GIS-based water inrush risk evaluation of the 10th coal seam floor in Zhuxianzhuang Coal Mine, northern Anhui Province, China

China is the world's largest producer and consumer of coal (Yan et al. 2021). Although China has been actively promoting the development of clean energy, coal will still be the main energy source in China in the coming decades, playing an important role (Saini et al. 2022). Currently, China's total coal reserves are quite rich, and North China is an important part of China's coal mining industry. With the increasing depth of coal mining resources, the coalfields in North China have almost fully entered the deep mining stage. Under the many conditions generated by deep mining, the damage to the coal bed floor is increasing day by day, and the impact on coal bed floor water inrush on the safe mining of coal has become an urgent problem to be solved (Mao et al. 2018; Zeng 2018; Liu et al. 2020).

The control factors affecting the floor water inrush accidents of coal seams are often complex and diverse but are mainly determined by hydrogeological conditions and geological formations (Odintsev & Miletenko 2015). This challenge has received attention and achieved remarkable results from many mining experts and scholars (Singh 2015; Wu et al. 2017; Khan & Jhamnani 2023), which has promoted the progress and development of mine water damage prevention and control. In the 1960s, several scholars from Hungary and the former Soviet Union and other countries took the thickness and strength of the coal seam floor as the influencing factors of water inrush at the coal seam floor based on hydrostatic theory (Galsa et al. 2022). In 1989, Santos and Bieniawski introduced the concept of the critical energy release point, improved the Hoek–Brown rock body strength criterion, and studied the bearing capacity of rock strata at the base of coal seam mining (Mendecki et al. 2019). On this basis, Chinese experts and scholars have put forward many theories, such as the theory of the next three zones, the theory of the key layer, the theory of strong seepage, and the theory of ‘rock–stress relationship’ (Hu et al. 2019; Zhang et al. 2023). Research scholars not only start from the mechanism of the floor water burst but also apply the physical detection method to the mine water burst evaluation (Wang et al. 2022a; Yu et al. 2022). Among them, detection instruments such as the SEAMEX-85 trough wave seismometer from Germany and the Petro-Sonde geophone from the United States are widely used. Evaluation of the danger of water inrush at the floor is the key to preventing and controlling water at the coal seam floors. Currently, the water-surge coefficient method is widely used (Shi et al. 2019), which is simple and easy to use. While the water emergence coefficient method only considers two control factors, aquifer thickness and aquifer water pressure, the influencing factors of water emergence on the floor are complicated. Therefore, the evaluation accuracy of the water emergence coefficient method is obviously lower. In addition, the zonal evaluation methods of water-surge hazard have been widely used (Li et al., 2022b; Wang et al., 2022b), such as artificial neural network (Liu & Han 2023) and random forest (Zhao et al. 2018) methods coupled with the geographic information system (GIS) to establish the evaluation of water-surge hazard of the coal seam floor model, which provides a new idea for coal water control research.

This study utilizes the latest hydrogeological data accumulated in recent years from the third mining area of the Zhuxianzhuang Coal Mine in northern Anhui, China; establishes a comprehensive judgment model of the danger of water inrush in the floor of the mine for aquifer, water barrier, geological structure, and other multi-source controlling factors; and calculates the comprehensive weights by adding and integrating the subjective and objective weights obtained by the analytic hierarchy process (AHP) and entropy weighting method. Using GIS spatial data processing function, effective mining blocks are divided in detail through comprehensive superposition of various main control factors. The proposed GIS-based comprehensive discrimination division technology of effective mining blocks of coal resources provides an important reference basis for the prevention and control of water inrush in the 10th coal seam floor in the third mining area of the Zhuxianzhuang Coal Mine.

The research object of this paper is the 10th coal seam in the third mining area in the south of the mine, mainly distributed in the three levels (−700 to −1,100 m). The thickness of recoverable coal is 1.00–6.42 m, with an average of 2.65 m. The Taiyuan Formation limestone karst aquifer is a karst fissure aquifer, with obvious vertical zoning law. Its recharge is mainly interlayer, and in the shallow outcrop area, it is complementary to the Ordovician limestone karst water and the fourth aquifer water. The limestone in the outcrop and shallow karst fissure cave development gradually weakened as it got deeper. There is a 50–60 m mudstone water separator between the ash and the 10th coal seam, and under normal circumstances, there is no direct water-filling relationship between the Taiyuan Formation limestone karst water and the mining of the 10th coal seam. However, due to the influence of faults, the distance between them becomes smaller, and the Taiyuan Formation limestone karst water becomes a direct hidden danger to the safe mining of the 10th coal seam.

Water sources and water-conducting channels are the necessary conditions for the occurrence of water inrush accidents in mines. Fault fissures and water-conducting fissure zones in the floor under the effect of mining are the main water-conducting channels, and the faults expand under the joint action of mining stress and water pressure. In the vicinity of the fault, more secondary fissures occur, resulting in a significant increase in the development height of the water-conducting fissure zone (Xiao et al. 2022). After conduction through the floor aquifer, water inrush accidents can easily occur and directly affect the working seam face.

Reasonable selection of the main control factors of coal bed water inrush plays a key role in the accuracy of the evaluation results (Liu et al. 2021b). Due to the complexity of the mechanism of water inrush at the bottom of coal seams and the many factors influencing the control, it is necessary to analyze the water inrush factors by combining the actual production experience of the mine and its own geological characteristics. According to the geological and hydrological report data of the mining area, combined with relevant literature and research results, it is generally believed that the aquifer, aquiclude, geological structure, etc., are the key factors for the occurrence of water inrush in the coal seam baseplate, and aquifer water pressure, aquifer water richness, aquifer thickness, aquiclude thickness, core recovery percentage, brittle–plastic rock thickness ratio, fault fractal dimension, and other indicators can fully describe it.

In this paper, data from 49 drill holes in of Zhuxianzhuang's third mining areas are selected to evaluate and analyze the risk of water inrush in the 10th coal seam floor. Among them, data on water pressure of the aquifer, water richness of the aquifer, thickness of the aquifer, thickness of the water barrier, the core take rate, and thickness ratio of brittle and plastic rocks was obtained directly from the geological survey report and drilling data by simple calculations. The fault dimension was programmed by AutoLISP language to carry out statistics with AutoCAD plug-in. In the AutoCAD platform, only the fault trace layer was opened and the completed AutoCAD plug-in was imported. The boundary was set as a square area of 5,000 × 5,000 so that the study area was within the set block. The number of sections was set to 10 and then the arithmetic data was studied. The number of grids N(r) corresponding to r = 1/2, r = 1/4, r = 1/8, and r = 1/16 at each scale was imported into an Excel sheet for regression calculations, and finally the optimal slope of the regression straight line, i.e., the value of the subdimension, was calculated.

Water richness indicates the aquifer water storage size and the ability to release water. Water richness and its karst fissure development, runoff conditions, tectonic development, burial depth, and other factors are related. Aquifer water richness reflects the amount of water in the aquifer, and the stronger the aquifer water richness, the greater the risk of water outbursts (Wang et al. 2023). This time, the water richness of the mine area is depicted by the standard drilling unit water influx with a borehole caliber of 91 mm and a pumping level drop depth of 10 m as a guideline. As can be seen from Figure 3(b), the aquifer in the southwestern side of the study area is more water-rich.

The existence of the bottom slab pressurized aquifer is the precondition for the occurrence of bottom slab water inrush (Zhan et al. 2023). Due to the different final hole locations of the borehole data, the distance from the top of the first limestone to the bottom of the fourth limestone is chosen as the criterion for judging the thickness of the aquifer. The sandstone layer is the basis on which groundwater exists, and it is generally believed that the greater the thickness of the sandstone layer, especially medium and coarse sandstone, the better the water-rich nature. The sandstone layer is the basis on which groundwater exists, and it is generally believed that the greater the thickness of the sandstone layer, especially medium and coarse sandstone, the better the water-rich nature. Therefore, the cumulative thickness of sandstone in the aquifer is one of the factors to characterize the water richness of coal sandstone aquifer. Aquifers with a thickness of less than 40 m were distributed in the northern and south-central parts of the study area (Figure 3(c)).

The water barrier between the coal seam floor and the water-bearing layer has an inhibiting effect on the water inrush of the coal seam floor, and its ability to prevent water inrush is mainly related to the thickness and strength of the water barrier and the combination of lithology. The greater the thickness of the water barrier, the stronger the ability to resist water. General mudstone and sandy mudstone fissures are not developed and have water resistance. Therefore, the cumulative thickness of mudstone in the water barrier characterizes the inhibition of sandstone fissure water. The thickness of the water barrier in the northeastern region of the study area is relatively large (Figure 3(d)).

Core take rate refers to the ratio of core length to return footage, which, to a certain extent, can reflect the degree of fissure development in the formation. In general, the larger the core take rate, the higher the integrity of the rock formation, and the fissures are relatively undeveloped. The smaller the core take rate, the higher the degree of fissure development in the rock formation and the poorer the integrity of the rock formation. The distribution of core take rate in this study area is shown in Figure 3(e), and its value is between 60 and 100%. The distribution is relatively complex, and there is no obvious change law.

The thickness ratio of brittle–plastic rock refers to the ratio of the thickness of water-rich sandstone to the thickness of mudstone (siltstone) in the aquifer. The greater the sandstone to mudstone thickness ratio, the thicker the water-rich sandstone aquifer and the stronger the degree of water enrichment. The water-blocking capacity and compressive strength of brittle and plastic rock combinations differ from each other. When the brittle rock is stress damaged, cracks develop and the water permeability changes greatly. Therefore, the permeability performance of the sandstone fissure aquifer is indirectly characterized by the ratio of brittle–plastic rock thickness combination. When the ratio is larger, firm water-rich characteristic is presented, and when the ratio is smaller, weak water-rich characteristic is presented. The thickness of the brittle–plastic rock in the middle and north of the study area is relatively small (Figure 3(f)), indicating that the impermeability of the rock strata in this area is worse, which is not conducive to the safe mining of coal seams.

Fracture structure is one of the main influencing factors of mine water inrush, and the influence of fault distribution needs to be considered according to the geological structure of the study area. The fault dimension value can be used as a quantitative parameter to reflect the complexity of the fracture structure, which is an effective and accurate index to quantitatively evaluate the complexity of the fracture structure. It is more objective and accurate than other indicators (e.g., fault density) and better reflects the complex changes of the tectonic network (Liu et al. 2021a, 2021b). Large fault fractals can easily cause the development of geological structures, resulting in rock fragmentation and strong water conductivity. The larger fault fractal dimensions are mainly located in the eastern boundary of the study area (Figure 3(g)), which is mainly due to the distribution of F21 and F22 faults.

The vulnerability index (VI) method based on the fusion of GIS information is a commonly used method to assess the vulnerability of an area or system. GIS provides effective analysis, management, and visualization tools by integrating geospatial data with other relevant data. The VI method combines different vulnerability indicators with spatial datasets to assess the vulnerability of a study area. In the late 1990s, numerous scholars began to apply the method to coal bed water inrush problems (Li et al. 2022a; Kirlas et al. 2023).

The AHP is a method used for decision analysis (Lv et al. 2022), proposed by Thomas Saaty in the 1970s. Analyzing and comparing the importance of different criteria help people make reasonable choices when facing complex and challenging decisions. The basic principle of the AHP is to decompose a complex decision problem into a hierarchical structure, and then finally arrive at an optimal solution by comparing and assigning weights to the criteria at different levels.

The entropy weight method is an objective assignment method (Zhao & Gu 2023) drawing on the idea of information entropy, which calculates the information entropy of indicators and decides the weight of the indicators according to the relative change degree of the indicators on the system as a whole; the indicators with a large degree of relative change have larger weights. The entropy weighting method tries to find the best weights according to the objective and real data using the principle of difference driving. It strives to truly reflect the information contained in the indicator data, and the assignment process has a high degree of credibility. Therefore, the entropy weight method can be utilized to calculate the weight of each indicator according to the degree of variation of each indicator, which provides the basis for the comprehensive evaluation of multiple indicators.

The AHP and entropy weight method are used to obtain the comprehensive weight by using the additive integration method, which considers the influence of subjective opinions of experts and objective data on decision-making. Decision-making problems are often faced with uncertainty and ambiguity, and the AHP allows decision-makers to take uncertainty into account in comparisons and evaluations. The entropy weighting method reduces the dependence on subjective judgment and considers uncertainty more objectively (Wu et al. 2022; Xie et al. 2022). The combination of the two methods to find the comprehensive weight makes the decision-making results more accurate and reliable, and improves the scientific and rationality of decision-making.

In this paper, we use GIS to quantify the graphical information and establish a hierarchical analysis model and entropy weighting structure to comprehensively determine the influence weights of each factor. Based on this, using the spatial assignment superposition tool of GIS, the influence factors are superimposed according to the influence weights, and the results of the comprehensive influence of each factor are visualized in a graphical form.

According to the above hierarchical structure analysis model, on the basis of the two major principles of objectivity and the specificity of the evaluation subject, each evaluation parameter of the same level is compared with a two-by-two approach with respect to the importance of the previous level (Table 1). The relative weight (W) of each evaluation parameter can be calculated using the four-level scale to form the judgment matrix and solving the largest characteristic root of the matrix and its corresponding eigenvector (Miao et al. 2019).

Based on the experience of previous mine water inrush accidents, experts believe that the coal seam floor aquifer (B1) and aquiclude (B2) contribute more to the water inrush, while the contribution of geological structure B3 to the water inrush is relatively small. The experts scored each factor according to its role in water inrush accidents, constituting the judgment matrix for AHP evaluation of water inrush at the 10th coal seam floor in the study area (Tables 2–4).

According to the AHP, the judgment matrix is considered to have satisfactory consistency if consistency ratio (CR) < 0.10 (Wei et al. 2021). Otherwise, the judgment matrix needs to be adjusted to have satisfactory consistency. The consistency test of the above discrimination matrix is shown in Table 5, and all requirements are met. Taking the weight vectors of each level and calculating them step by step along the hierarchical model, the weight of each indicator on the total goal can be established.

Table 5

Judgment matrix consistency test index

.	.	CI .	CR .
(i = 1,2,3)	3	0	0
(i = 1,2,3)	3.0355	0.0178	0.0307
(i = 4,5,6)	3.0534	0.0267	0.0460

.	.	CI .	CR .
(i = 1,2,3)	3	0	0
(i = 1,2,3)	3.0355	0.0178	0.0307
(i = 4,5,6)	3.0534	0.0267	0.0460

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The weight of each indicator to the total target, i.e., the result of the weight of each indicator at the decision level to the target level A via the criterion level, is shown in Table 6. The subjective weights of each main control factor (C1–C7) calculated by the AHP were finally obtained as 0.2277, 0.0703, 0.1464, 0.1464, 0.2277, 0.0703, and 0.1111, respectively.

To reduce the influence of subjective weights on the evaluation results, the entropy weight method is used to determine the objective weights of each indicator. Generally speaking, the smaller the information entropy of an indicator, the greater the degree of variability of its value, and the greater the amount of information it carries, the greater the weight. In this paper, the entropy weight method is introduced to calculate the objective weights to avoid the bias caused by human factors. The calculation process is described as follows:

• (1)
  Establish the judgment matrix with sample number m and evaluation index n:

  

  (1)

In Equation (1), X is the judgment indicator matrix; is the corresponding indicator element of row i and column j in the judgment indicator matrix.

• (2)
  Normalize the judgment matrix to form a new matrix:

  

  (2)

In Equation (2), B is the new matrix after normalizing the judgment matrix, is the corresponding indicator element of row i and column j in the normalized matrix, and the normalization formula is shown in Equation (7).

• (3)
  The normalized matrix is homotrended to obtain a new matrix:

  

  (3)

In Equation (3), B′ is the new matrix obtained after homotrending the normalized matrix; is the indicator element corresponding to row i and column j in the homotrended matrix.

• (4)
  According to the definition of information entropy, the entropy weight of the evaluation index is calculated as (Yang et al. 2023):

  

  (4)

In Equation (4), is the value of the corresponding transition variable of j evaluation index in the process of entropy weight calculation; is the transition variable in the process of entropy weight calculation.

• (5)
  Calculate the entropy weights of evaluation indexes and obtain the vector of information entropy weight:

  

  (5)

⁠

The information entropy values, entropy weights, and weights of seven main control factors corresponding to the risk of water inrush in the 10th coal seam floor in the third mining area of the Zhuxianzhuang Coal Mine are shown in Table 7. The objective weights of each main control factor (C1–C7) calculated by the entropy weight method are 0.1669, 0.0674, 0.1569, 0.1482, 0.2511, 0.1545, and 0.0548, respectively.

Table 7

Result of weight calculation by entropy weight method

Controlling factor .	Information entropy weight .	Entropy weight .	Weight W .
Aquifer water pressure (C1)	0.7034	0.2966	0.1669
Aquifer water richness (C2)	0.8802	0.1198	0.0674
Aquifer thickness (C3)	0.7211	0.2789	0.1569
Aquiclude thickness (C4)	0.7366	0.2634	0.1482
Core recovery percentage (C5)	0.5538	0.4462	0.2511
Brittle-plastic rock thickness ratio (C6)	0.7255	0.2745	0.1545
Fault fractal dimension (C7)	0.9026	0.0974	0.0548

Controlling factor .	Information entropy weight .	Entropy weight .	Weight W .
Aquifer water pressure (C1)	0.7034	0.2966	0.1669
Aquifer water richness (C2)	0.8802	0.1198	0.0674
Aquifer thickness (C3)	0.7211	0.2789	0.1569
Aquiclude thickness (C4)	0.7366	0.2634	0.1482
Core recovery percentage (C5)	0.5538	0.4462	0.2511
Brittle-plastic rock thickness ratio (C6)	0.7255	0.2745	0.1545
Fault fractal dimension (C7)	0.9026	0.0974	0.0548

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where is the data after normalization, is the original data before normalization, and are the maximum and minimum values of the quantitative values of each master control factor, respectively, and n is the number of samples (Zhang et al. 2022).

In terms of obtaining weights, a single subjective factor evaluation weight or objective factor evaluation weight is one-sided, and so combining the two evaluation methods for evaluation weights becomes particularly crucial to the accuracy and rigor of the results. While analyzing the internal laws and characteristics of the data, a more professional and reliable comprehensive weight is obtained by macro-adjustment of each factor based on previous expert experience. From the comprehensive weights obtained by the additive integration method, it is known that the water pressure of the aquifer and the adoption rate of the rock core have a greater impact on the water breakout of the coal seam bottom plate; therefore, the vulnerable area in the evaluation results of the VI is characterized by a strong water-rich nature and the rock core of the water trap is relatively broken.

In terms of evaluation methods, most of the coal production units nowadays take the traditional method of water inrush coefficient as the main method for safe mining of coal seams. However, the only factors considered in the method are aquifer thickness and aquifer water pressure, and this method is only applicable to the evaluation of the study area with a simple geological structure. For the study area with complex geological structure conditions, the evaluation results of the water inrush coefficient method are not comprehensive. The VI method takes more main control factors into consideration, and the normalized data are applied to the spatial interpolation analysis function of the GIS to obtain the thematic maps of the main control factors, which can intuitively observe the change trend of the influence of the main control factors in the spatial location, and the spatial superposition generates the more detailed evaluation results of the VI method.

This paper is scientific and reliable, but there are certain limitations at the same time: due to the limitation of the data, the current hydrogeological survey data do not have the relevant water inrush point data, so it is not possible to further verify the accuracy of the method. At present, there is no survey data revealing the characteristics of the groundwater dynamic environment in the study area, and the dynamic evolution of groundwater is complicated, so it is not possible to control the spatial change rule of groundwater in an all-round way. The 10th coal seam in the third mining area of the Zhuxianzhuang Coal Mine is a coal seam to be mined, which leads to the evaluation of the VI method based on the original static condition that the mine has not been mined yet. In fact, the groundwater and geological stress conditions are always changing with the influence of mining, which leads to the destruction of the bottom plate and the main control factors of water breakout becoming more complicated. Therefore, the static quantitative analysis model of coal seam water inrush risk constructed in this paper only has certain reference value for realizing mine safety production, and it still needs further exploration and research to obtain more realistic risk assessment results in the study area.

• (1)

  According to the hydrogeological investigation data of the study area, seven indicators in three features – aquifer, water barrier, and geological structure – were selected as the main control factors for evaluating the danger of the 10th coal seam floor by comprehensively considering the hydrogeological conditions and the structure and strength of the mine floor. The study determines the weights of the main control factors affecting the risk of water inrush in the 10th coal seam floor in the third mining area of the Zhuxianzhuang Coal Mine through the subjective–objective comprehensive weight analysis method combining the AHP and the entropy weight method, and adopts the comprehensive weighting method of the addition and integration method. The analysis shows that aquifer water pressure and core take rate are the key controlling factors for water breakout in the study area.

• (2)

  From the obtained comprehensive weights, combined with the spatial data analysis function of the GIS, the VI method of coal bed floor water inrush hazard evaluation was used to obtain more scientific results of the zoning evaluation of the water inrush hazard of the 10th coal bed floor in the third mining area. The floor of the working face is divided into five evaluation zones: safe zone, safer zone, transition zone, more vulnerable zone, and vulnerable zone. The overall danger of the study area gradually increases from north to east. The vulnerable zone is mainly located in the eastern part of the study area, which is highly water-rich and provides a good source of water for water surges. In addition, the existence of large faults and broken cores in this area make it easy to form a natural channel for water surges. The evaluation model of mine floor water inrush is based on the original static conditions before mining; however, the groundwater and geological stress conditions are always changing in mining, and so this study has a certain reference value for the safe mining of the 10th coal seam in the third mining area of the Zhuxianzhuang Coal Mine.

We sincerely thank the editors and reviewers for their valuable comments that greatly improved this paper. In addition, we thank QL for financial support.

This work was financially supported by the Natural Science Foundation of Anhui Province under grant number 1908085ME145.

QJ performed software simulations, analyzed and interpreted data, and wrote the paper. QL conceived and designed the study and provided grant funding. HC supervised and revised the paper. XH collected and contributed experimental data.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.