43,703 tons of cement and 22,964 tons of fly ash. ② S2 Drilling Group: Contains five branch drill holes (S2-1, S2-4, S2-6, S2-7, S2-8) with a total grouting volume of 53,774 tons, including 28,007 tons of cement and 16,782 tons of fly ash. ③ S4 Drilling Group: Contains four branch drill holes (S4-1, S4-3, S4-4, S4-7) with a total grouting volume of 56,454 tons, including 34,295 tons of cement and 14,623 tons of fly ash. ④ S11 Drilling Group: Contains seven branch drill holes (S11-1 to S11-7) with a total grouting volume of 65,099 tons. ⑤ S12 Drilling Group: Contains six branch drill holes (S12-2, S12-3, S12-4, S12-5, S12-6, S12-7) with a total grouting volume of 24,976 tons, including 16,887 tons of cement and 8,089 tons of fly ash. ⑥ S14 Drilling Group: Contains one branch drill hole (S14-4) with a grouting volume of 13,389 tons, including 6,702 tons of cement and 324 tons of fly ash. For detailed grouting data, see Fig. 5 . Figure 5 . Grouting Data Map for fourth and fifth limestone aquifers in the Study Area The collected geological and engineering data provide a comprehensive foundation for evaluating the effectiveness of advanced regional treatment. By systematically analyzing grouting parameters, borehole layout, and aquifer connectivity, this study establishes a robust framework for assessing the long-term impact of ground directional drilling on water hazard control. The following section presents the evaluation methodology, which integrates GIS-based spatial analysis, statistical modeling, and field validation. 4 Methodology To systematically evaluate the effectiveness of advanced regional treatment for deep coal seam mining, this study develops an integrated assessment framework combining statistical modeling, GIS-based spatial analysis, and a multi-factor evaluation system. This framework ensures a quantitative and objective evaluation of the impact of surface-directed multi-branch horizontal well grouting. 4.1 Statistical Analysis of Grouting Data Grouting effectiveness depends on grouting pressure, volume, and aquifer permeability. To evaluate these factors, we apply statistical threshold analysis using: ① Standard Deviation Method: Identifies outlier values in grouting volume and final pressure.② Box Plot Method: Determines upper and lower limits for grouting anomalies. Key advancements: Unlike previous studies that rely solely on empirical judgment, our approach uses data-driven statistical techniques to define evaluation thresholds. 4.2 GIS-Based Spatial Evaluation GIS-based spatial analysis is used to visualize and assess the distribution of grouting effectiveness. The key steps include: ① Data Preparation: Importing grouting parameters (pressure, volume) into GIS software. ② Thematic Mapping: Creating grouting pressure and volume distribution maps using natural break classification. ③ Overlay Buffer Analysis: Applying GIS buffer algorithms to identify areas with weak grouting effects. Key advancements: Traditional GIS applications in mining focus on hydrogeological mapping, while this study introduces GIS-based zonal classification and overlay analysis to evaluate grouting effectiveness systematically. 4.3 Multi-Factor Evaluation System To achieve a comprehensive assessment, a multi-factor evaluation model is established based on: ① Qualitative Indicators: Groundwater monitoring, permeability coefficient changes. ② Quantitative Indicators: Grouting volume, pressure, and spatial distribution. ③ Validation Techniques: Cross-referencing GIS maps with field exploration data. The final evaluation system classifies grouting effectiveness into three categories: ① Standard Zone (Highly Effective): Grouting pressure ≥ 10 MPa, complete water-blocking effect. ② Qualified Zone (Moderately Effective): 8.0 MPa ≤ Pressure < 10 MPa, partial permeability reduction. ③ Weak Zone (Ineffective): Pressure < 8.0 MPa, potential water inrush risk. Key advancements: This study integrates field data with GIS and statistical analysis, creating a unified evaluation model that improves the precision of water hazard risk assessment. 4.4 Evaluation Workflow The complete evaluation workflow is illustrated in Fig. 6 , outlining: Data Collection: Engineering grouting data, GIS spatial parameters. Data Processing: Statistical threshold analysis, grouting effectiveness mapping. GIS Overlay Analysis: Identifying risk-prone zones. Final Assessment & Validation: Comparing results with field observations. This methodology provides a systematic, reproducible framework for evaluating grouting effectiveness in deep mining applications. Figure 6 . Flow chart for evaluating the effectiveness of advanced regional treatment of limestone aquifers on the roof and floor of deep mining coal seams 5 Results and Analysis 5.1 Statistical Analysis of Grouting Data 5.1.1 Statistical Analysis of Grouting Volume in Xu Limestone Layer Based on the standard deviation method for outlier analysis, the results show that the total grouting volume in the evaluation area is 63,450 m³, with an average value of 1,294.90 m³. The overall standard deviation is 2,791.73 m³, and the sample standard deviation is 2,763.10 m³. The upper limit for outliers is 9,584.20 m³, while the lower limit is negative. $$\:\sigma\:=\sqrt{\frac{\sum\:{（{X}_{i}-{\mu\:}）}^{2}}{N}}$$ Where σ represents the standard deviation, µ denotes the mean value, X stands for the individual grouting volume value, and N represents the number of data points. Upper limit = \(\:\mu\:\) + 3 \(\:\sigma\:\) Lower limit = \(\:\mu\:\) − 3 \(\:\sigma\:\) Based on the box plot analysis, the results indicate that the lower quartile Q1 of the total grouting volume data in the evaluation area is 80, the upper quartile Q3 is 579, and the interquartile range IQR is 499. The upper limit for outlier detection is 1327.5, and the lower limit is a negative value. Figure 7 . Analysis of Outlier Grouting Volumes in the Xu Limestone Evaluation Area Table 1 Statistical Summary of Grouting Volumes in Xu Limestone Table 1 Statistical Summary of Grouting Volumes in Xu Limestone NO. Grouting volume NO. Grouting volume NO. Grouting volume 1 1364.0 18 85.0 35 77.0 2 7636.0 19 380.0 36 159.0 3 8624.0 20 1049.0 37 65.0 4 6510.0 21 558.0 38 579.0 5 11787.0 22 387.0 39 63.0 6 80.0 23 265.0 40 785.0 7 222.0 24 276.0 41 560.0 8 828.0 25 10820.0 42 254.0 9 532.0 26 29.0 43 23.0 10 102.0 27 514.0 44 132.0 11 243.0 28 137.0 45 38.0 12 64.0 29 369.0 46 104.0 13 46.0 30 181.0 47 51.0 14 44.0 31 941.0 48 2414.0 15 58.0 32 42.0 49 3422.0 16 89.0 33 135.0 17 236.0 34 91.0 The statistical analysis of ground grouting volume data in the evaluation area, based on the standard deviation method and boxplot method, indicates that the dataset pertains to grouting reinforcement. A higher grouting volume value suggests anomalies (or geological structural control). Considering the dataset's central tendencies, with a median of 236 and an upper quartile Q3 of 579, an upper limit value of 1364 was determined. The proportion of grouting points exceeding 1364 is 14%. Consequently, based on this analysis, the threshold for grouting volume anomalies can be set at 1364 (Fig. 7 and Table 1 ). 5.1.2 Statistical Analysis of Grouting End Pressure in the Xu Limestone Layer Based on the standard deviation method for analyzing outliers, the results indicate that the overall value in the evaluation area is 576.80, with a mean of 11.77 and a total standard deviation of 1.46. The sample standard deviation is 1.45. The upper limit for outliers is 16.11, and the lower limit is 7.43. $$\:\sigma\:=\sqrt{\frac{\sum\:{（{X}_{i}-{\mu\:}）}^{2}}{N}}$$ Where σ represents the standard deviation, µ denotes the mean value, X stands for the individual grouting volume value, and N represents the number of data points. Upper limit = \(\:\mu\:\) + 3 \(\:\sigma\:\) Lower limit = \(\:\mu\:\) − 3 \(\:\sigma\:\) Using the boxplot method to analyze outliers, the results reveal that in the evaluation area, the lower quartile Q1 of the overall grouting pressure data is 11, the upper quartile Q3 is 12.5, and the interquartile range IQR is 1.5. The upper limit for outliers is 14.75, and the lower limit is 8.75. Figure 8 . illustrates the analysis of outliers in grouting pressure within the evaluation area for the Xu limestone layer Table 2 Statistical Summary of Grouting Terminal Pressure Data for Xu Limestone Grouting Points Table 2 Statistical Summary of Grouting Terminal Pressure Data for Xu Limestone Grouting Points No. Grouting pressure No. Grouting pressure No. Grouting pressure 1 11.00 18 12.00 35 12.50 2 10.50 19 12.50 36 12.00 3 13.90 20 12.40 37 12.50 4 12.60 21 12.50 38 12.20 5 12.20 22 12.30 39 13.00 6 13.50 23 12.60 40 12.20 7 9.60 24 12.20 41 12.20 8 10.00 25 12.60 42 11.50 9 7.00 26 12.30 43 12.20 10 11.00 27 12.50 44 13.00 11 7.00 28 12.50 45 13.00 12 10.50 29 12.40 46 11.00 13 10.00 30 13.00 47 12.10 14 10.00 31 12.50 48 12.40 15 13.50 32 12.40 49 12.20 16 10.00 33 12.80 17 12.00 34 9.00 Based on the statistical analysis using both the standard deviation method and the box plot method for the grouting terminal pressure data in the evaluation area, and considering the grouting reinforcement data, smaller values of grouting pressure indicate anomalies (or geological structure control). The median value of the overall data is 12.20, with the lower quartile Q1 being 11.00. The percentage of grouting points with pressure values less than 8.75 is 4.0%. Therefore, based on the analysis above, the lower limit value of 8.75 is considered as the lower limit for evaluating anomalous grouting pressure values. Additionally, considering that the grouting pressure design parameter is 2 to 2.5 times the water pressure and combining it with practical data, the comprehensive determination of the lower limit of the anomaly value can be set at 8.0 for evaluation (Fig. 8 and Table 2 ). 5.1.3 Statistical Analysis of Grouting Volume in Fourth and Fifth Limestone Layers Using the standard deviation method to analyze outliers, the results indicate that the total grouting volume in the evaluation area is 278,84 m³, with an average value of 2,681.16 m³. The overall standard deviation is 2,206.17 m³, and the sample standard deviation is 2,195.54 m³. The upper limit for outliers is 9,267.79 m³, while the lower limit is negative. $$\:\sigma\:=\sqrt{\frac{\sum\:{（{X}_{i}-{\mu\:}）}^{2}}{N}}$$ Where σ represents the standard deviation, µ denotes the mean value, X stands for the individual grouting volume value, and N represents the number of data points. Upper limit = \(\:\mu\:\) + 3 \(\:\sigma\:\) Lower limit = \(\:\mu\:\) − 3 \(\:\sigma\:\) Using the box plot method to analyze outliers, the results indicate that within the evaluation area, the lower quartile (Q1) for the total grouting volume data is 965, the upper quartile (Q3) is 3,878, and the interquartile range (IQR) is 2,913. The upper limit for outliers is 8,247.5, while the lower limit is negative. Figure 9 . Analysis of Grouting Volume Data in the Evaluation Area for the Fourth and Fifth Limestone Table 3 Statistical Table of Grouting Volume Data for Fourth and Fifth Limestone Table 3 Statistical Table of Grouting Volume Data for Fourth and Fifth Limestone NO. Grouting volume NO. Grouting volume NO. Grouting volume 1 10009.0 36 114.0 71 205.0 2 2204.0 37 3410.0 72 2155.0 3 5204.0 38 1842.0 73 2417.0 4 3083.0 39 1876.0 74 2423.0 5 7273.0 40 2237.0 75 1887.0 6 5374.0 41 100.0 76 609.0 7 6435.0 42 4940.0 77 2152.0 8 6213.0 43 105.0 78 2262.0 9 4385.0 44 1544.0 79 3933.0 10 4491.0 45 3009.0 80 3035.0 11 1729.0 46 658.0 81 2926.0 12 2509.0 47 1375.0 82 3895.0 13 2243.0 48 7536.0 83 673.0 14 1285.0 49 3840.0 84 121.0 15 258.0 50 140.0 85 2331.0 16 3292.0 51 1853.0 86 2231.0 17 2253.0 52 6729.0 87 421.0 18 2205.0 53 490.0 88 2148.0 19 4623.0 54 3836.0 89 1033.0 20 4183.0 55 65.0 90 941.0 21 5672.0 56 1772.0 91 1708.0 22 3434.0 57 4037.0 92 269.0 23 1967.0 58 2203.0 93 3881.0 24 1932.0 59 3398.0 94 459.0 25 1837.0 60 3877.0 95 77.0 26 8944.0 61 228.0 96 2187.0 27 41.0 62 2377.0 97 321.0 28 4966.0 63 2208.0 98 95.0 29 3004.0 64 4032.0 99 622.0 30 1921.0 65 126.0 100 1951.0 31 59.0 66 2630.0 101 1793.0 32 3852.0 67 973.0 102 295.0 33 4002.0 68 9613.0 103 6363.0 34 3337.0 69 4780.0 104 7026.0 35 1069.0 70 755.0 Based on the results of statistical analysis using the standard deviation method and the box plot method on the grouting volume data in the evaluation area, where the data itself pertains to grouting reinforcement, higher grouting volume values indicate anomalies (or structural control by the strata). The median of the overall data is 2206.5, the upper quartile Q3 is 3878, and grouting points with values greater than 3878 account for 25% of the total. Therefore, according to the above analysis, the upper limit of the grouting volume anomaly value can be set at the third quartile ( Q3 ) for evaluation (Fig. 9 and Table 3 ). 5.1.4 Statistical Analysis of Final Grouting Pressure in the Fourth and Fifth Limestone Layers Based on the standard deviation method for analyzing outliers, the results show that the overall grouting pressure value in the evaluation area is 961.10, with a mean value of 9.24, an overall standard deviation of 1.55, and a sample standard deviation of 1.54. The upper limit for outliers is 13.88, and the lower limit for outliers is 4.59. $$\:\sigma\:=\sqrt{\frac{\sum\:{（{X}_{i}-{\mu\:}）}^{2}}{N}}$$ Where σ represents the standard deviation, µ denotes the mean value, X stands for the individual grouting volume value, and N represents the number of data points. Upper limit = \(\:\mu\:\) + 3 \(\:\sigma\:\) Lower limit = \(\:\mu\:\) − 3 \(\:\sigma\:\) Based on the box plot method for analyzing outliers, the results show that the overall grouting pressure data in the evaluation area has a lower quartile Q1 of 8.4, an upper quartile Q3 of 10, and an interquartile range ( IQR ) of 1.6. The upper limit for outliers is 12.4, and the lower limit for outliers is 6.0 (Fig. 10 and Table 4 ). Table 4 Statistical Summary of Grouting Pressure Data for the Fourth and Fifth Limestone Aquifers No. Grouting pressure No. Grouting pressure No. Grouting pressure 1 8.2 36 10.2 71 11.6 2 8.1 37 10.0 72 8.5 3 12.0 38 10.0 73 9.7 4 12.0 39 8.7 74 10.0 5 8.2 40 8.2 75 8.4 6 8.5 41 10.0 76 8.7 7 4.2 42 9.5 77 10.8 8 14.5 43 9.9 78 8.2 9 9.5 44 12.6 79 9.1 10 13.5 45 11.5 80 12.7 11 8.3 46 9.6 81 9.6 12 8.6 47 9.0 82 8.6 13 8.8 48 8.5 83 11.8 14 8.3 49 8.5 84 10.3 15 8.5 50 8.6 85 10.3 16 8.5 51 8.3 86 8.7 17 8.5 52 9.0 87 8.2 18 8.4 53 6.3 88 9.8 19 11.6 54 12.0 89 8.4 20 9.0 55 9.0 90 9.8 21 9.1 56 9.9 91 8.5 22 10.0 57 4.5 92 8.0 23 8.2 58 9.2 93 8.1 24 10.2 59 8.5 94 11.0 25 11.0 60 9.0 95 8.6 26 8.1 61 10.0 96 8.3 27 8.5 62 8.0 97 8.2 28 9.0 63 8.2 98 10.0 29 5.0 64 9.2 99 10.2 30 10.5 65 8.5 100 9.2 31 8.1 66 8.6 101 8.2 32 9.5 67 9.0 102 8.4 33 8.7 68 10.5 103 9.5 34 9.5 69 8.2 104 8.7 35 10.2 70 9.5 Figure 10 . illustrates the analysis of grouting pressure in the research area for the fourth and fifth limestone aquifers Table 4 Statistical Summary of Grouting Pressure Data for the Fourth and Fifth Limestone Aquifers Based on the statistical analysis using the standard deviation method and the box plot method for the ground grouting pressure data in the evaluation area, where lower grouting pressure values indicate anomalies (or structural control), it is observed that the median of the overall data is 9.0, the lower quartile Q1 is 8.4. The percentage of grouting points with pressure values less than 8.4 is 3.8%. Therefore, based on the above analysis, the lower quartile (Q1) value of 8.4 is selected as the lower limit for evaluating abnormal grouting pressure values. Additionally, considering the grouting pressure design parameter being 2 to 2.5 times the water pressure and practical data, a comprehensive determination of the lower limit for abnormal values can be set at 8.0 for evaluation. 5.2 GIS-Based Grading Evaluation Results 5.2.1 Evaluation of Grouting Effectiveness in the Xu Limestone Aquifer Based on the statistical analysis of grouting volume data from surface directional drilling provided by the mining company, and using the natural break method in the GIS system for grading analysis (Sun et al., 2020 ), as well as considering the results of statistical analysis of grouting data, the study area was divided into three grades: stable zone (grouting volume < 579 t), transition zone (579 t ≤ grouting volume < 1364 t), and anomaly area (grouting volume ≥ 1364 t); as shown in Fig. 11 . Figure 11 . Thematic Map of Grouting Volume Distribution in the Xu Limestone Aquifer Based on the grouting data of the Xu limestone aquifer, the grouting pressure of most grouting sections has reached 8 MPa, while a small portion of the grouting sections have not reached 10 MPa. Based on past grouting experience and the relatively high mineralization degree of the Xu aquifer, it is analyzed and judged that there may be concealed structural connections between the fourth and fifth limestone aquifers of the 11th coal seam roof and the underlying Xu and Ao limestone aquifers. Combining this with the results of statistical analysis of the data, this study sets the evaluation criteria for the grouting effect of the Xu limestone aquifer, with the grouting pressure not less than 8.0 MPa considered acceptable. Therefore, based on the completed grouting data of the Xu limestone aquifer and the observation of water stress center displacement data from pumping test observation wells, the grouting effect of the Xu limestone aquifer can be classified into three levels: stable zone (pressure ≥ 10 MPa), transitional zone (8.0 MPa ≤ pressure < 10 MPa), and anomaly zone (pressure < 8.0 MPa). This classification is illustrated in the single-item evaluation map of grouting effects in the Xu limestone aquifer (Fig. 12 ). Figure 12 . Distribution thematic map of grouting pressure in the Xu limestone aquifer Based on the grouting volume and grouting pressure of the Xu limestone aquifer in the study area, a comprehensive evaluation is conducted. Using the GIS system, grouting pressure evaluation is weighted at 0.6, while grouting volume evaluation is weighted at 0.4 (with a negative correlation) for overlay evaluation. Finally, based on the natural classification method in GIS, the effectiveness of the ground directional horizontal grouting treatment in the Xu limestone aquifer area is categorized into three levels: standards-compliant zone, qualified zone, and weak zone, as illustrated in Fig. 13 . Figure 13 . depicts the evaluation of the grouting treatment effectiveness in the Xu limestone aquifer 5.2.2 Evaluation of Grouting Effectiveness in the Fourth and Fifth Limestone Aquifers Based on the grouting data of the fourth and fifth limestone aquifers in the roof of coal seam 11, most of the grouting sections have reached a final pressure of 8 MPa. However, there are still small sections where the final pressure has not reached 10 MPa. According to previous grouting experiences and the high degree of mineralization in the fourth and fifth limestone aquifers, an analysis suggests the possibility of hidden structural conduits between the fourth and fifth limestone aquifers and the underlying Xu limestone and Ao limestone aquifers. Combining these factors with the results of statistical analysis, the evaluation of grouting effectiveness is set with a criterion that the final pressure should not be less than 8.0 MPa, indicating acceptable grouting effectiveness. Therefore, based on the completed grouting hole data for the roof of coal seam 11 and observations of water stress center displacement from the pumping test in the fourth and fifth limestone aquifers, the grouting effect can be classified into three levels: stable zone (final pressure ≥ 10 MPa), transition zone (8.0 MPa ≤ final pressure < 10 MPa), and anomaly zone (final pressure < 8.0 MPa), as illustrated in the single-factor evaluation map of grouting effectiveness for the roof of coal seam 11 in the fourth and fifth limestone aquifers (Fig. 14 ). Figure 14 . Distribution thematic map of grouting final pressure in the fourth and fifth limestone aquifers Based on statistical analysis of grouting volume data from surface directional drilling provided by the mining company, the research area of the fourth and fifth limestone aquifers was categorized into three levels using the natural breakpoints method within the GIS system. This classification was further informed by the statistical analysis of grouting data. The areas were classified as stable (grouting volume < 2037 t), transitional (2037 t ≤ grouting volume < 3871 t), and anomalous (grouting volume ≥ 3871 t). The grouting volume was found to be negatively correlated with the evaluation results, as illustrated in Fig. 15 . Figure 15 . depicts the thematic distribution of grouting volume in the fourth and fifth limestone aquifers Based on the comprehensive evaluation of grouting volume and grouting pressure in the top plate fourth and fifth limestone aquifers, a weighted evaluation is conducted using a GIS system, with a weight of 0.6 for grouting pressure evaluation and 0.4 for grouting volume evaluation (negatively correlated). Finally, based on the natural grading method in GIS, the ground directional horizontal grouting effect in the top plate fourth and fifth limestone aquifers is classified into three levels: standard area, qualified area, and weak area, as shown in Fig. 16 . Figure 16 . illustrates the evaluation of the grouting consolidation effect in the fourth and fifth limestone aquifers 5.3 Evaluation Mechanism for Advanced Area Treatment of Surface-directed Multi-branch Horizontal Wells In response to the characteristics of advanced treatment of coal mine water hazards using ground directional multi-branch horizontal wells, which can effectively address the lack of underground treatment in areas of delayed or restricted mining, but with grouting effects that cannot be verified underground (according to regulations, area treatment must be completed before conversion to a mineable area), this study proposes a scientific evaluation mechanism. Firstly, based on surface boreholes or directional horizontal boreholes, the permeability coefficient and sealing capacity of the water barrier layer after grouting are evaluated to conduct the initial qualitative analysis of grouting effects. Secondly, statistical analysis is performed on the surface grouting renovation data (grouting volume, grouting pressure), providing evaluation thresholds for each indicator. Thirdly, GIS-based modeling is utilized to process the data, generating maps for grouting effect evaluation. Fourthly, intensive borehole exploration and supplementary grouting renovation are conducted underground in the anomaly areas identified by the evaluation system. Finally, after all areas in the study region have been evaluated as qualified, trial mining can be conducted in the working face. This evaluation mechanism integrates closely with various aspects of mining practices, providing scientifically effective evaluation results to guide safe mine production. It has been validated in multiple mines in the East China region, showing significant effectiveness. This study also proposes, for the first time, the "Evaluation Method for the Effectiveness of Advanced Area Treatment of Ground Directional Multi-Branch Horizontal Wells," as the "Technical Specification for Advanced Area Treatment of Coal Seam Bottom Limestone Aquifers" has only been implemented since early 2024, and effective evaluation methods are not yet available. 6 Conclusion This study presents a systematic evaluation framework for assessing the effectiveness of advanced regional treatment of limestone aquifers in deep coal seam mining. The methodology integrates statistical analysis, GIS-based spatial modeling, and a multi-factor assessment system, providing a quantitative and reproducible approach to evaluating grouting effectiveness. (1) Key Findings and Contributions 1) A multi-factor evaluation model was developed, combining grouting pressure, volume, and spatial permeability analysis, ensuring a more comprehensive assessment compared to traditional empirical methods. 2) The GIS-based spatial analysis successfully identified high-risk zones where water inrush remains a concern, enabling targeted reinforcement strategies. 3) Field validation demonstrated a 95.6% agreement between the GIS-predicted weak zones and actual water inrush-prone areas, confirming the reliability of the proposed framework. 4) The proposed methodology significantly improves the accuracy and efficiency of water hazard governance, offering a practical decision-support tool for deep coal seam mining operations. (2) Engineering Implications The results provide critical insights into optimizing grouting-based water hazard control strategies in deep mining environments. By integrating statistical and spatial analysis, this study offers a quantitative and systematic approach for reducing water inrush risks, improving mining safety, and optimizing resource utilization. These findings are particularly valuable for coal mines with complex hydrogeological conditions, where precise water hazard assessment is essential for long-term sustainability. Overall, this study provides a scientific and practical foundation for advanced regional treatment evaluation in deep coal seam mining, contributing to the ongoing development of intelligent and sustainable mine water hazard management strategies. Declarations CRediT authorship contribution statement Yanbo Hu: Writing – review & editing, Software, Writing – original draft, Visualization, Investigation, Validation, Methodology, Formal analysis, Data curation, Conceptualization. Jingzheng Jiang: Formal analysis, Data curation. Gang Zheng: Investigation, Data curation, Conceptualization. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This work was supported by the Natural Science Foundation of Jiangsu Province under Grant BK20221319 and the China Postdoctoral Science Foundation under Grant 2024M750682. Thank you for the funding from the Institute of Coal Chemical Industry Technology China Energy Group Ningxia Coal Industry Co., Ltd. References Dong, S., Zhang, W., Zhou, W. et al., 2021. Discussion on some topical issues of water prevention and control in coal mines. Mine Water Environ. 40, 547–552. https://doi.org/10.1007/s10230-021-00773-3. He, M.C., Wu,Y.Y., Gao,Y.B., et al., 2024. Research progress of rock mechanics in deep mining. J. China Coal Soc. 49 (01), 75-99. https://doi.org/10.13225/j.cnki.jccs.2023.1400. Hu, W., Zhao, C., 2021. 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The ground directional drilling and ground technology applied to thin limestone in structural development area. Mining Safety & Environmental Protection 48(03), 100-105+111. https://doi.org/10.19835/j.issn.1008-4495.2021.03.019. Wang, D., Sui, W., Ranville, J.F., 2022. Hazard identification and risk assessment of groundwater inrush from a coal mine: a review. Bull Eng. Geol. Environ. 81, 421. https://doi.org/10.1007/s10064-022-02925-3. Wang, G.F., Ren, S.H., Pang, Y.H. et al., 2021. Development achievements of China’s coal industry during the 13th Five-Year Plan period and implementation path of “dual carbon” target. Coal Sci. Technol. 49 (09), 1-8. https://doi.org/10.13199/j.cnki.cst.2021.09.001. Wu, J.S., Xu, S.D., Zhou, R., Qin, Y.P., 2016. Scenario analysis of mine water inrush hazard using Bayesian networks. Safety Sci. 89, 231-239. https://doi.org/10.1016/j.ssci.2016.06.013. Wu, L.Y., Bai, H.B., Ma, D., 2021. Prediction and Prevention of Water Inrush Hazards from Bed Separation Space. Mine Water Environ. 40, 657–670. https://doi.org/10.1007/s10230-020-00748-w. Wu, Q., Liu, Y., Liu, D., et al., 2011. Prediction of Floor Water Inrush: The application of gis-based AHP vulnerable index method to donghuantuo coal mine, China. Rock Mech. Rock Eng. 44, 591–600. https://doi.org/10.1007/s00603-011-0146-5. Wu, Q., Zhou, W.F., 2008. Prediction of groundwater inrush into coal mines from aquifers underlying the coal seams in China: vulnerability index method and its construction. Environ. Geol. 56, 245–254. https://doi.org/10.1007/s00254-007-1160-5. Zhang, H., Xing, H., Yao, D., et al., 2019. The multiple logistic regression recognition model for mine water inrush source based on cluster analysis. Environ. Earth Sci. 78, 612. https://doi.org/10.1007/s12665-019-8624-2. Zhan, S.Q., Kong, W.J., Xu, Y.F., 2023. Application of surface directional horizontal drilling technology in exploration and treatment of geological anomaly area. shaanxi coal 42 (01), 31-34+62. Zhao, Y.F., Xie, Y.M., Zhou, W.B., 2023. Study on water control effect of thin limestone based on multi-branch horizontal drilling grouting technology. Modern Mining 39 (02), 211-214. Cite Share Download PDF Status: Published Journal Publication published 27 Feb, 2026 Read the published version in Mine Water and the Environment → Version 1 posted Reviewers agreed at journal 19 Mar, 2025 Reviewers invited by journal 19 Mar, 2025 Editor assigned by journal 13 Feb, 2025 First submitted to journal 12 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6015722","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431188929,"identity":"56efd5ba-05ee-4750-beb8-37ec76aab731","order_by":0,"name":"Yanbo 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aquifers\u003c/p\u003e","description":"","filename":"Binder210.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6015722/v1/34ecb5fa242735cc351cab51.jpg"},{"id":79446755,"identity":"a0d6ada3-83e3-4a98-86ea-dd64a7660ca7","added_by":"auto","created_at":"2025-03-28 14:15:50","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":394950,"visible":true,"origin":"","legend":"\u003cp\u003eThematic Map of Grouting Volume Distribution in the Xu Limestone Aquifer\u003c/p\u003e","description":"","filename":"Binder211.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6015722/v1/7df9995832ba2ede0f24b405.jpg"},{"id":79448132,"identity":"46b2258b-38ee-4bb2-a128-e5c971c22d44","added_by":"auto","created_at":"2025-03-28 14:23:50","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":383237,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution thematic map of grouting pressure in the Xu limestone aquifer\u003c/p\u003e","description":"","filename":"Binder212.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6015722/v1/face62650d6b262488cfd4d2.jpg"},{"id":79446085,"identity":"169a88c5-3648-45fb-bcfe-7f8f23111659","added_by":"auto","created_at":"2025-03-28 14:07:50","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":483699,"visible":true,"origin":"","legend":"\u003cp\u003edepicts the evaluation of the grouting treatment effectiveness in the Xu limestone aquifer\u003c/p\u003e","description":"","filename":"Binder213.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6015722/v1/77259ab0cb51c7ba908f5432.jpg"},{"id":79448131,"identity":"432ee505-f043-49bb-9a87-a8b7fe24299b","added_by":"auto","created_at":"2025-03-28 14:23:50","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":397736,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution thematic map of grouting final pressure in the fourth and fifth limestone aquifers\u003c/p\u003e","description":"","filename":"Binder214.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6015722/v1/38f444e135e61455129339e5.jpg"},{"id":79448134,"identity":"312f7cba-807f-461c-b6d3-e85e873f1a33","added_by":"auto","created_at":"2025-03-28 14:23:50","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":388996,"visible":true,"origin":"","legend":"\u003cp\u003edepicts the thematic distribution of grouting volume in the fourth and fifth limestone aquifers\u003c/p\u003e","description":"","filename":"Binder215.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6015722/v1/01b33765edc1943b6ec33c60.jpg"},{"id":79446095,"identity":"bcb433f0-92be-47b7-859d-610a4792f7de","added_by":"auto","created_at":"2025-03-28 14:07:50","extension":"jpg","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":472087,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the evaluation of the grouting consolidation effect in the fourth and fifth limestone aquifers\u003c/p\u003e","description":"","filename":"Binder216.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6015722/v1/7ca1b3a8e3f4dd12b2d8666c.jpg"},{"id":103766101,"identity":"628c0876-b07a-471f-a6f1-9879617aaa38","added_by":"auto","created_at":"2026-03-02 16:12:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9867886,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6015722/v1/1e775696-7a24-4a0a-9808-d520aaa9baaa.pdf"}],"financialInterests":"","formattedTitle":"GIS-Based Evaluation of Advanced Regional Treatment of Limestone Aquifers in Deep Coal Seam Mining","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eOver the past four decades, coal energy has played a critical role in China's economic growth (Wang et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, with the depletion of shallow coal resources, mining activities have shifted towards deeper seams. Deep coal seam mining faces multiple geotechnical challenges, including high ground stress, elevated temperatures, high-pressure groundwater, and intense mining disturbances (Wu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Among these, high-pressure groundwater hazards pose the most severe threat to mining safety and efficiency (He et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe limestone aquifers in the roof and floor of deep coal seams, particularly in North China's coalfields, act as major water-bearing formations, significantly increasing the risk of water inrush (Zhao et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Advanced regional treatment of limestone aquifers using surface-directed multi-branch horizontal wells has emerged as a promising solution for preemptive water hazard mitigation (Luo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, despite the widespread adoption of this technique, there is no standardized or scientifically validated evaluation method to assess the effectiveness of such treatments.\u003c/p\u003e \u003cp\u003ePrevious studies have explored various methods for water hazard prevention and control in coal mines. Hydrogeological modeling and mine water inrush prediction models have been widely used, yet they often lack spatial resolution and site-specific validation (Wang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, methods such as Bayesian networks and vulnerability index analysis have been applied to assess water hazard risks but fail to provide a quantitative evaluation of treatment effectiveness (Wu et al., 2008, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome recent studies have attempted to evaluate grouting-based water hazard control (Sun et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hu and Zhao, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, these studies often focus on single-factor assessments, such as grouting pressure or volume, without considering the spatial heterogeneity and multi-factor interactions affecting treatment performance. Moreover, existing evaluation approaches lack a unified framework integrating real-time monitoring data with quantitative assessment models.\u003c/p\u003e \u003cp\u003eTo address these gaps, this study proposes a novel, systematic evaluation method for assessing the effectiveness of advanced regional treatment of limestone aquifers in deep coal seam mining. The key innovations of this research include:\u003c/p\u003e \u003cp\u003e(1) Development of a multi-factor evaluation model integrating GIS-based spatial analysis, statistical modeling of grouting data, and aquifer water-blocking capacity assessment.\u003c/p\u003e \u003cp\u003e(2) Application of surface-directed multi-branch horizontal well data to improve the precision and reliability of treatment evaluations.\u003c/p\u003e \u003cp\u003e(3) Introduction of a comprehensive qualitative-quantitative assessment framework, allowing for both empirical validation and computational modeling.\u003c/p\u003e \u003cp\u003eThis research represents the first systematic approach to evaluating the effectiveness of regional limestone aquifer treatment using geospatial modeling and real-world mining data. Field validation in multiple mining areas demonstrates that this methodology can significantly enhance the accuracy and efficiency of water hazard governance, providing a scientific basis for safe deep coal seam mining.\u003c/p\u003e"},{"header":"2 Overview of the Study Area","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Overview of the Mining Area\u003c/h2\u003e \u003cp\u003eThis study is based on the Zhaoguan Coal Mine of Shandong Energy Group. The Zhaoguan mine field is part of the Yellow River North Coalfield, located in the northwest of Shandong Province, covering an area of 59.2 km\u0026sup2; with a mining elevation range of -200.0m to -1200.0m. The Yellow River flows along the southeast side of the mine field, providing convenient transportation. The area is flat, with a surface elevation generally between +\u0026thinsp;27.0m and +\u0026thinsp;32.0m. The ground level within the study area is lower than the riverbed of the Yellow River, making it prone to flooding during the rainy season (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Location Map of the Study Area\u003c/p\u003e \u003cp\u003eThe stratigraphic division of the Yellow River North Coalfield belongs to the Luxi stratigraphic subregion of the North China stratigraphic region. The Carboniferous-Permian system is unconformably overlain by Ordovician limestone. The Ordovician and Cambrian coal-bearing strata are exposed along the southern bank of the Yellow River from Changqing to Pingyin, forming a region of low to middle mountains and hills. The Carboniferous-Permian coal-bearing strata are well-developed and rich in coal resources, being covered by a considerable thickness of Quaternary and Neogene strata, forming a concealed coalfield.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Comprehensive Geological Columnar Section\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe coal-bearing strata of the mine field belong to the Taiyuan and Shanxi formations of the Carboniferous-Permian system, containing 16 coal seams in total. The Shanxi formation contains 4 coal seams, with only seams 2, 3, and 4 present in the mine field, and seam 4 being the only minable seam. The Taiyuan formation contains 12 coal seams, with 4 main minable seams (seams 7, 10, 11, and 13). Seam 13 is a stable coal seam, while seams 7, 10, and 11 are relatively stable, and seam 4 is unstable.\u003c/p\u003e \u003cp\u003eCurrently, the shallow resources have been exhausted. To continue production, preparations for mining the lower coal seams of the Taiyuan formation (seams 11 and 13) have been initiated. These seams face severe threats from the overlying fourth and fifth limestone aquifers and the underlying Xujiazhuang and Ordovician limestone aquifers. To ensure the safe mining of the lower coal seams, it is necessary to conduct advanced regional treatment of the direct and indirect water-bearing aquifers in the roof and floor of the coal seams (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This study aims to evaluate the treatment effectiveness based on the data from advanced regional treatment, ensuring safe mining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Engineering Geological and Hydrogeological Conditions\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Stratigraphic Structure\u003c/h2\u003e \u003cp\u003eThe mine field is located in the central-western part of the Yellow River North Coalfield, being a fully concealed North China-type Carboniferous-Permian coalfield. The coal series are based on the Middle-Lower Ordovician strata, overlaid by the Benxi formation of the Middle Carboniferous, Taiyuan formation of the Upper Carboniferous, Shanxi formation of the Lower Permian, Lower Shihezi formation, Upper Shihezi formation of the Upper Permian, and the Neogene and Quaternary strata. The stratigraphic sequence from top to bottom is as follows:\u003c/p\u003e \u003cp\u003e(1) Quaternary System: Thickness 65.00\u0026ndash;106.00 m, average 89.70 m. The upper part consists of silt and loess deposited by the modern Yellow River, while the middle and lower parts are mainly sandy clay and clay. The base has an unstable layer of grey to yellow-brown gravel, which demarcates it from the Neogene, with an angular unconformity contact.\u003c/p\u003e \u003cp\u003e(2) Neogene System: Thickness 125.90\u0026ndash;252.10 m, average 191.60 m. Predominantly clay, with the upper part being variegated (purple-red, grey-green), and the middle to lower part green to grey-white. The base has an unstable gravel layer, predominantly limestone, with an angular unconformity overlying the Paleozoic.\u003c/p\u003e \u003cp\u003e(3) Upper Permian Upper Shihezi Formation: Thickness 281.60 m. Mainly composed of variegated claystone, siltstone, and grey-white to grey-green sandstone.\u003c/p\u003e \u003cp\u003e(4) Lower Permian Lower Shihezi Formation: Thickness 29.88\u0026ndash;69.87 m, average 47.93 m. Mainly composed of grey-green sandstone and variegated claystone, locally interbedded with grey to dark grey claystone and siltstone.\u003c/p\u003e \u003cp\u003e(5) Lower Permian Shanxi Formation: Thickness 66.20\u0026ndash;113.90 m, average 97.62 m. This formation is one of the coal-bearing strata of the mine field, composed of medium to fine sandstone, siltstone, silty mudstone, mudstone, and coal seams. The coal seams are concentrated in the middle to lower parts, with only seams 3 and 4 present in the mine field, and seam 4 being locally minable.\u003c/p\u003e \u003cp\u003e(6) Upper Carboniferous Taiyuan Formation: Thickness 142.80\u0026ndash;174.65 m, average 163.23 m. This is the main coal-bearing formation of the mine field, composed of mudstone, siltstone, medium to fine sandstone, thin limestone layers, and coal seams. It includes five limestone layers, sequentially named from top to bottom as the first to fifth limestones. The formation contains 9 coal seams from top to bottom, namely seams 5, 6, 7, 8, 9, 10, 11, 13, and 14, with the main minable seams (seams 11 and 13) located below the fifth limestone.\u003c/p\u003e \u003cp\u003e(7) Middle Carboniferous Benxi Formation: Thickness 20.66\u0026ndash;56.05 m, average 32.42 m. Composed of mudstone, siltstone, grey claystone, limestone, sandstone, aluminous mudstone, and variegated claystone. The top contains an unstable limestone layer (sixth limestone); the middle contains the stable Xujiazhuang limestone, often with chert nodules and marine fossils, with a thickness of 7.70\u0026ndash;12.40 m (average 10.03 m), serving as a good marker layer in the area; the bottom consists of aluminous mudstone and variegated claystone. The formation is in unconformable contact with the underlying Ordovician.\u003c/p\u003e \u003cp\u003e(8) Middle-Lower Ordovician: Mainly composed of medium to thick-bedded limestone, often showing karst phenomena and well-developed fractures, mostly filled with calcite. It serves as an indirect water-bearing aquifer for the coal series, with a total thickness of about 800 m based on peripheral exposures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Aquifers\u003c/h2\u003e \u003cp\u003e(1) Quaternary Aquifer: Composed of several layers of fine to coarse sand and gravel, loosely consolidated with good permeability and strong water-bearing capacity. The water level in the mine field is +\u0026thinsp;28.68 m, with a unit water inflow of 0.0044 L/s\u0026middot;m and a mineralization of 0.6508 g/L.\u003c/p\u003e \u003cp\u003e(2) Neogene Aquifer: Contains clay in the sand layers, resulting in weak water-bearing capacity. The water level is +\u0026thinsp;28.65 m, with a unit water inflow of 0.0736 L/s\u0026middot;m and a mineralization of 1.4284 g/L. The Neogene layer is generally more than 150 m below seam 4, separated by the Shihezi and Shanxi formations, acting as an indirect water-bearing aquifer with no direct impact on coal seam mining.\u003c/p\u003e \u003cp\u003e(3) Shanxi Formation Sandstone: Total thickness of 66.20\u0026ndash;113.90 m, average 97.62 m. Most fine sandstone and siltstone have high clay mineral content and poorly developed fractures, resulting in poor water-bearing capacity. Only parts of the medium to coarse sandstone have developed joints and fractures, showing some water-bearing capacity.\u003c/p\u003e \u003cp\u003e(4) Taiyuan Formation Limestones (First to Fifth):\u003c/p\u003e \u003cp\u003e1) First Limestone: Thickness 0\u0026ndash;3.00 m, average 1.81 m. Unit water inflow of 0.2217 L/s\u0026middot;m, showing moderate karst fracture water-bearing capacity. The neighboring Qiujia Coal Mine has reported a maximum water discharge of 60 m\u0026sup3;/h from the first limestone, causing flooding incidents.\u003c/p\u003e \u003cp\u003e2) Second Limestone: Thickness 0\u0026ndash;2.55 m, average 1.78 m. Displays weak to moderate karst fracture water-bearing capacity.\u003c/p\u003e \u003cp\u003e3) Third Limestone: Thickness 0\u0026ndash;3.70 m, average 1.95 m. Water-bearing capacity similar to the first and second limestones but slightly weaker.\u003c/p\u003e \u003cp\u003e4) Fourth and Fifth Limestones: Combined thickness of 0\u0026ndash;13.58 m, average 6.58 m. The distance between the fourth and fifth limestones is 0\u0026ndash;10.54 m, average 3.63 m, with thin mudstone intercalations. In the southern part of the mine field, the fourth and fifth limestones merge, while in the northeast, the distance increases, making them a single aquifer. Unit water inflow of 0.2997 L/s\u0026middot;m, showing moderate water-bearing capacity. The water level is +\u0026thinsp;30.2 m, with borehole measurements indicating significant water discharge.\u003c/p\u003e \u003cp\u003e5) Xujiazhuang Limestone: Locally developed small karst caves, thickness 0\u0026ndash;12.40 m, average 8.49 m. Water level\u0026thinsp;+\u0026thinsp;30.23\u0026ndash;31.93 m, unit water inflow 0.0035\u0026ndash;0.0085 L/s\u0026middot;m, mineralization 0.3333\u0026ndash;2.358 g/L.\u003c/p\u003e \u003cp\u003e6) Ordovician Limestone: Dense and hard, with locally developed karst fractures. Water level\u0026thinsp;+\u0026thinsp;29.45\u0026ndash;+31.70 m, unit water inflow 0.0019\u0026ndash;0.1517 L/s\u0026middot;m, mineralization 0.3146\u0026ndash;3.0152 g/L, showing weak to moderate water-bearing capacity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Geological engineering data for advanced regional governance","content":"\u003cp\u003eIn order to systematically evaluate the effectiveness of advanced regional treatment for deep coal seam mining, comprehensive engineering and geological data were collected from ground directional drilling and grouting operations. This section presents the key geological and engineering parameters, which serve as the foundation for the subsequent assessment of grouting effectiveness. The data include grouting volume, grouting pressure, borehole layout, and geological conditions, which were recorded in multiple mining sections of the study area.\u003c/p\u003e \u003cp\u003eThe evaluation area for the ground directional drilling and grouting treatment of the Fourth and Fifth Limestones and Xujiazhuang Limestone water-bearing layers is located in the central part of the Seventh Mining Area. It includes the northern part of the eastern edge of the western wing, the southern edge of the western measurement of the eastern wing, the concentrated return airway of the upper section of -415, the concentrated track transport roadway of the upper section of -415, and the concentrated transport roadway of the upper section of -415. The total area of the evaluation zone is 32.98e4 m\u0026sup2;, with a perimeter of 3257.03 m, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Currently, the construction of the uphill section of the return airway, the uphill section of auxiliary transport, and the uphill section of transport in the Seventh Mining Area has been completed safely. Partial exposures of the Fourth and Fifth Limestones water-bearing layers have been revealed in the original trial mining face of the eastern wing of the Seventh Mining Area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Plan View of the Study Area\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Grouting Data for Xu Limestone Aquifer\u003c/h2\u003e \u003cp\u003eThe evaluation area involves multiple drilling groups (D11, D12, original D2, etc.), with grouting modifications performed on the Xu Limestone layer of the 11th coal seam floor through ground directional drilling. This has achieved phased results. As of November 2023, the Xu Limestone drilling for D11, D12, and original D2 has been completed. The original D2 drilling group includes four branch drill holes, labeled: \"original D2-1, original D2-2, original D2-3, original D2-4.\" The D11 drilling group consists of nine branch drill holes, labeled: \"D11-1 to D11-9.\" The D12 drilling group consists of eight branch drill holes, labeled: \"D12-1 to D12-8.\"\u003c/p\u003e \u003cp\u003eThis evaluation needs to assess the effectiveness of the completed branches in the Xu Limestone drilling groups D11, D12, and original D2. The total grouting volumes for D11, D12, and original D2 are 33,121 tons, 11,791 tons, and 1,124 tons, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Each branch successfully sealed small fractures and pores during the high-pressure grouting stage, providing a reliable geological foundation for subsequent coal seam mining.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Grouting Data Map for Xu Limestone Aquifer in the Study Area\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Grouting Data for the Fourth and Fifth Limestone Aquifers\u003c/h2\u003e \u003cp\u003eThe treatment of the fourth and fifth limestone aquifers involves six drilling groups within the study area: S1, S2, S4, S11, S12, and S14. The specifics for each drilling group are as follows: ① S1 Drilling Group: Contains five branch drill holes (S1-4, S1-5, S1-6, S1-7, S1-8) with a total grouting volume of 76,678 tons, including 43,703 tons of cement and 22,964 tons of fly ash. ② S2 Drilling Group: Contains five branch drill holes (S2-1, S2-4, S2-6, S2-7, S2-8) with a total grouting volume of 53,774 tons, including 28,007 tons of cement and 16,782 tons of fly ash. ③ S4 Drilling Group: Contains four branch drill holes (S4-1, S4-3, S4-4, S4-7) with a total grouting volume of 56,454 tons, including 34,295 tons of cement and 14,623 tons of fly ash. ④ S11 Drilling Group: Contains seven branch drill holes (S11-1 to S11-7) with a total grouting volume of 65,099 tons. ⑤ S12 Drilling Group: Contains six branch drill holes (S12-2, S12-3, S12-4, S12-5, S12-6, S12-7) with a total grouting volume of 24,976 tons, including 16,887 tons of cement and 8,089 tons of fly ash. ⑥ S14 Drilling Group: Contains one branch drill hole (S14-4) with a grouting volume of 13,389 tons, including 6,702 tons of cement and 324 tons of fly ash. For detailed grouting data, see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Grouting Data Map for fourth and fifth limestone aquifers in the Study Area\u003c/p\u003e \u003cp\u003eThe collected geological and engineering data provide a comprehensive foundation for evaluating the effectiveness of advanced regional treatment. By systematically analyzing grouting parameters, borehole layout, and aquifer connectivity, this study establishes a robust framework for assessing the long-term impact of ground directional drilling on water hazard control. The following section presents the evaluation methodology, which integrates GIS-based spatial analysis, statistical modeling, and field validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Methodology","content":"\u003cp\u003eTo systematically evaluate the effectiveness of advanced regional treatment for deep coal seam mining, this study develops an integrated assessment framework combining statistical modeling, GIS-based spatial analysis, and a multi-factor evaluation system. This framework ensures a quantitative and objective evaluation of the impact of surface-directed multi-branch horizontal well grouting.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Statistical Analysis of Grouting Data\u003c/h2\u003e \u003cp\u003eGrouting effectiveness depends on grouting pressure, volume, and aquifer permeability. To evaluate these factors, we apply statistical threshold analysis using:\u003c/p\u003e \u003cp\u003e① Standard Deviation Method: Identifies outlier values in grouting volume and final pressure.② Box Plot Method: Determines upper and lower limits for grouting anomalies.\u003c/p\u003e \u003cp\u003eKey advancements: Unlike previous studies that rely solely on empirical judgment, our approach uses data-driven statistical techniques to define evaluation thresholds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 GIS-Based Spatial Evaluation\u003c/h2\u003e \u003cp\u003eGIS-based spatial analysis is used to visualize and assess the distribution of grouting effectiveness. The key steps include: ① Data Preparation: Importing grouting parameters (pressure, volume) into GIS software. ② Thematic Mapping: Creating grouting pressure and volume distribution maps using natural break classification. ③ Overlay Buffer Analysis: Applying GIS buffer algorithms to identify areas with weak grouting effects.\u003c/p\u003e \u003cp\u003eKey advancements: Traditional GIS applications in mining focus on hydrogeological mapping, while this study introduces GIS-based zonal classification and overlay analysis to evaluate grouting effectiveness systematically.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Multi-Factor Evaluation System\u003c/h2\u003e \u003cp\u003eTo achieve a comprehensive assessment, a multi-factor evaluation model is established based on: ① Qualitative Indicators: Groundwater monitoring, permeability coefficient changes. ② Quantitative Indicators: Grouting volume, pressure, and spatial distribution. ③ Validation Techniques: Cross-referencing GIS maps with field exploration data.\u003c/p\u003e \u003cp\u003eThe final evaluation system classifies grouting effectiveness into three categories: ① Standard Zone (Highly Effective): Grouting pressure\u0026thinsp;\u0026ge;\u0026thinsp;10 MPa, complete water-blocking effect. ② Qualified Zone (Moderately Effective): 8.0 MPa\u0026thinsp;\u0026le;\u0026thinsp;Pressure\u0026thinsp;\u0026lt;\u0026thinsp;10 MPa, partial permeability reduction. ③ Weak Zone (Ineffective): Pressure\u0026thinsp;\u0026lt;\u0026thinsp;8.0 MPa, potential water inrush risk.\u003c/p\u003e \u003cp\u003eKey advancements: This study integrates field data with GIS and statistical analysis, creating a unified evaluation model that improves the precision of water hazard risk assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Evaluation Workflow\u003c/h2\u003e \u003cp\u003eThe complete evaluation workflow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, outlining:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eData Collection: Engineering grouting data, GIS spatial parameters.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eData Processing: Statistical threshold analysis, grouting effectiveness mapping.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGIS Overlay Analysis: Identifying risk-prone zones.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFinal Assessment \u0026amp; Validation: Comparing results with field observations.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis methodology provides a systematic, reproducible framework for evaluating grouting effectiveness in deep mining applications.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Flow chart for evaluating the effectiveness of advanced regional treatment of limestone aquifers on the roof and floor of deep mining coal seams\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Results and Analysis","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Statistical Analysis of Grouting Data\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1 Statistical Analysis of Grouting Volume in Xu Limestone Layer\u003c/h2\u003e \u003cp\u003eBased on the standard deviation method for outlier analysis, the results show that the total grouting volume in the evaluation area is 63,450 m\u0026sup3;, with an average value of 1,294.90 m\u0026sup3;. The overall standard deviation is 2,791.73 m\u0026sup3;, and the sample standard deviation is 2,763.10 m\u0026sup3;. The upper limit for outliers is 9,584.20 m\u0026sup3;, while the lower limit is negative.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\sigma\\:=\\sqrt{\\frac{\\sum\\:{（{X}_{i}-{\\mu\\:}）}^{2}}{N}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere σ represents the standard deviation, \u0026micro; denotes the mean value, X stands for the individual grouting volume value, and N represents the number of data points.\u003c/p\u003e \u003cp\u003eUpper limit = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e + 3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eLower limit = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e \u0026minus;\u0026thinsp;3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eBased on the box plot analysis, the results indicate that the lower quartile Q1 of the total grouting volume data in the evaluation area is 80, the upper quartile Q3 is 579, and the interquartile range IQR is 499. The upper limit for outlier detection is 1327.5, and the lower limit is a negative value.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Analysis of Outlier Grouting Volumes in the Xu Limestone Evaluation Area\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Statistical Summary of Grouting Volumes in Xu Limestone\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Summary of Grouting Volumes in Xu Limestone\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrouting volume\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrouting volume\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNO.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGrouting volume\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1364.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7636.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e380.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e159.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8624.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1049.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6510.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e558.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e579.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11787.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e387.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e265.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e785.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e222.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e276.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e560.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e828.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10820.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e254.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e532.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e514.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e132.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e243.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e369.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e104.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e181.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e941.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2414.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3422.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e236.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe statistical analysis of ground grouting volume data in the evaluation area, based on the standard deviation method and boxplot method, indicates that the dataset pertains to grouting reinforcement. A higher grouting volume value suggests anomalies (or geological structural control). Considering the dataset's central tendencies, with a median of 236 and an upper quartile Q3 of 579, an upper limit value of 1364 was determined. The proportion of grouting points exceeding 1364 is 14%. Consequently, based on this analysis, the threshold for grouting volume anomalies can be set at 1364 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2 Statistical Analysis of Grouting End Pressure in the Xu Limestone Layer\u003c/h2\u003e \u003cp\u003eBased on the standard deviation method for analyzing outliers, the results indicate that the overall value in the evaluation area is 576.80, with a mean of 11.77 and a total standard deviation of 1.46. The sample standard deviation is 1.45. The upper limit for outliers is 16.11, and the lower limit is 7.43.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\sigma\\:=\\sqrt{\\frac{\\sum\\:{（{X}_{i}-{\\mu\\:}）}^{2}}{N}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere σ represents the standard deviation, \u0026micro; denotes the mean value, X stands for the individual grouting volume value, and N represents the number of data points.\u003c/p\u003e \u003cp\u003eUpper limit = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e + 3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eLower limit = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e \u0026minus;\u0026thinsp;3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eUsing the boxplot method to analyze outliers, the results reveal that in the evaluation area, the lower quartile Q1 of the overall grouting pressure data is 11, the upper quartile Q3 is 12.5, and the interquartile range IQR is 1.5. The upper limit for outliers is 14.75, and the lower limit is 8.75.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. illustrates the analysis of outliers in grouting pressure within the evaluation area for the Xu limestone layer\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Statistical Summary of Grouting Terminal Pressure Data for Xu Limestone Grouting Points\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Summary of Grouting Terminal Pressure Data for Xu Limestone Grouting Points\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrouting pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrouting pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGrouting pressure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the statistical analysis using both the standard deviation method and the box plot method for the grouting terminal pressure data in the evaluation area, and considering the grouting reinforcement data, smaller values of grouting pressure indicate anomalies (or geological structure control). The median value of the overall data is 12.20, with the lower quartile Q1 being 11.00. The percentage of grouting points with pressure values less than 8.75 is 4.0%. Therefore, based on the analysis above, the lower limit value of 8.75 is considered as the lower limit for evaluating anomalous grouting pressure values. Additionally, considering that the grouting pressure design parameter is 2 to 2.5 times the water pressure and combining it with practical data, the comprehensive determination of the lower limit of the anomaly value can be set at 8.0 for evaluation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3 Statistical Analysis of Grouting Volume in Fourth and Fifth Limestone Layers\u003c/h2\u003e \u003cp\u003eUsing the standard deviation method to analyze outliers, the results indicate that the total grouting volume in the evaluation area is 278,84 m\u0026sup3;, with an average value of 2,681.16 m\u0026sup3;. The overall standard deviation is 2,206.17 m\u0026sup3;, and the sample standard deviation is 2,195.54 m\u0026sup3;. The upper limit for outliers is 9,267.79 m\u0026sup3;, while the lower limit is negative.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\sigma\\:=\\sqrt{\\frac{\\sum\\:{（{X}_{i}-{\\mu\\:}）}^{2}}{N}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere σ represents the standard deviation, \u0026micro; denotes the mean value, X stands for the individual grouting volume value, and N represents the number of data points.\u003c/p\u003e \u003cp\u003eUpper limit = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e + 3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eLower limit = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e \u0026minus;\u0026thinsp;3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eUsing the box plot method to analyze outliers, the results indicate that within the evaluation area, the lower quartile (Q1) for the total grouting volume data is 965, the upper quartile (Q3) is 3,878, and the interquartile range (IQR) is 2,913. The upper limit for outliers is 8,247.5, while the lower limit is negative.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Analysis of Grouting Volume Data in the Evaluation Area for the Fourth and Fifth Limestone\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Statistical Table of Grouting Volume Data for Fourth and Fifth Limestone\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Table of Grouting Volume Data for Fourth and Fifth Limestone\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrouting volume\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrouting volume\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNO.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGrouting volume\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10009.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e205.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2204.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3410.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2155.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5204.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1842.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2417.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3083.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1876.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2423.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7273.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2237.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1887.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5374.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e609.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6435.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4940.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2152.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6213.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2262.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4385.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1544.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3933.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4491.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3009.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3035.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1729.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e658.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2926.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2509.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1375.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3895.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2243.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7536.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e673.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1285.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3840.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e121.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2331.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3292.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1853.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2231.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2253.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6729.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e421.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2205.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e490.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2148.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4623.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3836.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1033.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4183.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e941.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5672.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1772.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1708.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3434.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4037.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e269.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1967.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2203.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3881.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1932.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3398.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e459.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1837.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3877.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8944.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e228.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2187.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2377.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e321.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4966.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2208.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3004.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4032.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e622.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1921.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1951.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2630.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1793.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3852.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e973.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e295.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4002.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9613.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6363.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3337.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4780.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7026.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1069.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e755.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the results of statistical analysis using the standard deviation method and the box plot method on the grouting volume data in the evaluation area, where the data itself pertains to grouting reinforcement, higher grouting volume values indicate anomalies (or structural control by the strata). The median of the overall data is 2206.5, the upper quartile \u003cem\u003eQ3\u003c/em\u003e is 3878, and grouting points with values greater than 3878 account for 25% of the total. Therefore, according to the above analysis, the upper limit of the grouting volume anomaly value can be set at the third quartile (\u003cem\u003eQ3\u003c/em\u003e) for evaluation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e5.1.4 Statistical Analysis of Final Grouting Pressure in the Fourth and Fifth Limestone Layers\u003c/h2\u003e \u003cp\u003eBased on the standard deviation method for analyzing outliers, the results show that the overall grouting pressure value in the evaluation area is 961.10, with a mean value of 9.24, an overall standard deviation of 1.55, and a sample standard deviation of 1.54. The upper limit for outliers is 13.88, and the lower limit for outliers is 4.59.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\sigma\\:=\\sqrt{\\frac{\\sum\\:{（{X}_{i}-{\\mu\\:}）}^{2}}{N}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere σ represents the standard deviation, \u0026micro; denotes the mean value, X stands for the individual grouting volume value, and N represents the number of data points.\u003c/p\u003e \u003cp\u003eUpper limit = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e + 3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eLower limit = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e \u0026minus;\u0026thinsp;3\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eBased on the box plot method for analyzing outliers, the results show that the overall grouting pressure data in the evaluation area has a lower quartile \u003cem\u003eQ1\u003c/em\u003e of 8.4, an upper quartile \u003cem\u003eQ3\u003c/em\u003e of 10, and an interquartile range (\u003cem\u003eIQR\u003c/em\u003e) of 1.6. The upper limit for outliers is 12.4, and the lower limit for outliers is 6.0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Summary of Grouting Pressure Data for the Fourth and Fifth Limestone Aquifers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrouting pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrouting pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGrouting pressure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. illustrates the analysis of grouting pressure in the research area for the fourth and fifth limestone aquifers\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Statistical Summary of Grouting Pressure Data for the Fourth and Fifth Limestone Aquifers\u003c/p\u003e \u003cp\u003eBased on the statistical analysis using the standard deviation method and the box plot method for the ground grouting pressure data in the evaluation area, where lower grouting pressure values indicate anomalies (or structural control), it is observed that the median of the overall data is 9.0, the lower quartile Q1 is 8.4. The percentage of grouting points with pressure values less than 8.4 is 3.8%. Therefore, based on the above analysis, the lower quartile (Q1) value of 8.4 is selected as the lower limit for evaluating abnormal grouting pressure values. Additionally, considering the grouting pressure design parameter being 2 to 2.5 times the water pressure and practical data, a comprehensive determination of the lower limit for abnormal values can be set at 8.0 for evaluation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.2 GIS-Based Grading Evaluation Results\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Evaluation of Grouting Effectiveness in the Xu Limestone Aquifer\u003c/h2\u003e \u003cp\u003eBased on the statistical analysis of grouting volume data from surface directional drilling provided by the mining company, and using the natural break method in the GIS system for grading analysis (Sun et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as well as considering the results of statistical analysis of grouting data, the study area was divided into three grades: stable zone (grouting volume\u0026thinsp;\u0026lt;\u0026thinsp;579 t), transition zone (579 t\u0026thinsp;\u0026le;\u0026thinsp;grouting volume\u0026thinsp;\u0026lt;\u0026thinsp;1364 t), and anomaly area (grouting volume\u0026thinsp;\u0026ge;\u0026thinsp;1364 t); as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. Thematic Map of Grouting Volume Distribution in the Xu Limestone Aquifer\u003c/p\u003e \u003cp\u003eBased on the grouting data of the Xu limestone aquifer, the grouting pressure of most grouting sections has reached 8 MPa, while a small portion of the grouting sections have not reached 10 MPa. Based on past grouting experience and the relatively high mineralization degree of the Xu aquifer, it is analyzed and judged that there may be concealed structural connections between the fourth and fifth limestone aquifers of the 11th coal seam roof and the underlying Xu and Ao limestone aquifers. Combining this with the results of statistical analysis of the data, this study sets the evaluation criteria for the grouting effect of the Xu limestone aquifer, with the grouting pressure not less than 8.0 MPa considered acceptable. Therefore, based on the completed grouting data of the Xu limestone aquifer and the observation of water stress center displacement data from pumping test observation wells, the grouting effect of the Xu limestone aquifer can be classified into three levels: stable zone (pressure\u0026thinsp;\u0026ge;\u0026thinsp;10 MPa), transitional zone (8.0 MPa\u0026thinsp;\u0026le;\u0026thinsp;pressure\u0026thinsp;\u0026lt;\u0026thinsp;10 MPa), and anomaly zone (pressure\u0026thinsp;\u0026lt;\u0026thinsp;8.0 MPa). This classification is illustrated in the single-item evaluation map of grouting effects in the Xu limestone aquifer (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e. Distribution thematic map of grouting pressure in the Xu limestone aquifer\u003c/p\u003e \u003cp\u003eBased on the grouting volume and grouting pressure of the Xu limestone aquifer in the study area, a comprehensive evaluation is conducted. Using the GIS system, grouting pressure evaluation is weighted at 0.6, while grouting volume evaluation is weighted at 0.4 (with a negative correlation) for overlay evaluation.\u003c/p\u003e \u003cp\u003eFinally, based on the natural classification method in GIS, the effectiveness of the ground directional horizontal grouting treatment in the Xu limestone aquifer area is categorized into three levels: standards-compliant zone, qualified zone, and weak zone, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e. depicts the evaluation of the grouting treatment effectiveness in the Xu limestone aquifer\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Evaluation of Grouting Effectiveness in the Fourth and Fifth Limestone Aquifers\u003c/h2\u003e \u003cp\u003eBased on the grouting data of the fourth and fifth limestone aquifers in the roof of coal seam 11, most of the grouting sections have reached a final pressure of 8 MPa. However, there are still small sections where the final pressure has not reached 10 MPa. According to previous grouting experiences and the high degree of mineralization in the fourth and fifth limestone aquifers, an analysis suggests the possibility of hidden structural conduits between the fourth and fifth limestone aquifers and the underlying Xu limestone and Ao limestone aquifers. Combining these factors with the results of statistical analysis, the evaluation of grouting effectiveness is set with a criterion that the final pressure should not be less than 8.0 MPa, indicating acceptable grouting effectiveness. Therefore, based on the completed grouting hole data for the roof of coal seam 11 and observations of water stress center displacement from the pumping test in the fourth and fifth limestone aquifers, the grouting effect can be classified into three levels: stable zone (final pressure\u0026thinsp;\u0026ge;\u0026thinsp;10 MPa), transition zone (8.0 MPa\u0026thinsp;\u0026le;\u0026thinsp;final pressure\u0026thinsp;\u0026lt;\u0026thinsp;10 MPa), and anomaly zone (final pressure\u0026thinsp;\u0026lt;\u0026thinsp;8.0 MPa), as illustrated in the single-factor evaluation map of grouting effectiveness for the roof of coal seam 11 in the fourth and fifth limestone aquifers (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e. Distribution thematic map of grouting final pressure in the fourth and fifth limestone aquifers\u003c/p\u003e \u003cp\u003eBased on statistical analysis of grouting volume data from surface directional drilling provided by the mining company, the research area of the fourth and fifth limestone aquifers was categorized into three levels using the natural breakpoints method within the GIS system. This classification was further informed by the statistical analysis of grouting data. The areas were classified as stable (grouting volume\u0026thinsp;\u0026lt;\u0026thinsp;2037 t), transitional (2037 t\u0026thinsp;\u0026le;\u0026thinsp;grouting volume\u0026thinsp;\u0026lt;\u0026thinsp;3871 t), and anomalous (grouting volume\u0026thinsp;\u0026ge;\u0026thinsp;3871 t). The grouting volume was found to be negatively correlated with the evaluation results, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e. depicts the thematic distribution of grouting volume in the fourth and fifth limestone aquifers\u003c/p\u003e \u003cp\u003eBased on the comprehensive evaluation of grouting volume and grouting pressure in the top plate fourth and fifth limestone aquifers, a weighted evaluation is conducted using a GIS system, with a weight of 0.6 for grouting pressure evaluation and 0.4 for grouting volume evaluation (negatively correlated).\u003c/p\u003e \u003cp\u003eFinally, based on the natural grading method in GIS, the ground directional horizontal grouting effect in the top plate fourth and fifth limestone aquifers is classified into three levels: standard area, qualified area, and weak area, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e. illustrates the evaluation of the grouting consolidation effect in the fourth and fifth limestone aquifers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Evaluation Mechanism for Advanced Area Treatment of Surface-directed Multi-branch Horizontal Wells\u003c/h2\u003e \u003cp\u003eIn response to the characteristics of advanced treatment of coal mine water hazards using ground directional multi-branch horizontal wells, which can effectively address the lack of underground treatment in areas of delayed or restricted mining, but with grouting effects that cannot be verified underground (according to regulations, area treatment must be completed before conversion to a mineable area), this study proposes a scientific evaluation mechanism.\u003c/p\u003e \u003cp\u003eFirstly, based on surface boreholes or directional horizontal boreholes, the permeability coefficient and sealing capacity of the water barrier layer after grouting are evaluated to conduct the initial qualitative analysis of grouting effects. Secondly, statistical analysis is performed on the surface grouting renovation data (grouting volume, grouting pressure), providing evaluation thresholds for each indicator. Thirdly, GIS-based modeling is utilized to process the data, generating maps for grouting effect evaluation. Fourthly, intensive borehole exploration and supplementary grouting renovation are conducted underground in the anomaly areas identified by the evaluation system. Finally, after all areas in the study region have been evaluated as qualified, trial mining can be conducted in the working face.\u003c/p\u003e \u003cp\u003eThis evaluation mechanism integrates closely with various aspects of mining practices, providing scientifically effective evaluation results to guide safe mine production. It has been validated in multiple mines in the East China region, showing significant effectiveness. This study also proposes, for the first time, the \"Evaluation Method for the Effectiveness of Advanced Area Treatment of Ground Directional Multi-Branch Horizontal Wells,\" as the \"Technical Specification for Advanced Area Treatment of Coal Seam Bottom Limestone Aquifers\" has only been implemented since early 2024, and effective evaluation methods are not yet available.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study presents a systematic evaluation framework for assessing the effectiveness of advanced regional treatment of limestone aquifers in deep coal seam mining. The methodology integrates statistical analysis, GIS-based spatial modeling, and a multi-factor assessment system, providing a quantitative and reproducible approach to evaluating grouting effectiveness.\u003c/p\u003e \u003cp\u003e(1) Key Findings and Contributions\u003c/p\u003e \u003cp\u003e1) A multi-factor evaluation model was developed, combining grouting pressure, volume, and spatial permeability analysis, ensuring a more comprehensive assessment compared to traditional empirical methods.\u003c/p\u003e \u003cp\u003e2) The GIS-based spatial analysis successfully identified high-risk zones where water inrush remains a concern, enabling targeted reinforcement strategies.\u003c/p\u003e \u003cp\u003e3) Field validation demonstrated a 95.6% agreement between the GIS-predicted weak zones and actual water inrush-prone areas, confirming the reliability of the proposed framework.\u003c/p\u003e \u003cp\u003e4) The proposed methodology significantly improves the accuracy and efficiency of water hazard governance, offering a practical decision-support tool for deep coal seam mining operations.\u003c/p\u003e \u003cp\u003e(2) Engineering Implications\u003c/p\u003e \u003cp\u003eThe results provide critical insights into optimizing grouting-based water hazard control strategies in deep mining environments. By integrating statistical and spatial analysis, this study offers a quantitative and systematic approach for reducing water inrush risks, improving mining safety, and optimizing resource utilization. These findings are particularly valuable for coal mines with complex hydrogeological conditions, where precise water hazard assessment is essential for long-term sustainability.\u003c/p\u003e \u003cp\u003eOverall, this study provides a scientific and practical foundation for advanced regional treatment evaluation in deep coal seam mining, contributing to the ongoing development of intelligent and sustainable mine water hazard management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCRediT authorship contribution statement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYanbo Hu:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing, Software, Writing \u0026ndash; original draft, Visualization, Investigation, Validation, Methodology, Formal analysis, Data curation, Conceptualization. \u003cstrong\u003eJingzheng Jiang:\u0026nbsp;\u003c/strong\u003eFormal analysis, Data curation. \u003cstrong\u003eGang Zheng:\u003c/strong\u003e Investigation, Data curation, Conceptualization.\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Jiangsu Province under Grant BK20221319 and the China Postdoctoral Science Foundation under Grant 2024M750682. Thank you for the funding from the Institute of Coal Chemical Industry Technology China Energy Group Ningxia Coal Industry Co., Ltd.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDong, S., Zhang, W., Zhou, W. et al., 2021. Discussion on some topical issues of water prevention and control in coal mines. Mine Water Environ. 40, 547\u0026ndash;552. https://doi.org/10.1007/s10230-021-00773-3.\u003c/li\u003e\n\u003cli\u003eHe, M.C., Wu,Y.Y., Gao,Y.B., et al., 2024. Research progress of rock mechanics in deep mining. J. China Coal Soc. 49 (01), 75-99. https://doi.org/10.13225/j.cnki.jccs.2023.1400.\u003c/li\u003e\n\u003cli\u003eHu, W., Zhao, C., 2021. Evolution of water hazard control technology in china\u0026rsquo;s coal mines. Mine Water Environ. 40, 334\u0026ndash;344. https://doi.org/10.1007/s10230-020-00744-0.\u003c/li\u003e\n\u003cli\u003eHu, Y.B., Li, W.P., Chen, X.M., Xu, H.Z., Liu, S.L., 2022. 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Modern Mining 39 (02), 211-214.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"mine-water-and-the-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mwen","sideBox":"Learn more about [Mine Water and the Environment](http://link.springer.com/journal/10230)","snPcode":"10230","submissionUrl":"https://www.editorialmanager.com/mwen/default2.aspx","title":"Mine Water and the Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"water damage control, deep mining, GIS, treatment effect","lastPublishedDoi":"10.21203/rs.3.rs-6015722/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6015722/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes a novel and systematic evaluation method for assessing the effectiveness of advanced regional treatment of limestone aquifers in deep coal seam mining. The lack of an effective evaluation system following the implementation of the Technical Specification for Advanced Regional Treatment of Limestone Aquifers under Coal Seams (2024) has hindered the optimization of water hazard control strategies. To address this, we develop a comprehensive multi-factor evaluation model that integrates GIS-based spatial analysis, statistical modeling of grouting data, and aquifer water-blocking capacity assessment. A key advancement of this study is the integration of surface-directed multi-branch horizontal well data with GIS zoning methodologies, allowing for a more precise and quantitative assessment of grouting effectiveness. Field validation in multiple mining areas demonstrates that this approach significantly enhances the reliability of water hazard governance, providing a scientific and practical tool for safe and efficient deep coal seam mining.\u003c/p\u003e","manuscriptTitle":"GIS-Based Evaluation of Advanced Regional Treatment of Limestone Aquifers in Deep Coal Seam Mining","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-28 14:07:45","doi":"10.21203/rs.3.rs-6015722/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-03-20T01:39:57+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-19T17:34:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-13T15:00:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Mine Water and the Environment","date":"2025-02-12T08:42:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"mine-water-and-the-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mwen","sideBox":"Learn more about [Mine Water and the Environment](http://link.springer.com/journal/10230)","snPcode":"10230","submissionUrl":"https://www.editorialmanager.com/mwen/default2.aspx","title":"Mine Water and the Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"130295c4-f9b1-427c-ae17-608fb6b055de","owner":[],"postedDate":"March 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:08:39+00:00","versionOfRecord":{"articleIdentity":"rs-6015722","link":"https://doi.org/10.1007/s10230-026-01109-9","journal":{"identity":"mine-water-and-the-environment","isVorOnly":false,"title":"Mine Water and the Environment"},"publishedOn":"2026-02-27 15:59:13","publishedOnDateReadable":"February 27th, 2026"},"versionCreatedAt":"2025-03-28 14:07:45","video":"","vorDoi":"10.1007/s10230-026-01109-9","vorDoiUrl":"https://doi.org/10.1007/s10230-026-01109-9","workflowStages":[]},"version":"v1","identity":"rs-6015722","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6015722","identity":"rs-6015722","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}