Investigation on the Dynamic Evolution and Evaluation of Floor Microseismic Responses during Extra-Thick Coal Seam Mining

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When a strong aquifer exists beneath the coal seam and the effective aquiclude is weak, the risk of floor water inrush increases significantly. In recent years, microseismic (MS) monitoring technology has been widely applied, and using MS monitoring to assess floor water inrush risk has proven to be a reliable and effective approach. To address the high-risk problem of floor water inrush during the extraction of extremely thick coal seams, this study employed MS monitoring to investigate the evolution characteristics of floor MS activity and its associated water inrush risk. The MS response characteristics induced by thick seam mining were analyzed, and a floor water inrush risk evaluation model was established based on the Analytic Hierarchy Process–Entropy Weight Method (AHP–EWM). The evaluation results were consistent with field monitoring data. The results indicate that most floor MS events in the 61607 working face of the Longwanggou Coal Mine were concentrated in the Taiyuan Formation. The MS monitoring indicators, such as the extent of the advanced influence zone, floor failure depth, and total daily released energy, showed strong correlations with observed data including water inflow and mine pressure. Key controlling factors of floor water inrush risk were summarized, focusing on the activity, spatiotemporal, and intensity characteristics of floor MS events. A quantitative risk evaluation system for floor water inrush during extremely thick seam mining was developed using the AHP–EWM objective–subjective weighting method, demonstrating good agreement with field observations. This study provides theoretical and data support for analyzing the dynamic process and influencing factors of floor damage and fracture evolution under thick seam mining. It offers a new approach for predicting and forecasting water inrush risk, enriches the application of MS monitoring in mine floor water hazard prevention, and holds significant practical importance for promoting safe coal mine production. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Extra-thick coal seam Floor failure Microseismic monitoring Risk evaluation 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 The coal resources in the Jin–Shan–Meng mining area of China are characterized by large reserves and thick coal seams 1 . With the continuous progress of coal mining technology, a high-intensity and high-efficiency mining mode has been gradually established. Compared with conventional thin and medium-thick coal seams, the mining of extra-thick coal seams is associated with higher strata pressure, stronger ground stress effects, and greater disturbance to the floor, often resulting in extensive and deep floor fracturing 2 , 3 . Moreover, the Carboniferous–Permian coal seam floors in China generally overlie Ordovician limestone aquifers with high water pressure. The fractures in the floor can easily connect with the aquifers and trigger water inrush disasters, posing serious threats to mine safety. Floor water hazards caused by high-pressure confined aquifers have become one of the main factors restricting the safe and efficient extraction of coal resources in China 4 , 5 . Therefore, it is necessary to investigate the dynamic failure evolution of the floor during extra-thick coal seam mining. Many scholars have conducted studies on extra-thick coal seam mining and achieved valuable results. Zheng et al. 6 analyzed the water inrush mechanism of the Huangyuchuan Mine during top-coal caving and clarified the coupling mechanism between Ordovician limestone permeable channels and floor failure, proposing a directional long-borehole exploration technique to prevent Ordovician limestone water hazards during mining. Shao 7 systematically analyzed the roof and floor structure and failure characteristics of fully mechanized top-coal caving faces and established an elastic mechanical model of the floor. The study showed that the maximum failure depth of the floor is positively correlated with the face width and surrounding rock stress and negatively correlated with rock compressive strength. Li et al. 8 based on the gas–water two-phase flow similarity theory in fractured rock masses, established a CFDEM numerical model to simulate the fault-induced floor water inrush and roof–floor coupling failure in extra-thick coal seam mining. Their results indicated that the root cause of water inrush lies in the instability of key high-level strata, subsidence of masonry beams, and secondary fracturing of the floor. Wang et al. 9,10,11 used theoretical analysis, numerical simulation, and field measurements to study the stress and fracture evolution of the floor during fully mechanized top-coal caving. Zhang et al. 12,13,14,15 applied distributed optical fiber sensing and cross-hole resistivity CT testing to obtain in situ data on strain and geoelectric field responses during floor failure in extra-thick coal seam mining. Guo 16 analyzed the three-dimensional dynamic evolution of floor disturbance using borehole stress–strain monitoring. Cao et al. 17 derived a mechanical relationship between floor failure depth and coal seam thickness and established a floor water inrush risk evaluation model using interval grey optimal clustering analysis. In addition to floor failure research, Zhu et al. 18 studied the mechanism of floor heave in fully mechanized top-coal caving faces and proposed an underground regional hydraulic fracturing technique for controlling floor heave. Zhang et al. 19,20 investigated the characteristics of roof caving structure and dynamic loading mechanisms, analyzing the strong dynamic pressure phenomena in working faces of extra-thick seams. Kong et al. 21,22 examined the evolution of fractures and seepage characteristics during mining and developed a predictive model for the height of the water-conducting fracture zone. Yu et al. 23 explored the multi-field coupling mechanism of disaster formation and proposed a collaborative control technology. Most existing studies have been based on theoretical analysis, numerical simulation, and field measurements, which have limited continuity and fail to fully represent the dynamic evolution process of floor failure. As a real-time, dynamic, and efficient geophysical monitoring technique, MS observation has been increasingly applied in floor safety studies 24 . Yin et al. 25 used machine learning and deep learning models to analyze MS events and successfully predicted water inrush incidents. Xin et al. 26 applied cumulative and kernel density indicators of MS events to interpret fault activation characteristics in the floor. Jia et al. 27,28 established a water inrush risk evaluation method based on MS events, while Gai et al. 29,30 introduced weighted variation coefficients and floor safety indices to improve the risk evaluation model and zoning accuracy. Xu et al. 31,32 proposed an all-time–space early warning method for floor water inrush using MS monitoring, achieving accurate prediction at different mining stages. Zuo et al. 33,34,35 combined theoretical analysis, MS monitoring, and numerical simulation to identify and predict potential water inrush channels based on floor rock fracture characteristics. Although these studies have explored various aspects of floor water hazard prevention using MS technology and developed related theories and prediction methods, the understanding of floor failure characteristics and evaluation systems under special mining conditions remains incomplete. This is especially true for extra-thick coal seam mining in western China, where the large mining height produces strong and complex disturbances to the floor. Therefore, taking the Longwanggou Coal Mine in the Zhungeer Coalfield as the research area, this study applies MS monitoring technology to analyze the dynamic evolution of floor failure. Based on the AHP–EWM method and combined with measured water inflow data, a quantitative classification and evaluation of floor water inrush risk is performed. The research results can further enhance the application of MS technology in floor water hazard prevention and provide theoretical support for similar mining operations, offering practical significance for improving coal mine safety. 2 Study Area Overview The Longwanggou Coal Mine is located in the central part of the Zhungeer Coalfield, Inner Mongolia, China. It belongs to the Dongsheng Subzone of the Ordos Stratigraphic Subregion within the North China Stratigraphic Region. The main mining seam is the Carboniferous Taiyuan Formation coal seam. The 61607 working face is situated in the northern wing of the 61st panel district. The western side of the panel is bounded by intact coal and rock strata, while the eastern side is adjacent to the goaf area, as shown in Fig. 1 . The working face has a strike length of 1,276.2 m, an inclined length of 255 m, an average mining thickness of 21.6 m, and a burial depth ranging from 280.4 to 426.8 m, with an average dip angle of 3° . The lithological profile of the roof and floor strata of the 61607 working face is shown in Fig. 2 . The main aquifer in the floor is the karst fissure aquifer of the Ordovician limestone. The principal water-bearing section is the Middle Ordovician Majiagou Formation, whose upper part consists of light gray argillaceous limestone and thick-bedded limestone interbedded with thin layers of gray limestone. The lower part is composed of dolomitic limestone and thick-bedded dolomite, with well-developed dissolution fissures, cavities, and pores. The unit water inflow varies between 0.024 and 34.321 L/s·m, indicating weak to extremely strong water richness. The distance between the floor of the working face and the top boundary of the Ordovician limestone ranges from 33 to 53.3 m, with an average of 44.57 m. The Bauxite-bearing mudstone of the Carboniferous Benxi Formation, with an average thickness of about 8.22 m, serves as an effective aquiclude between the coal seam floor and the Ordovician aquifer. 3 Research Methods 3.1 Microseismic Monitoring System The 61607 working face of the Longwanggou Coal Mine is monitored using the KJ551 underground MS monitoring system 24 , 36 , 37 , 38 , 39 . The system structure is shown in Fig. 3 . MS sensors installed in the survey area receive vibration signals, which are transmitted to the MS monitoring substation. Signals are then sent through a ring network to the data acquisition host and subsequently to the data storage and processing host. The processing host performs secondary filtering, noise reduction, events localization, and multi-directional visualization of MS events. The KJ551 MS system employs high-sensitivity sensors (100 V/m/s) with a wide frequency range (0.1–600 Hz). For signal transmission, an advanced combination of cable and optical fiber technology is used. Cable transmission can cover distances of at least 10 km, while optical fiber transmission can reach up to 60 km, meeting the requirements of large-scale mining operations. Based on the actual conditions of the 61607 working face, two MS substations were deployed, each installed in a well-conditioned chamber. Ten sensors were installed in both the main haulage and auxiliary haulage entries of the working face. The boreholes for sensor installation were positioned 20 cm from the roadway ribs. Except for the first sensor, which was located 50 m from the cut-through, the spacing between adjacent sensors was approximately 100 m. After installation, each sensor underwent on-site hammer testing to ensure signal quality. Once the substations and sensors were installed, calibration blasting tests were conducted in the two main entries of the working face to ensure precise sensor positioning. The elastic wave velocity of the surrounding rock was accurately determined based on the blasting signals received by the sensors, allowing the calculation of the actual average wave velocity of the rock mass. Using this wave velocity, the source location of the test blasts was analyzed to validate the system’s positioning accuracy. The obtained velocity model and the actual sensor positions were imported into the processing software. During actual mining operations, the time differences of arrival at each MS sensor were used to calculate the source location and energy of events. This enabled determination of the spatial–temporal characteristics and energy levels of each MS events, providing the basis for corresponding early-warning measures. 3.2 Analytic Hierarchy Process–Entropy Weight Method (AHP–EWM) 3.2.1 Analytic Hierarchy Process The Analytic Hierarchy Process 40 , 41 , 42 (AHP) is a method for calculating weights both qualitatively and quantitatively. It involves three main steps: (1) Constructing a hierarchical structure model: The decision problem is decomposed into a hierarchical structure of goal layer–criteria layer–indicator layer. (2) Building the judgment matrix: For elements at the same hierarchical level, a relative importance scale is used. Experts are invited to score the importance of each element, forming a judgment matrix A =( a ij )n × n , where n is the number of elements at that level. The matrix satisfies reciprocity and reflexivity. (3) Weight calculation and consistency test: The maximum eigenvalue λ max and its corresponding eigenvector ω of matrix A are determined. The weights ω AHP,j for each element are calculated accordingly. The consistency index ( CI ) and consistency ratio ( CR ) are computed to ensure CR 1} \right)$$ 1 $$CR=\frac{{CI}}{{RI}}$$ 2 where CI is the consistency index, λ max is the maximum eigenvalue of the judgment matrix, r is the matrix order, and RI is the random consistency index. 3.2.2 Entropy Weight Method The Entropy Weight Method 43 , 44 (EWM) is based on information entropy theory. It quantifies the contribution of each indicator to the decision-making process according to its data dispersion, achieving objective weighting. The steps are as follows: (1) Data normalization: Since the indicators differ in dimension and magnitude, the raw data are normalized. (2) Calculation of information entropy: For the i -th sample of the j -th indicator, the probability p ij is calculated. The entropy value E j of each indicator is then obtained using the information entropy formula. (3) Determination of indicator weights: Smaller entropy E j indicates larger data dispersion and more effective information. The weight of the j -th indicator is calculated as: $${p_{ij}}=\frac{{{x_{ij}}}}{{\sum\limits_{{i=1}}^{m} {{x_{ij}}} }}$$ 3 $${E_j}= - \frac{1}{{\ln m}}\sum\nolimits_{{i=1}}^{m} {{p_{ij}}\ln {p_{ij}}}$$ 4 $${\omega _{EWM,j}}=\frac{{1 - {E_j}}}{{\sum\nolimits_{{i=1}}^{n} {\left( {1 - {E_j}} \right)} }}$$ 5 where x ij is the normalized value of the j -th indicator for the i -th sample, m is the number of samples, n is the number of indicators, and 1 − E j represents the information utility. 3.2.3 Vulnerability Index Method The Vulnerability Index Method is a mature evaluation model commonly used for floor water inrush risk assessment 45 , 46 , 47 , 48 , 49 . The vulnerability index I is calculated as: $$I=\sum\limits_{{j=1}}^{n} {{\omega _j} \cdot {x_{ij}}}$$ 6 Combining AHP and EWM, the composite weight ω j is calculated as: $${\omega _j}=\frac{{{\omega _{AHP,j}} \cdot {\omega _{EWM,j}}}}{{\sum\limits_{{j=1}}^{n} {{\omega _{AHP,j}} \cdot {\omega _{EWM,j}}} }}$$ 7 where ω j is the weight of the j -th indicator, ω AHP,j and ω EWM,j are the weights obtained from AHP and EWM, respectively, and n is the number of evaluation indicators. 4 Results 4.1 Overview of Microseismic Monitoring The study period was from April 1, 2023, to August 31, 2023, during which the 61607 working face advanced a total of 416.7 m, with an average daily advance of approximately 4 m. Based on the comprehensive geological column of the 61607 working face and actual mining exposure, the recorded MS events were classified according to the stratigraphic layers. The frequency of MS events for each layer is summarized in Table 1 . Before mining, hydraulic pre-fracturing was applied to the roof, causing partial roof failure. The compromised roof integrity interrupted the stress transmission paths, resulting in a relatively small number of roof MS events, accounting for only 5.98% of the total events. Therefore, this study focuses on floor failure, and roof-related events are not further discussed. Statistical analysis shows that MS events in the floor accounted for 95.67% of all recorded events within the study interval. Among them, MS events in the Carboniferous Taiyuan Formation floor strata represented 97.41% of all floor events. This indicates that the closer the rock mass is to the coal seam floor, the more significant the disturbance under mining-induced stress, making it more susceptible to failure and stress-concentration fracturing. The Carboniferous Benxi Formation and Ordovician Formation accounted for 1.73% and 1.02% of floor MS events, respectively. Although floor failure is mainly concentrated in the Taiyuan Formation, in areas with concealed structures, local floor disturbance can extend deeper, breaking through the Benxi Formation aquiclude and reaching the Ordovician aquifer. According to MS monitoring, the deepest disturbed point in the floor was 83.55 m below the coal seam floor, reaching the Ordovician limestone strata. Table 1 Frequency Statistics of MS Events at Working Face 61607 (Apr 1–Aug 31, 2023) Layer Frequency Percentage/% Percentage /% Roof Above the roof 280 4.33 4.33 Floor Carboniferous Taiyuan Formation 6008 92.92 97.25 Carboniferous Benxi Formation 112 1.73 98.98 Ordovician 48 1.02 100 To more intuitively illustrate the temporal variation of MS events, daily frequency histograms for roof and floor MS events and a daily average advance length line chart were plotted, as shown in Fig. 4 . The frequency of floor MS events showed significant increases on April 22, July 15, and August 4, and after August 16, the floor MS events frequency exhibited a consistent upward trend. Comparing with the daily average advance data, periods of increased advance length and speed (April 1–22, June 19–July 10, and July 16–28) corresponded to marked rises in MS events frequency. This indicates that high-intensity mining, combined with factors such as advance support pressure, vertical stress from high mining height and goaf areas, and hydrostatic pressure from the Ordovician limestone aquifer, significantly destabilized the floor rock. Under these high-stress conditions, primary fractures were stretched and propagated, accompanied by frequent micro-fracturing, generating a large number of MS events. During periods of reduced advance length and speed (April 23–June 18), MS events frequency remained relatively uniform without significant peaks, indicating a decrease in floor disturbance. After August 16, as both advance length and speed continued to increase and the working face entered the “secondary square” stage, the frequency of floor MS events rose again, reflecting enhanced rock mass disturbance. 4.2 Spatiotemporal Response Characteristics of Floor Microseismic Events Floor MS events represent energy-releasing fractures in the floor rock and can directly reflect the degree of floor failure across different strata in both space and time. In the 61607 working face, the floor aquiclude is relatively thin. The propagation of primary fractures or the development of concealed structures can easily connect to the floor aquifer, increasing water inflow or even causing water inrush. To better understand the impact of mining on floor disturbance, the spatiotemporal distribution characteristics of floor MS events were analyzed from the perspectives of time, space, and energy. 4.2.1 Temporal Distribution Characteristics The Carboniferous Benxi Formation lies below the floor failure zone and above the Ordovician limestone aquifer. This aquiclude mainly consists of bauxite-bearing mudstone and mudstone, with the bauxite-bearing mudstone layers being thick, lithologically stable, and possessing high uniaxial compressive strength, making it an effective floor aquiclude. If this layer is compromised, the integrity of the floor aquiclude may be lost, forming a water-conducting channel through which pressurized water can rise along fractures, potentially causing water inrush. Therefore, the Benxi Formation can be treated as a monitoring alert layer in MS studies. Daily floor MS events frequencies were counted for each stratigraphic layer and plotted ( Fig. 5 ). From April to June, MS events frequency remained relatively stable, with low frequencies in both the Benxi Formation and Ordovician strata. On July 15, MS events increased noticeably, particularly in the Benxi alert layer and the Ordovician aquifer, indicating intensified disturbance. After August 4 and 16, floor MS events frequencies continued to rise synchronously, reflecting further destabilization of the Ordovician limestone aquifer. Comparison with real-time monitoring of water inflow and Ordovician limestone water levels (Fig. 6 ) shows two significant water level drops during the study period. The first occurred from April 30 to May 10, without a corresponding increase in inflow. MS events frequencies in the Benxi alert layer and Ordovician strata remained low, suggesting that floor failure was confined to the Taiyuan Formation and had not yet connected to the Ordovician aquifer. The second drop occurred from June 30 to July 20, during which water inflow increased significantly between June 30 and July 31. From July 15, MS events frequency in the Benxi alert layer and Ordovician strata rose sharply, indicating that floor failure had developed into the Benxi Formation and the top of the Ordovician aquifer. Consequently, water inflow increased markedly, demonstrating a strong hydraulic connection between floor fractures and the aquifer. However, no water inrush occurred, suggesting that although mining-induced fractures communicated with the aquifer, some fractures were reclosed under high-stress conditions due to the large mining height, limiting water-conducting capacity. 4.2.2 Planar Distribution Characteristics The planar distribution of floor MS events within the 61607 working face study area is shown in Fig. 7 . Events are primarily concentrated 640 m ahead of the mining face, with an outward extension of approximately 200 m along both roadway sides. Examining the planar distribution of MS events by stratigraphic layer, the Taiyuan Formation exhibits the widest distribution. Events cluster near the SF10 fault and adjacent 61605 working face goaf, with concentration along the roadway sides significantly higher than in the center. This indicates that the post-mining surrounding rock experiences substantial vertical stress, leading to severe plastic deformation and release of potential energy. The clustering of MS events near the SF10 fault suggests that geologically weak zones are more susceptible to disturbance and failure. MS events in the Benxi Formation and Taiyuan Formation are mainly concentrated in the central part of the working face. The distribution of MS events in the Benxi Formation is broader than that in the Ordovician strata. During the early stage of mining, MS events clustering in both layers was low, gradually increasing toward the mid-section around mid-July. Combined with water inflow and Ordovician water level data (Fig. 6 and Fig. 7 ), the increase in MS events frequency in the Benxi alert layer corresponds with a drop in Ordovician water level and an increase in water inflow, lasting until the end of July. After the mining face passes through this region, the Ordovician water level rises while water inflow decreases. The MS events clusters in the Benxi and Ordovician strata form irregular elliptical patterns. This is attributed to roof pressure relief after mining, which causes the main stress to concentrate in the sidewalls. Consequently, lateral shear stress is imposed on the floor beneath the goaf, resulting in floor compression and expansion toward the goaf. This lateral stress concentration in the central part of the working face leads to floor disturbance and fracturing. Overall, the advance influence range of floor MS events in thick coal seam mining can reach up to 500 m, significantly exceeding the advance influence range of conventional thickness coal seams (200–300 m). This range is closely related to hydrogeological conditions and other factors. To investigate the dynamic evolution of the floor MS influence range, the daily MS events located ahead of the mining line are defined as the advance influence zone, while those located behind the mining line are defined as the rear influence zone, as illustrated in Fig. 8 . Daily distances of MS events in the advance and rear influence zones were statistically analyzed and plotted in a bar chart (Fig. 9 ). From April 1 to August 31, the advance influence distance continuously expanded with mining progress. During the early mining stage (April), the advance influence distance was relatively short, mostly within 200 m, indicating that early mining-induced stress had limited effect on floor disturbance. In the mid-mining stage (May–June), the advance influence distance extended to 300–400 m, and during the late mining stage (July–August), it reached a maximum of approximately 500 m. This indicates that the large mining height and strong disturbance of thick coal seam mining result in an advance influence range far exceeding that of conventional seams, a characteristic that becomes more pronounced as mining progresses. In contrast, the rear influence distance remained smaller and relatively stable, generally within 200 m, suggesting that after stress release in the goaf, the continued disturbance on the floor is weak. Therefore, MS events are primarily dominated by stress concentration in the advance zone. 4.2.3 Vertical Distribution Characteristics Based on the actual geological profile of the working face (along the main haulage entry), the vertical distribution of floor MS events was plotted as shown in Fig. 10 . The Carboniferous Taiyuan Formation (colored in blue) exhibits the highest number and widest distribution of MS events, representing the primary layer of floor MS activity. The large mining height and associated stress from thick coal seam extraction directly act on this layer, causing frequent rock fracturing and the strongest MS responses. In the Carboniferous Benxi Formation (yellow), MS events are significantly reduced and distributed sparsely beneath the Taiyuan Formation. The vertical transmission of mining-induced stress to this layer is weaker than in the Taiyuan Formation, resulting in a lower frequency of rock fracturing. The Ordovician Formation (red) exhibits the fewest MS events, which are sporadic and scattered. The high rock strength and greater distance from the coal seam floor, coupled with the influence of mining-induced stress and floor water pressure, make this layer less susceptible to large-scale fracturing. The development depth of floor MS events reflects the concentrated floor failure range and serves as a key indicator for floor water inrush early warning. By statistically analyzing the spatial coordinates of MS events, the spatial influence range, i.e., the development depth of floor MS events, can be determined. The development depth was segmented into 2 m intervals, and the corresponding MS events frequency, proportion, and cumulative proportion were calculated (Fig. 11 ), providing a clear visualization of the vertical distribution of MS events. MS events are highly concentrated in the shallow region beneath the coal seam floor, with the 0–28 m depth interval exhibiting peak frequency, accounting for approximately 96% of total events. This interval is directly below the coal seam floor and is subjected to mining-induced stress, resulting in rock failure primarily through tensile fracturing and shear slip, which represents the core zone of mining energy release and the strongest MS responses. With increasing depth, MS events frequency decays exponentially. The 26–28 m interval accounts for about 0.59%, and the 28–30 m interval accounts for only 0.31%, indicating very few MS events beyond 28 m. Therefore, the concentrated floor failure zone based on MS monitoring is considered to extend up to 28 m below the coal seam floor. According to the ‘Code for Design on Pillar Reserving and Pressure Coal Mining of Buildings, Water Bodies, Railways, and Major Tunnels,’ a large number of observed values of floor damage depth are statistically analyzed. Using the linear regression method, factors influencing the depth of floor damage, such as the inclined length of the working face, coal seam mining depth, coal seam mining thickness, and working face inclination angle, are taken into consideration. The mining-induced floor failure depth h can be estimated using the empirical formula: $$h=0.0085H+0.1665a+0.1079L - 4.3579$$ 8 where L is the working face dip length (m), α is the coal seam dip angle (°), and H is the mining depth (m). For the 61607 working face, L = 255 m, α = 3°, and H is taken as the average burial depth of 353.6 m. Substituting these values yields a floor failure depth of 26.66 m. The MS monitoring analysis results show a maximum deviation of less than 5% from the theoretical empirical calculation, indicating that the floor failure depth derived from MS monitoring is reasonable and reliable. 4.2.4 Energy Distribution Characteristics The energy of MS events quantitatively represents the release of elastic potential energy during rock fracturing, and can effectively reflect the scale, intensity, and degree of mining-induced stress disturbance in the rock mass. To further investigate the intrinsic relationship between MS events energy and mining-induced stress manifestation, the daily cumulative energy of floor MS events was analyzed in conjunction with the maximum subsidence of hydraulic support columns in the roadway, as shown in Fig. 12 . The daily cumulative MS energy exhibits an overall “low–high–low–high” trend. During the early and mid-mining stages (April–June), daily cumulative energy remained at a relatively low level with small fluctuations. During this period, the working face was in the “initial stress adjustment” stage, and mining-induced disturbances to the floor were relatively mild. Rock fracturing was limited in scale and frequency, the maximum subsidence of hydraulic supports was stable, mining-induced stress on the floor was low, and water inflow remained stable without significant increase. In the late mining stage (July–August), daily cumulative energy experienced a sudden surge around mid-July, coinciding with a corresponding increase in maximum hydraulic support subsidence. This period was accompanied by strong manifestation of mining-induced stress, a drop in limestone water level, and an increase in water inflow, indicating that as mining progressed, stress accumulation and release in the floor entered a high-disturbance stage. Large-scale, high-energy rock fracturing occurred, and MS responses intensified significantly. In late August, as the working face entered the “secondary square” stage, both daily cumulative energy and mining-induced stress increased again. The spatial distribution of MS events energy in the Carboniferous Benxi Formation and deeper strata from June to August is shown in Fig. 13 . In June, MS events were sparse and scattered, indicating weak disturbance of the Benxi Formation and deeper layers by mining-induced stress. In July, MS events frequency increased significantly, with the occurrence of multiple high-energy events, although clustering remained low. During this stage, mining-induced stress propagated from the coal seam floor to the overlying strata, markedly enhancing floor disturbance; both the frequency and scale of rock fracturing increased, and energy release intensified. In August, high-energy MS events became concentrated, forming strip-like distributions along the working face near the main haulage entry. The Benxi Formation and deeper strata experienced large-scale, concentrated rock fracturing, leading to the development and connectivity of floor fractures, and stress accumulation and release reached a peak. The temporal evolution of floor MS energy exhibits a characteristic pattern of “early low-energy stability, late high-energy burst”, which is closely coupled with mining-induced stress manifestation and water inflow changes. High-energy MS events are the key factor driving the extension of floor failure to greater depths and provide a direct basis for establishing energy-threshold-based early warning for floor water inrush risk. 4.3 Floor Water Inrush Hazard Evaluation Based on AHP-EWM 4.3.1 Indicators for Floor Water Inrush Hazard Based on the spatiotemporal characteristics of floor MS events analyzed in Section 4.2 , the MS-based indicators directly reflecting the floor water inrush hazard can be classified into three categories: activity indicators, spatiotemporal indicators, and intensity indicators. (1) Activity Indicators. As discussed in Section 4.2.1 , the activity of floor MS events reflects the degree of rock fracturing and floor disturbance. The selected activity indicators include: Daily frequency of floor MS events and daily frequency of MS events in the Benxi Formation and deeper strata. (2) Spatiotemporal Indicators. The formation of water-conducting channels is a necessary but not sufficient condition for water inrush. The spatiotemporal distribution of MS events can reflect the development and connectivity of these channels. Therefore, the selected spatiotemporal indicators include: Maximum daily development depth of floor MS events and advance influence distance of MS events. The Benxi Formation serves as a key floor aquiclude, and when the maximum daily development depth of floor MS events exceeds the Benxi Formation, it indicates an increased risk of water inflow, potentially leading to enhanced water discharge or even floor water inrush. The dynamic trend of this indicator can be directly linked to the evolution of mining-induced water-conducting fracture channels. The maximum daily development depth of floor MS events was plotted as a point-line graph (Fig. 14 ) to analyze the dynamic relationship between water inflow and floor failure depth during the mining of the 61607 working face. As shown in Fig. 14 , after July 14, the daily maximum floor failure depth increased, reaching the Ordovician strata. In geologically weak zones or areas with pre-existing fractures, thick coal seam mining can cause more intense floor damage, increasing the likelihood of new water-conducting fractures or enhanced water inflow. (3) Intensity Indicators. As analyzed in Section 4.2.4 , the energy of floor MS events reflects high-energy, large-scale fracturing and its impact on mining-induced stress manifestation, illustrating that MS events energy is closely associated with mining stress and the development of water-conducting channels. Therefore, the daily cumulative energy of floor MS events is selected as the intensity indicator. Based on the above analysis, five MS events indicators were identified to evaluate the floor water inrush hazard, reflecting the activity, spatiotemporal, and intensity characteristics of floor MS events: daily frequency of floor MS events, daily frequency of MS events in the Benxi Formation and deeper strata, maximum daily development depth of floor MS events, advance influence distance of MS events, and daily cumulative energy of floor MS events. All five indicators exhibit a positive correlation with the floor water inrush hazard, i.e., higher values correspond to higher risk. 4.3.2 Weight Calculation of Floor Water Inrush Hazard Indicators The weights of the floor water inrush hazard indicators were determined using both AHP and EWM, representing subjective and objective perspectives, respectively. The composite weights ( ω ) were then calculated by combining the two methods, as summarized in Table 2 . According to the composite weights, the ranking of the indicators from highest to lowest importance is as follows: daily frequency of MS events in the Benxi Formation and deeper strata > advance influence distance of MS events > daily cumulative energy of floor MS events > maximum development depth of floor MS events > daily frequency of floor MS events. Table 2 Table of Weight Calculation Results Indicators ω AHP ω EWM ω AHP × ω EWM ω Daily cumulative energy of floor MS events 0.0716 0.1409 0.010145 0.049015 Daily frequency of MS events in the Benxi Formation and deeper strata 0.3998 0.3315 0.107738 0.520538 Daily cumulative energy of floor MS events 0.2478 0.2476 0.023691 0.114464 Advance influence distance of MS events 0.1597 0.0745 0.040111 0.193799 Daily cumulative energy of floor MS events 0.1210 0.2056 0.025289 0.122184 4.3.3 Comprehensive Evaluation of Floor Water Inrush Hazard The floor water inrush hazard evaluation model was established by combining the composite weights of each indicator with their quantitative normalized values as follows: \({I_i}=0.072280{x_{i1}}+0.489597{x_{i2}}+0.026968{x_{i3}}+0.260178{x_{i4}}+0.150976{x_{i5}}\) The daily comprehensive hazard index was calculated and compared with the observed water inflow data of the working face, as shown in Fig. 15 . The comprehensive index was classified into risk levels using equal interval division: low risk (0–0.3), medium risk (0.3–0.6) and high risk (0.6–0.9). As shown in Fig. 15 , from April to June, the comprehensive index remained within the low-risk range, consistent with stable water inflow. In late June, some daily indices entered the medium-risk range, accompanied by minor fluctuations in water inflow. From July 1 to 21, the comprehensive index reached the high-risk range, with a peak on July 14, corresponding to the maximum observed water inflow. In August, some indices reached the medium-risk range, while individual indices reached high-risk levels. During this period, water inflow increased significantly, and as the working face entered the “secondary square” stage, mining-induced stress further enhanced floor failure depth, increasing the water inrush hazard. The evaluation model was further applied to MS events data from February to June 2024 for calculation and validation (Fig. 16 ). The results show that the comprehensive hazard index correlates well with observed water inflow variations, demonstrating that the model can be effectively used for early warning of floor water inrush. 5 Discussion and Outlook This study takes the Longwanggou Coal Mine in the Zhun’geer Coalfield, China as an example and employs MS monitoring technology to investigate the dynamic evolution characteristics of floor MS events during thick coal seam mining. By analyzing the spatiotemporal response characteristics of floor MS events (time, space, and energy), the correlations between various indicators and floor failure were assessed, and an AHP-EWM-based floor water inrush hazard evaluation model was established, providing guidance for the mining of thick coal seams. In fact, floor fracturing induced by thick coal seam mining results from the dynamic coupling of mining-induced stress, surrounding rock properties, and stratigraphic structure, making it more complex than thin- or medium-thickness coal seam mining. This study provides a preliminary investigation based on MS monitoring results. (1) Stress Transfer Characteristics of Floor Fracturing. In thick coal seam mining, the larger mined-out space significantly increases the depth and intensity of stress transfer to the floor. The maximum concentrated destruction depth of MS events reaches 26.66 m in the middle and lower Taiyuan Formation, and MS events are still detectable in the Benxi Formation and deeper strata (beyond 34.14 m), accounting for ~ 8% of total events, contrasting with thin- or medium-thickness seams where floor failure is typically within 10 m. This is primarily because thick coal seam mining induces a wider roof movement and more intense stress release, generating energy release in deep floor strata and forming a vertically layered fracture pattern: shallow dense fractures → middle gradual fractures → deep localized fractures. From a water inrush hazard perspective, the floor fracture mechanism directly controls the formation of water-conducting channels. While shallow fractures are dense, they rarely connect to the Ordovician high-pressure aquifer, whereas deep fractures, when combined with aquifer pressure, can trigger a “fracture propagation – hydraulic lift” coupled disaster process. The high correlation between daily frequency of MS events in the Benxi Formation and deeper strata and water inrush risk (subjective weight 0.3998, objective weight 0.3315, composite weight 0.5205) confirms that deep floor fracturing is a key early-warning factor, supporting the shift of mitigation focus to deeper strata. (2) Dynamic Evaluation Using MS Monitoring Previous floor water inrush evaluations often relied on static indicators. This study constructs a dynamic indicator system based on MS monitoring, comprising activity, spatiotemporal, and intensity indicators, and applies the AHP-EWM combined weighting method to determine indicator weights. By integrating subjective and objective weights, the reliability and scientific validity of the evaluation are enhanced. The combined weighting results are consistent with field monitoring data, demonstrating the method’s effectiveness. (3) Limitations and Future Research Directions The current MS monitoring dataset covers a limited temporal span, excluding the full mining cycle (e.g., initial and periodic stress manifestation stages), which may introduce bias in weight calculations. Moreover, the selected evaluation indicators do not consider multi-factor coupling effects, such as rock mechanical properties or grouting reinforcement coverage, potentially causing deviations between predicted and actual water inrush risk under complex geological conditions. Future research could focus on: Extending monitoring duration and expanding the indicator system: Cover the full mining cycle and incorporate factors such as floor rock integrity coefficients and grouting reinforcement effectiveness, constructing a “dynamic MS – static geological – engineering intervention” multidimensional evaluation system to improve comprehensiveness. Mechanism study with numerical simulation: Develop a floor stress-fracture numerical model for thick coal seam mining, and compare the simulated fracture depth and energy distribution with MS monitoring data to reveal the “mining-induced stress – rock fracturing – hydraulic lift” coupled disaster thresholds, providing a theoretical basis for weight optimization. Development of intelligent early-warning systems: Based on the AHP-EWM evaluation model and real-time MS monitoring data, develop an integrated system of data acquisition → weight calculation → risk classification → early-warning issuance, enabling dynamic and automated water inrush risk prediction, thereby supporting safe and efficient mining of thick coal seams. 6 Conclusions This study investigates the dynamic evolution characteristics of floor MS events during thick coal seam mining. Using MS monitoring technology and a comprehensive evaluation method, the spatiotemporal evolution of floor MS events was systematically analyzed, revealing their evolutionary patterns, and MS event characteristics were used as indicators for hazard assessment. The main conclusions are as follows: (1) Spatiotemporal Characteristics of Floor MS Events Continuous MS monitoring was conducted at the 61607 working face of Longwanggou Coal Mine, and the spatiotemporal response of floor MS events was analyzed. The results indicate that MS events are primarily concentrated in the Taiyuan Formation. As mining progressed, the frequency, depth, spatial extent, and energy of MS events increased. Combined with observed water inflow and mining-induced stress data, indicators such as advance influence zone, floor failure depth, and daily energy release showed clear response patterns, reflecting the floor damage morphology and characteristics under thick coal seam mining. (2) Key Factors for Early Warning of Floor Water Inrush Based on the spatiotemporal analysis of floor MS events, thick coal seam mining induced MS events characterized by greater depth, wider spatial distribution, and higher energy release. MS events occurring in the Benxi Formation and deeper strata are critical for identifying deep aquifer disturbances. These events are closely associated with groundwater movement and tectonic activation and provide important early-warning information for MS monitoring systems, effectively enhancing the accuracy of floor water inrush prediction. (3) Quantitative Evaluation of Floor Water Inrush Hazard The study summarizes the key controlling factors of floor water inrush, focusing on MS events activity, spatiotemporal distribution, and intensity characteristics. By integrating multiple influencing indicators and applying the AHP-EWM combined weighting method, a quantitative evaluation system for floor water inrush hazard in thick coal seam mining was established. The evaluation results show good agreement with field monitoring data, demonstrating the method’s reliability and practical applicability. Declarations Acknowledgements The author gratefully acknowledges the funding and assistance from proof-readers and editors. Data availability The authors declare that all data presented in this manuscript are available to researchers for noncommercial use, provided participant confidentiality is maintained. Data requests can be directed to the corresponding author. Author contributions Haorui Wang conceived the research and wrote the initial draft of the paper, Shangxian Yin and Huiqing Lian provided guidance for the on-site experiments, Min Cao was responsible for data processing and chart drawing, Enke Hou and Xu Wang proofread and translated the paper, and Guofu Xing provided the experimental site. All authors reviewed, revised, and approved the final version of the manuscript. Funding National Key Research and Development Program of China (No. 2022YFC3005905-1); Project under the Central-Guided Fund for Local Science and Technology Development (No. 246Z7604G); Hebei Natural Science Foundation Project (No. D2025508011); The Fundamental Research Funds for the Central Universities (No. 3142025007). Competing interests The authors declare no competing interests. Informed Consent Statement All experts involved in the experimental part of this study were fully informed of the research objectives, procedures, and intended use of the collected data. 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Cite Share Download PDF Status: Published Journal Publication published 25 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Nov, 2025 Reviews received at journal 26 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 07 Nov, 2025 Editor assigned by journal 06 Nov, 2025 Editor invited by journal 06 Nov, 2025 Submission checks completed at journal 02 Nov, 2025 First submitted to journal 02 Nov, 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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2","display":"","copyAsset":false,"role":"figure","size":104892,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive Stratigraphic Column of Roof and Floor at Working Face 61607\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/f470cb28409c04e087c1e01c.png"},{"id":96203048,"identity":"f64f5ba8-9ae0-4434-9459-e0a349e387f4","added_by":"auto","created_at":"2025-11-18 16:48:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82460,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic Diagram of the MS Monitoring System\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/5688e97298773a75045a32d1.png"},{"id":96252887,"identity":"b6615675-566d-4a9f-a221-6f45525e1c7f","added_by":"auto","created_at":"2025-11-19 07:41:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":86591,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency Statistics of MS Events at Working Face 61607 (Apr 1–Aug 31, 2023)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/309abc33b818c7a5ce55d5c0.png"},{"id":96203060,"identity":"ccd761de-36cc-4927-bd52-8374e585eb7a","added_by":"auto","created_at":"2025-11-18 16:48:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":79612,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram of MS Events Frequency in Different Floor Strata\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/d1706e82c37ba86bd866e9d1.png"},{"id":96203054,"identity":"6a820ca7-f19e-4ba3-bf4b-0221d7990419","added_by":"auto","created_at":"2025-11-18 16:48:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":254891,"visible":true,"origin":"","legend":"\u003cp\u003eVariation Trend of Water Inflow and Ordovician Limestone Water Level in the Working Face\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/c5d48550836f5264945024d4.png"},{"id":96252539,"identity":"c4c9e416-5869-491a-95f7-f95e30f8600d","added_by":"auto","created_at":"2025-11-19 07:41:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":717312,"visible":true,"origin":"","legend":"\u003cp\u003ePlan Distribution Map of Floor MS Events\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/f2f12490aa4cedddcee98af9.png"},{"id":96203061,"identity":"4dac6e2b-29c8-490d-9900-7cc61379f2df","added_by":"auto","created_at":"2025-11-18 16:48:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":90299,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic Diagram of the Planar Influence Range of MS Events\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/298f7f0b0a0c0b11fc9ff63a.png"},{"id":96203057,"identity":"7db47af9-5199-4f45-ba5d-6cd9bd978254","added_by":"auto","created_at":"2025-11-18 16:48:57","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":102625,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence Distance before and after the Mining Line\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/536ee08f0aeabeb483a5f3d2.png"},{"id":96203076,"identity":"55c0089c-2371-406d-b2d9-bcadc560765e","added_by":"auto","created_at":"2025-11-18 16:48:58","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":148178,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal Section Distribution of MS Events along the Roadway\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/1f256e8a57366d1463e6e979.png"},{"id":96203056,"identity":"4c6f3f3b-c1e4-4797-9938-2618dfebe138","added_by":"auto","created_at":"2025-11-18 16:48:57","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":205815,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical Chart of Floor MS Events Development Depth\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/ed31e67a66fcab367bcaaab0.png"},{"id":96203058,"identity":"c285a9e5-76c7-46b6-9070-12f3ff46e572","added_by":"auto","created_at":"2025-11-18 16:48:57","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":187279,"visible":true,"origin":"","legend":"\u003cp\u003eDaily Total Energy of Floor MS Events and Variation of Maximum Hydraulic Support Convergence\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/1eec17887271418f7d79004e.png"},{"id":96203067,"identity":"8ecaf83f-a114-469f-8dba-9dc8dc1b1d41","added_by":"auto","created_at":"2025-11-18 16:48:58","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":198058,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy Distribution of MS Events in the Benxi Formation and Deeper Strata: (a) June; (b) July; (c) August\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/726ed06de6d00323edc13d3f.png"},{"id":96203059,"identity":"2dc316fc-9fe2-43e4-bf7b-fda21be08651","added_by":"auto","created_at":"2025-11-18 16:48:57","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":300230,"visible":true,"origin":"","legend":"\u003cp\u003ePoint-Line Chart of the Daily Maximum Development Depth of Floor MS Events\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/eab32e22d229ed29a61e70eb.png"},{"id":96252091,"identity":"63f0a59e-c44d-4fc3-b46c-1705bf8b3683","added_by":"auto","created_at":"2025-11-19 07:40:26","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":380247,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive Evaluation of Floor Water Inrush Risk\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/404f76b5d72f77194f6bdf9e.png"},{"id":96203068,"identity":"219be425-f25b-4c10-8b6d-45f48aa57dc8","added_by":"auto","created_at":"2025-11-18 16:48:58","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":389511,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of the Comprehensive Evaluation of Floor Water Inrush Risk (February–June 2024)\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/36d7aab84c0b7d706d67979a.png"},{"id":99172885,"identity":"6faac540-dc32-465c-88ce-747e493ef7cb","added_by":"auto","created_at":"2025-12-29 16:11:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5112672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7988169/v1/598f8b45-92a9-4056-a6cc-d56786dd82a3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation on the Dynamic Evolution and Evaluation of Floor Microseismic Responses during Extra-Thick Coal Seam Mining","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe coal resources in the Jin\u0026ndash;Shan\u0026ndash;Meng mining area of China are characterized by large reserves and thick coal seams\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. With the continuous progress of coal mining technology, a high-intensity and high-efficiency mining mode has been gradually established. Compared with conventional thin and medium-thick coal seams, the mining of extra-thick coal seams is associated with higher strata pressure, stronger ground stress effects, and greater disturbance to the floor, often resulting in extensive and deep floor fracturing\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Moreover, the Carboniferous\u0026ndash;Permian coal seam floors in China generally overlie Ordovician limestone aquifers with high water pressure. The fractures in the floor can easily connect with the aquifers and trigger water inrush disasters, posing serious threats to mine safety. Floor water hazards caused by high-pressure confined aquifers have become one of the main factors restricting the safe and efficient extraction of coal resources in China\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Therefore, it is necessary to investigate the dynamic failure evolution of the floor during extra-thick coal seam mining.\u003c/p\u003e\u003cp\u003eMany scholars have conducted studies on extra-thick coal seam mining and achieved valuable results. Zheng et al.\u003csup\u003e6\u003c/sup\u003e analyzed the water inrush mechanism of the Huangyuchuan Mine during top-coal caving and clarified the coupling mechanism between Ordovician limestone permeable channels and floor failure, proposing a directional long-borehole exploration technique to prevent Ordovician limestone water hazards during mining. Shao\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e systematically analyzed the roof and floor structure and failure characteristics of fully mechanized top-coal caving faces and established an elastic mechanical model of the floor. The study showed that the maximum failure depth of the floor is positively correlated with the face width and surrounding rock stress and negatively correlated with rock compressive strength. Li et al.\u003csup\u003e8\u003c/sup\u003e based on the gas\u0026ndash;water two-phase flow similarity theory in fractured rock masses, established a CFDEM numerical model to simulate the fault-induced floor water inrush and roof\u0026ndash;floor coupling failure in extra-thick coal seam mining. Their results indicated that the root cause of water inrush lies in the instability of key high-level strata, subsidence of masonry beams, and secondary fracturing of the floor. Wang et al.\u003csup\u003e9,10,11\u003c/sup\u003e used theoretical analysis, numerical simulation, and field measurements to study the stress and fracture evolution of the floor during fully mechanized top-coal caving. Zhang et al.\u003csup\u003e12,13,14,15\u003c/sup\u003e applied distributed optical fiber sensing and cross-hole resistivity CT testing to obtain in situ data on strain and geoelectric field responses during floor failure in extra-thick coal seam mining. Guo\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e analyzed the three-dimensional dynamic evolution of floor disturbance using borehole stress\u0026ndash;strain monitoring. Cao et al.\u003csup\u003e17\u003c/sup\u003e derived a mechanical relationship between floor failure depth and coal seam thickness and established a floor water inrush risk evaluation model using interval grey optimal clustering analysis.\u003c/p\u003e\u003cp\u003eIn addition to floor failure research, Zhu et al.\u003csup\u003e18\u003c/sup\u003e studied the mechanism of floor heave in fully mechanized top-coal caving faces and proposed an underground regional hydraulic fracturing technique for controlling floor heave. Zhang et al.\u003csup\u003e19,20\u003c/sup\u003e investigated the characteristics of roof caving structure and dynamic loading mechanisms, analyzing the strong dynamic pressure phenomena in working faces of extra-thick seams. Kong et al.\u003csup\u003e21,22\u003c/sup\u003e examined the evolution of fractures and seepage characteristics during mining and developed a predictive model for the height of the water-conducting fracture zone. Yu et al.\u003csup\u003e23\u003c/sup\u003e explored the multi-field coupling mechanism of disaster formation and proposed a collaborative control technology.\u003c/p\u003e\u003cp\u003eMost existing studies have been based on theoretical analysis, numerical simulation, and field measurements, which have limited continuity and fail to fully represent the dynamic evolution process of floor failure. As a real-time, dynamic, and efficient geophysical monitoring technique, MS observation has been increasingly applied in floor safety studies\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Yin et al.\u003csup\u003e25\u003c/sup\u003e used machine learning and deep learning models to analyze MS events and successfully predicted water inrush incidents. Xin et al.\u003csup\u003e26\u003c/sup\u003e applied cumulative and kernel density indicators of MS events to interpret fault activation characteristics in the floor. Jia et al.\u003csup\u003e27,28\u003c/sup\u003e established a water inrush risk evaluation method based on MS events, while Gai et al.\u003csup\u003e29,30\u003c/sup\u003e introduced weighted variation coefficients and floor safety indices to improve the risk evaluation model and zoning accuracy. Xu et al.\u003csup\u003e31,32\u003c/sup\u003e proposed an all-time\u0026ndash;space early warning method for floor water inrush using MS monitoring, achieving accurate prediction at different mining stages. Zuo et al.\u003csup\u003e33,34,35\u003c/sup\u003e combined theoretical analysis, MS monitoring, and numerical simulation to identify and predict potential water inrush channels based on floor rock fracture characteristics.\u003c/p\u003e\u003cp\u003eAlthough these studies have explored various aspects of floor water hazard prevention using MS technology and developed related theories and prediction methods, the understanding of floor failure characteristics and evaluation systems under special mining conditions remains incomplete. This is especially true for extra-thick coal seam mining in western China, where the large mining height produces strong and complex disturbances to the floor. Therefore, taking the Longwanggou Coal Mine in the Zhungeer Coalfield as the research area, this study applies MS monitoring technology to analyze the dynamic evolution of floor failure. Based on the AHP\u0026ndash;EWM method and combined with measured water inflow data, a quantitative classification and evaluation of floor water inrush risk is performed. The research results can further enhance the application of MS technology in floor water hazard prevention and provide theoretical support for similar mining operations, offering practical significance for improving coal mine safety.\u003c/p\u003e"},{"header":"2 Study Area Overview","content":"\u003cp\u003eThe Longwanggou Coal Mine is located in the central part of the Zhungeer Coalfield, Inner Mongolia, China. It belongs to the Dongsheng Subzone of the Ordos Stratigraphic Subregion within the North China Stratigraphic Region. The main mining seam is the Carboniferous Taiyuan Formation coal seam. The 61607 working face is situated in the northern wing of the 61st panel district. The western side of the panel is bounded by intact coal and rock strata, while the eastern side is adjacent to the goaf area, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe working face has a strike length of 1,276.2 m, an inclined length of 255 m, an average mining thickness of 21.6 m, and a burial depth ranging from 280.4 to 426.8 m, with an average dip angle of \u003cb\u003e3\u0026deg;\u003c/b\u003e. The lithological profile of the roof and floor strata of the 61607 working face is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The main aquifer in the floor is the karst fissure aquifer of the Ordovician limestone. The principal water-bearing section is the Middle Ordovician Majiagou Formation, whose upper part consists of light gray argillaceous limestone and thick-bedded limestone interbedded with thin layers of gray limestone. The lower part is composed of dolomitic limestone and thick-bedded dolomite, with well-developed dissolution fissures, cavities, and pores. The unit water inflow varies between 0.024 and 34.321 L/s\u0026middot;m, indicating weak to extremely strong water richness.\u003c/p\u003e\u003cp\u003eThe distance between the floor of the working face and the top boundary of the Ordovician limestone ranges from 33 to 53.3 m, with an average of 44.57 m. The Bauxite-bearing mudstone of the Carboniferous Benxi Formation, with an average thickness of about 8.22 m, serves as an effective aquiclude between the coal seam floor and the Ordovician aquifer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3 Research Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Microseismic Monitoring System\u003c/h2\u003e\u003cp\u003eThe 61607 working face of the Longwanggou Coal Mine is monitored using the KJ551 underground MS monitoring system\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The system structure is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. MS sensors installed in the survey area receive vibration signals, which are transmitted to the MS monitoring substation. Signals are then sent through a ring network to the data acquisition host and subsequently to the data storage and processing host. The processing host performs secondary filtering, noise reduction, events localization, and multi-directional visualization of MS events.\u003c/p\u003e\u003cp\u003eThe KJ551 MS system employs high-sensitivity sensors (100 V/m/s) with a wide frequency range (0.1\u0026ndash;600 Hz). For signal transmission, an advanced combination of cable and optical fiber technology is used. Cable transmission can cover distances of at least 10 km, while optical fiber transmission can reach up to 60 km, meeting the requirements of large-scale mining operations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the actual conditions of the 61607 working face, two MS substations were deployed, each installed in a well-conditioned chamber. Ten sensors were installed in both the main haulage and auxiliary haulage entries of the working face. The boreholes for sensor installation were positioned 20 cm from the roadway ribs. Except for the first sensor, which was located 50 m from the cut-through, the spacing between adjacent sensors was approximately 100 m. After installation, each sensor underwent on-site hammer testing to ensure signal quality.\u003c/p\u003e\u003cp\u003eOnce the substations and sensors were installed, calibration blasting tests were conducted in the two main entries of the working face to ensure precise sensor positioning. The elastic wave velocity of the surrounding rock was accurately determined based on the blasting signals received by the sensors, allowing the calculation of the actual average wave velocity of the rock mass. Using this wave velocity, the source location of the test blasts was analyzed to validate the system\u0026rsquo;s positioning accuracy.\u003c/p\u003e\u003cp\u003eThe obtained velocity model and the actual sensor positions were imported into the processing software. During actual mining operations, the time differences of arrival at each MS sensor were used to calculate the source location and energy of events. This enabled determination of the spatial\u0026ndash;temporal characteristics and energy levels of each MS events, providing the basis for corresponding early-warning measures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Analytic Hierarchy Process\u0026ndash;Entropy Weight Method (AHP\u0026ndash;EWM)\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Analytic Hierarchy Process\u003c/h2\u003e\u003cp\u003eThe Analytic Hierarchy Process\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e (AHP) is a method for calculating weights both qualitatively and quantitatively. It involves three main steps:\u003c/p\u003e\u003cp\u003e(1) Constructing a hierarchical structure model: The decision problem is decomposed into a hierarchical structure of goal layer\u0026ndash;criteria layer\u0026ndash;indicator layer.\u003c/p\u003e\u003cp\u003e(2) Building the judgment matrix: For elements at the same hierarchical level, a relative importance scale is used. Experts are invited to score the importance of each element, forming a judgment matrix \u003cem\u003eA\u003c/em\u003e=(\u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)n\u003c/em\u003e\u0026times;\u003cem\u003en\u003c/em\u003e, where \u003cem\u003en\u003c/em\u003e is the number of elements at that level. The matrix satisfies reciprocity and reflexivity.\u003c/p\u003e\u003cp\u003e(3) Weight calculation and consistency test: The maximum eigenvalue \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and its corresponding eigenvector \u003cem\u003eω\u003c/em\u003e of matrix \u003cem\u003eA\u003c/em\u003e are determined. The weights \u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003eAHP,j\u003c/em\u003e\u003c/sub\u003e for each element are calculated accordingly. The consistency index (\u003cem\u003eCI\u003c/em\u003e) and consistency ratio (\u003cem\u003eCR\u003c/em\u003e) are computed to ensure \u003cem\u003eCR\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, avoiding logical contradictions in the judgments. The formulas are:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$CI=\\frac{{{\\lambda _{\\hbox{max} }} - r}}{{r - 1}}\\left( {r\u0026gt;1} \\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$CR=\\frac{{CI}}{{RI}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eCI\u003c/em\u003e is the consistency index, \u003cem\u003eλ\u003c/em\u003e\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e is the maximum eigenvalue of the judgment matrix, \u003cem\u003er\u003c/em\u003e is the matrix order, and \u003cem\u003eRI\u003c/em\u003e is the random consistency index.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Entropy Weight Method\u003c/h2\u003e\u003cp\u003eThe Entropy Weight Method\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e (EWM) is based on information entropy theory. It quantifies the contribution of each indicator to the decision-making process according to its data dispersion, achieving objective weighting. The steps are as follows:\u003c/p\u003e\u003cp\u003e(1) Data normalization: Since the indicators differ in dimension and magnitude, the raw data are normalized.\u003c/p\u003e\u003cp\u003e(2) Calculation of information entropy: For the \u003cem\u003ei\u003c/em\u003e-th sample of the \u003cem\u003ej\u003c/em\u003e-th indicator, the probability \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e is calculated. The entropy value \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e of each indicator is then obtained using the information entropy formula.\u003c/p\u003e\u003cp\u003e(3) Determination of indicator weights: Smaller entropy \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e indicates larger data dispersion and more effective information. The weight of the \u003cem\u003ej\u003c/em\u003e-th indicator is calculated as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${p_{ij}}=\\frac{{{x_{ij}}}}{{\\sum\\limits_{{i=1}}^{m} {{x_{ij}}} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${E_j}= - \\frac{1}{{\\ln m}}\\sum\\nolimits_{{i=1}}^{m} {{p_{ij}}\\ln {p_{ij}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${\\omega _{EWM,j}}=\\frac{{1 - {E_j}}}{{\\sum\\nolimits_{{i=1}}^{n} {\\left( {1 - {E_j}} \\right)} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e is the normalized value of the \u003cem\u003ej\u003c/em\u003e-th indicator for the \u003cem\u003ei\u003c/em\u003e-th sample, \u003cem\u003em\u003c/em\u003e is the number of samples, \u003cem\u003en\u003c/em\u003e is the number of indicators, and 1\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e represents the information utility.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Vulnerability Index Method\u003c/h2\u003e\u003cp\u003eThe Vulnerability Index Method is a mature evaluation model commonly used for floor water inrush risk assessment\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The vulnerability index \u003cem\u003eI\u003c/em\u003e is calculated as:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$I=\\sum\\limits_{{j=1}}^{n} {{\\omega _j} \\cdot {x_{ij}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCombining AHP and EWM, the composite weight \u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is calculated as:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${\\omega _j}=\\frac{{{\\omega _{AHP,j}} \\cdot {\\omega _{EWM,j}}}}{{\\sum\\limits_{{j=1}}^{n} {{\\omega _{AHP,j}} \\cdot {\\omega _{EWM,j}}} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is the weight of the \u003cem\u003ej\u003c/em\u003e-th indicator, \u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003eAHP,j\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003eEWM,j\u003c/em\u003e\u003c/sub\u003e are the weights obtained from AHP and EWM, respectively, and \u003cem\u003en\u003c/em\u003e is the number of evaluation indicators.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Overview of Microseismic Monitoring\u003c/h2\u003e\u003cp\u003eThe study period was from April 1, 2023, to August 31, 2023, during which the 61607 working face advanced a total of 416.7 m, with an average daily advance of approximately 4 m. Based on the comprehensive geological column of the 61607 working face and actual mining exposure, the recorded MS events were classified according to the stratigraphic layers. The frequency of MS events for each layer is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eBefore mining, hydraulic pre-fracturing was applied to the roof, causing partial roof failure. The compromised roof integrity interrupted the stress transmission paths, resulting in a relatively small number of roof MS events, accounting for only 5.98% of the total events. Therefore, this study focuses on floor failure, and roof-related events are not further discussed.\u003c/p\u003e\u003cp\u003eStatistical analysis shows that MS events in the floor accounted for 95.67% of all recorded events within the study interval. Among them, MS events in the Carboniferous Taiyuan Formation floor strata represented 97.41% of all floor events. This indicates that the closer the rock mass is to the coal seam floor, the more significant the disturbance under mining-induced stress, making it more susceptible to failure and stress-concentration fracturing.\u003c/p\u003e\u003cp\u003eThe Carboniferous Benxi Formation and Ordovician Formation accounted for 1.73% and 1.02% of floor MS events, respectively. Although floor failure is mainly concentrated in the Taiyuan Formation, in areas with concealed structures, local floor disturbance can extend deeper, breaking through the Benxi Formation aquiclude and reaching the Ordovician aquifer. According to MS monitoring, the deepest disturbed point in the floor was 83.55 m below the coal seam floor, reaching the Ordovician limestone strata.\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\u003eFrequency Statistics of MS Events at Working Face 61607 (Apr 1\u0026ndash;Aug 31, 2023)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLayer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage/%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage /%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoof\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove the roof\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eFloor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarboniferous Taiyuan Formation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarboniferous Benxi Formation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOrdovician\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\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo more intuitively illustrate the temporal variation of MS events, daily frequency histograms for roof and floor MS events and a daily average advance length line chart were plotted, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The frequency of floor MS events showed significant increases on April 22, July 15, and August 4, and after August 16, the floor MS events frequency exhibited a consistent upward trend.\u003c/p\u003e\u003cp\u003eComparing with the daily average advance data, periods of increased advance length and speed (April 1\u0026ndash;22, June 19\u0026ndash;July 10, and July 16\u0026ndash;28) corresponded to marked rises in MS events frequency. This indicates that high-intensity mining, combined with factors such as advance support pressure, vertical stress from high mining height and goaf areas, and hydrostatic pressure from the Ordovician limestone aquifer, significantly destabilized the floor rock. Under these high-stress conditions, primary fractures were stretched and propagated, accompanied by frequent micro-fracturing, generating a large number of MS events.\u003c/p\u003e\u003cp\u003eDuring periods of reduced advance length and speed (April 23\u0026ndash;June 18), MS events frequency remained relatively uniform without significant peaks, indicating a decrease in floor disturbance. After August 16, as both advance length and speed continued to increase and the working face entered the \u0026ldquo;secondary square\u0026rdquo; stage, the frequency of floor MS events rose again, reflecting enhanced rock mass disturbance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Spatiotemporal Response Characteristics of Floor Microseismic Events\u003c/h2\u003e\u003cp\u003eFloor MS events represent energy-releasing fractures in the floor rock and can directly reflect the degree of floor failure across different strata in both space and time. In the 61607 working face, the floor aquiclude is relatively thin. The propagation of primary fractures or the development of concealed structures can easily connect to the floor aquifer, increasing water inflow or even causing water inrush. To better understand the impact of mining on floor disturbance, the spatiotemporal distribution characteristics of floor MS events were analyzed from the perspectives of time, space, and energy.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Temporal Distribution Characteristics\u003c/h2\u003e\u003cp\u003eThe Carboniferous Benxi Formation lies below the floor failure zone and above the Ordovician limestone aquifer. This aquiclude mainly consists of bauxite-bearing mudstone and mudstone, with the bauxite-bearing mudstone layers being thick, lithologically stable, and possessing high uniaxial compressive strength, making it an effective floor aquiclude. If this layer is compromised, the integrity of the floor aquiclude may be lost, forming a water-conducting channel through which pressurized water can rise along fractures, potentially causing water inrush. Therefore, the Benxi Formation can be treated as a monitoring alert layer in MS studies.\u003c/p\u003e\u003cp\u003eDaily floor MS events frequencies were counted for each stratigraphic layer and plotted \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). From April to June, MS events frequency remained relatively stable, with low frequencies in both the Benxi Formation and Ordovician strata. On July 15, MS events increased noticeably, particularly in the Benxi alert layer and the Ordovician aquifer, indicating intensified disturbance. After August 4 and 16, floor MS events frequencies continued to rise synchronously, reflecting further destabilization of the Ordovician limestone aquifer.\u003c/p\u003e\u003cp\u003eComparison with real-time monitoring of water inflow and Ordovician limestone water levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) shows two significant water level drops during the study period. The first occurred from April 30 to May 10, without a corresponding increase in inflow. MS events frequencies in the Benxi alert layer and Ordovician strata remained low, suggesting that floor failure was confined to the Taiyuan Formation and had not yet connected to the Ordovician aquifer. The second drop occurred from June 30 to July 20, during which water inflow increased significantly between June 30 and July 31. From July 15, MS events frequency in the Benxi alert layer and Ordovician strata rose sharply, indicating that floor failure had developed into the Benxi Formation and the top of the Ordovician aquifer. Consequently, water inflow increased markedly, demonstrating a strong hydraulic connection between floor fractures and the aquifer. However, no water inrush occurred, suggesting that although mining-induced fractures communicated with the aquifer, some fractures were reclosed under high-stress conditions due to the large mining height, limiting water-conducting capacity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Planar Distribution Characteristics\u003c/h2\u003e\u003cp\u003eThe planar distribution of floor MS events within the 61607 working face study area is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Events are primarily concentrated 640 m ahead of the mining face, with an outward extension of approximately 200 m along both roadway sides.\u003c/p\u003e\u003cp\u003eExamining the planar distribution of MS events by stratigraphic layer, the Taiyuan Formation exhibits the widest distribution. Events cluster near the SF10 fault and adjacent 61605 working face goaf, with concentration along the roadway sides significantly higher than in the center. This indicates that the post-mining surrounding rock experiences substantial vertical stress, leading to severe plastic deformation and release of potential energy. The clustering of MS events near the SF10 fault suggests that geologically weak zones are more susceptible to disturbance and failure.\u003c/p\u003e\u003cp\u003eMS events in the Benxi Formation and Taiyuan Formation are mainly concentrated in the central part of the working face. The distribution of MS events in the Benxi Formation is broader than that in the Ordovician strata. During the early stage of mining, MS events clustering in both layers was low, gradually increasing toward the mid-section around mid-July. Combined with water inflow and Ordovician water level data (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), the increase in MS events frequency in the Benxi alert layer corresponds with a drop in Ordovician water level and an increase in water inflow, lasting until the end of July. After the mining face passes through this region, the Ordovician water level rises while water inflow decreases.\u003c/p\u003e\u003cp\u003eThe MS events clusters in the Benxi and Ordovician strata form irregular elliptical patterns. This is attributed to roof pressure relief after mining, which causes the main stress to concentrate in the sidewalls. Consequently, lateral shear stress is imposed on the floor beneath the goaf, resulting in floor compression and expansion toward the goaf. This lateral stress concentration in the central part of the working face leads to floor disturbance and fracturing.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, the advance influence range of floor MS events in thick coal seam mining can reach up to 500 m, significantly exceeding the advance influence range of conventional thickness coal seams (200\u0026ndash;300 m). This range is closely related to hydrogeological conditions and other factors. To investigate the dynamic evolution of the floor MS influence range, the daily MS events located ahead of the mining line are defined as the advance influence zone, while those located behind the mining line are defined as the rear influence zone, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Daily distances of MS events in the advance and rear influence zones were statistically analyzed and plotted in a bar chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom April 1 to August 31, the advance influence distance continuously expanded with mining progress. During the early mining stage (April), the advance influence distance was relatively short, mostly within 200 m, indicating that early mining-induced stress had limited effect on floor disturbance. In the mid-mining stage (May\u0026ndash;June), the advance influence distance extended to 300\u0026ndash;400 m, and during the late mining stage (July\u0026ndash;August), it reached a maximum of approximately 500 m. This indicates that the large mining height and strong disturbance of thick coal seam mining result in an advance influence range far exceeding that of conventional seams, a characteristic that becomes more pronounced as mining progresses.\u003c/p\u003e\u003cp\u003eIn contrast, the rear influence distance remained smaller and relatively stable, generally within 200 m, suggesting that after stress release in the goaf, the continued disturbance on the floor is weak. Therefore, MS events are primarily dominated by stress concentration in the advance zone.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3 Vertical Distribution Characteristics\u003c/h2\u003e\u003cp\u003eBased on the actual geological profile of the working face (along the main haulage entry), the vertical distribution of floor MS events was plotted as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe Carboniferous Taiyuan Formation (colored in blue) exhibits the highest number and widest distribution of MS events, representing the primary layer of floor MS activity. The large mining height and associated stress from thick coal seam extraction directly act on this layer, causing frequent rock fracturing and the strongest MS responses.\u003c/p\u003e\u003cp\u003eIn the Carboniferous Benxi Formation (yellow), MS events are significantly reduced and distributed sparsely beneath the Taiyuan Formation. The vertical transmission of mining-induced stress to this layer is weaker than in the Taiyuan Formation, resulting in a lower frequency of rock fracturing.\u003c/p\u003e\u003cp\u003eThe Ordovician Formation (red) exhibits the fewest MS events, which are sporadic and scattered. The high rock strength and greater distance from the coal seam floor, coupled with the influence of mining-induced stress and floor water pressure, make this layer less susceptible to large-scale fracturing.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe development depth of floor MS events reflects the concentrated floor failure range and serves as a key indicator for floor water inrush early warning. By statistically analyzing the spatial coordinates of MS events, the spatial influence range, i.e., the development depth of floor MS events, can be determined. The development depth was segmented into 2 m intervals, and the corresponding MS events frequency, proportion, and cumulative proportion were calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e), providing a clear visualization of the vertical distribution of MS events.\u003c/p\u003e\u003cp\u003eMS events are highly concentrated in the shallow region beneath the coal seam floor, with the 0\u0026ndash;28 m depth interval exhibiting peak frequency, accounting for approximately 96% of total events. This interval is directly below the coal seam floor and is subjected to mining-induced stress, resulting in rock failure primarily through tensile fracturing and shear slip, which represents the core zone of mining energy release and the strongest MS responses.\u003c/p\u003e\u003cp\u003eWith increasing depth, MS events frequency decays exponentially. The 26\u0026ndash;28 m interval accounts for about 0.59%, and the 28\u0026ndash;30 m interval accounts for only 0.31%, indicating very few MS events beyond 28 m. Therefore, the concentrated floor failure zone based on MS monitoring is considered to extend up to 28 m below the coal seam floor.\u003c/p\u003e\u003cp\u003eAccording to the \u0026lsquo;Code for Design on Pillar Reserving and Pressure Coal Mining of Buildings, Water Bodies, Railways, and Major Tunnels,\u0026rsquo; a large number of observed values of floor damage depth are statistically analyzed. Using the linear regression method, factors influencing the depth of floor damage, such as the inclined length of the working face, coal seam mining depth, coal seam mining thickness, and working face inclination angle, are taken into consideration. The mining-induced floor failure depth \u003cem\u003eh\u003c/em\u003e can be estimated using the empirical formula:\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$h=0.0085H+0.1665a+0.1079L - 4.3579$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eL\u003c/em\u003e is the working face dip length (m), \u003cem\u003eα\u003c/em\u003e is the coal seam dip angle (\u0026deg;), and \u003cem\u003eH\u003c/em\u003e is the mining depth (m). For the 61607 working face, \u003cem\u003eL\u003c/em\u003e\u0026thinsp;=\u0026thinsp;255 m, \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3\u0026deg;, and \u003cem\u003eH\u003c/em\u003e is taken as the average burial depth of 353.6 m. Substituting these values yields a floor failure depth of 26.66 m.\u003c/p\u003e\u003cp\u003eThe MS monitoring analysis results show a maximum deviation of less than 5% from the theoretical empirical calculation, indicating that the floor failure depth derived from MS monitoring is reasonable and reliable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.2.4 Energy Distribution Characteristics\u003c/h2\u003e\u003cp\u003eThe energy of MS events quantitatively represents the release of elastic potential energy during rock fracturing, and can effectively reflect the scale, intensity, and degree of mining-induced stress disturbance in the rock mass. To further investigate the intrinsic relationship between MS events energy and mining-induced stress manifestation, the daily cumulative energy of floor MS events was analyzed in conjunction with the maximum subsidence of hydraulic support columns in the roadway, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe daily cumulative MS energy exhibits an overall \u0026ldquo;low\u0026ndash;high\u0026ndash;low\u0026ndash;high\u0026rdquo; trend. During the early and mid-mining stages (April\u0026ndash;June), daily cumulative energy remained at a relatively low level with small fluctuations. During this period, the working face was in the \u0026ldquo;initial stress adjustment\u0026rdquo; stage, and mining-induced disturbances to the floor were relatively mild. Rock fracturing was limited in scale and frequency, the maximum subsidence of hydraulic supports was stable, mining-induced stress on the floor was low, and water inflow remained stable without significant increase.\u003c/p\u003e\u003cp\u003eIn the late mining stage (July\u0026ndash;August), daily cumulative energy experienced a sudden surge around mid-July, coinciding with a corresponding increase in maximum hydraulic support subsidence. This period was accompanied by strong manifestation of mining-induced stress, a drop in limestone water level, and an increase in water inflow, indicating that as mining progressed, stress accumulation and release in the floor entered a high-disturbance stage. Large-scale, high-energy rock fracturing occurred, and MS responses intensified significantly. In late August, as the working face entered the \u0026ldquo;secondary square\u0026rdquo; stage, both daily cumulative energy and mining-induced stress increased again.\u003c/p\u003e\u003cp\u003eThe spatial distribution of MS events energy in the Carboniferous Benxi Formation and deeper strata from June to August is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e. In June, MS events were sparse and scattered, indicating weak disturbance of the Benxi Formation and deeper layers by mining-induced stress. In July, MS events frequency increased significantly, with the occurrence of multiple high-energy events, although clustering remained low. During this stage, mining-induced stress propagated from the coal seam floor to the overlying strata, markedly enhancing floor disturbance; both the frequency and scale of rock fracturing increased, and energy release intensified. In August, high-energy MS events became concentrated, forming strip-like distributions along the working face near the main haulage entry. The Benxi Formation and deeper strata experienced large-scale, concentrated rock fracturing, leading to the development and connectivity of floor fractures, and stress accumulation and release reached a peak.\u003c/p\u003e\u003cp\u003eThe temporal evolution of floor MS energy exhibits a characteristic pattern of \u0026ldquo;early low-energy stability, late high-energy burst\u0026rdquo;, which is closely coupled with mining-induced stress manifestation and water inflow changes. High-energy MS events are the key factor driving the extension of floor failure to greater depths and provide a direct basis for establishing energy-threshold-based early warning for floor water inrush risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Floor Water Inrush Hazard Evaluation Based on AHP-EWM\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Indicators for Floor Water Inrush Hazard\u003c/h2\u003e\u003cp\u003eBased on the spatiotemporal characteristics of floor MS events analyzed in Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e, the MS-based indicators directly reflecting the floor water inrush hazard can be classified into three categories: activity indicators, spatiotemporal indicators, and intensity indicators.\u003c/p\u003e\u003cp\u003e(1) Activity Indicators. As discussed in Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4.2.1\u003c/span\u003e, the activity of floor MS events reflects the degree of rock fracturing and floor disturbance. The selected activity indicators include: Daily frequency of floor MS events and daily frequency of MS events in the Benxi Formation and deeper strata.\u003c/p\u003e\u003cp\u003e(2) Spatiotemporal Indicators. The formation of water-conducting channels is a necessary but not sufficient condition for water inrush. The spatiotemporal distribution of MS events can reflect the development and connectivity of these channels. Therefore, the selected spatiotemporal indicators include: Maximum daily development depth of floor MS events and advance influence distance of MS events.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Benxi Formation serves as a key floor aquiclude, and when the maximum daily development depth of floor MS events exceeds the Benxi Formation, it indicates an increased risk of water inflow, potentially leading to enhanced water discharge or even floor water inrush. The dynamic trend of this indicator can be directly linked to the evolution of mining-induced water-conducting fracture channels.\u003c/p\u003e\u003cp\u003eThe maximum daily development depth of floor MS events was plotted as a point-line graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e) to analyze the dynamic relationship between water inflow and floor failure depth during the mining of the 61607 working face. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e, after July 14, the daily maximum floor failure depth increased, reaching the Ordovician strata. In geologically weak zones or areas with pre-existing fractures, thick coal seam mining can cause more intense floor damage, increasing the likelihood of new water-conducting fractures or enhanced water inflow.\u003c/p\u003e\u003cp\u003e(3) Intensity Indicators. As analyzed in Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e4.2.4\u003c/span\u003e, the energy of floor MS events reflects high-energy, large-scale fracturing and its impact on mining-induced stress manifestation, illustrating that MS events energy is closely associated with mining stress and the development of water-conducting channels. Therefore, the daily cumulative energy of floor MS events is selected as the intensity indicator.\u003c/p\u003e\u003cp\u003eBased on the above analysis, five MS events indicators were identified to evaluate the floor water inrush hazard, reflecting the activity, spatiotemporal, and intensity characteristics of floor MS events: daily frequency of floor MS events, daily frequency of MS events in the Benxi Formation and deeper strata, maximum daily development depth of floor MS events, advance influence distance of MS events, and daily cumulative energy of floor MS events.\u003c/p\u003e\u003cp\u003eAll five indicators exhibit a positive correlation with the floor water inrush hazard, i.e., higher values correspond to higher risk.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Weight Calculation of Floor Water Inrush Hazard Indicators\u003c/h2\u003e\u003cp\u003eThe weights of the floor water inrush hazard indicators were determined using both AHP and EWM, representing subjective and objective perspectives, respectively. The composite weights (\u003cem\u003eω\u003c/em\u003e) were then calculated by combining the two methods, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eAccording to the composite weights, the ranking of the indicators from highest to lowest importance is as follows: daily frequency of MS events in the Benxi Formation and deeper strata\u0026thinsp;\u0026gt;\u0026thinsp;advance influence distance of MS events\u0026thinsp;\u0026gt;\u0026thinsp;daily cumulative energy of floor MS events\u0026thinsp;\u0026gt;\u0026thinsp;maximum development depth of floor MS events\u0026thinsp;\u0026gt;\u0026thinsp;daily frequency of floor MS events.\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\u003eTable of Weight Calculation Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003eAHP\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003eEWM\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003eAHP\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;\u003cem\u003eω\u003c/em\u003e\u003csub\u003e\u003cem\u003eEWM\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eω\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily cumulative energy of floor MS events\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.010145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily frequency of MS events in the Benxi Formation and deeper strata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.107738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.520538\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily cumulative energy of floor MS events\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.2478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.114464\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdvance influence distance of MS events\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.040111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.193799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily cumulative energy of floor MS events\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.025289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.122184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Comprehensive Evaluation of Floor Water Inrush Hazard\u003c/h2\u003e\u003cp\u003eThe floor water inrush hazard evaluation model was established by combining the composite weights of each indicator with their quantitative normalized values as follows:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({I_i}=0.072280{x_{i1}}+0.489597{x_{i2}}+0.026968{x_{i3}}+0.260178{x_{i4}}+0.150976{x_{i5}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe daily comprehensive hazard index was calculated and compared with the observed water inflow data of the working face, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e. The comprehensive index was classified into risk levels using equal interval division: low risk (0\u0026ndash;0.3), medium risk (0.3\u0026ndash;0.6) and high risk (0.6\u0026ndash;0.9).\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e, from April to June, the comprehensive index remained within the low-risk range, consistent with stable water inflow. In late June, some daily indices entered the medium-risk range, accompanied by minor fluctuations in water inflow. From July 1 to 21, the comprehensive index reached the high-risk range, with a peak on July 14, corresponding to the maximum observed water inflow. In August, some indices reached the medium-risk range, while individual indices reached high-risk levels. During this period, water inflow increased significantly, and as the working face entered the \u0026ldquo;secondary square\u0026rdquo; stage, mining-induced stress further enhanced floor failure depth, increasing the water inrush hazard.\u003c/p\u003e\u003cp\u003eThe evaluation model was further applied to MS events data from February to June 2024 for calculation and validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e). The results show that the comprehensive hazard index correlates well with observed water inflow variations, demonstrating that the model can be effectively used for early warning of floor water inrush.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5 Discussion and Outlook","content":"\u003cp\u003eThis study takes the Longwanggou Coal Mine in the Zhun\u0026rsquo;geer Coalfield, China as an example and employs MS monitoring technology to investigate the dynamic evolution characteristics of floor MS events during thick coal seam mining. By analyzing the spatiotemporal response characteristics of floor MS events (time, space, and energy), the correlations between various indicators and floor failure were assessed, and an AHP-EWM-based floor water inrush hazard evaluation model was established, providing guidance for the mining of thick coal seams. In fact, floor fracturing induced by thick coal seam mining results from the dynamic coupling of mining-induced stress, surrounding rock properties, and stratigraphic structure, making it more complex than thin- or medium-thickness coal seam mining. This study provides a preliminary investigation based on MS monitoring results.\u003c/p\u003e\u003cp\u003e(1) Stress Transfer Characteristics of Floor Fracturing.\u003c/p\u003e\u003cp\u003eIn thick coal seam mining, the larger mined-out space significantly increases the depth and intensity of stress transfer to the floor. The maximum concentrated destruction depth of MS events reaches 26.66 m in the middle and lower Taiyuan Formation, and MS events are still detectable in the Benxi Formation and deeper strata (beyond 34.14 m), accounting for ~\u0026thinsp;8% of total events, contrasting with thin- or medium-thickness seams where floor failure is typically within 10 m. This is primarily because thick coal seam mining induces a wider roof movement and more intense stress release, generating energy release in deep floor strata and forming a vertically layered fracture pattern: shallow dense fractures \u0026rarr; middle gradual fractures \u0026rarr; deep localized fractures. From a water inrush hazard perspective, the floor fracture mechanism directly controls the formation of water-conducting channels. While shallow fractures are dense, they rarely connect to the Ordovician high-pressure aquifer, whereas deep fractures, when combined with aquifer pressure, can trigger a \u0026ldquo;fracture propagation \u0026ndash; hydraulic lift\u0026rdquo; coupled disaster process. The high correlation between daily frequency of MS events in the Benxi Formation and deeper strata and water inrush risk (subjective weight 0.3998, objective weight 0.3315, composite weight 0.5205) confirms that deep floor fracturing is a key early-warning factor, supporting the shift of mitigation focus to deeper strata.\u003c/p\u003e\u003cp\u003e(2) Dynamic Evaluation Using MS Monitoring\u003c/p\u003e\u003cp\u003ePrevious floor water inrush evaluations often relied on static indicators. This study constructs a dynamic indicator system based on MS monitoring, comprising activity, spatiotemporal, and intensity indicators, and applies the AHP-EWM combined weighting method to determine indicator weights. By integrating subjective and objective weights, the reliability and scientific validity of the evaluation are enhanced. The combined weighting results are consistent with field monitoring data, demonstrating the method\u0026rsquo;s effectiveness.\u003c/p\u003e\u003cp\u003e(3) Limitations and Future Research Directions\u003c/p\u003e\u003cp\u003eThe current MS monitoring dataset covers a limited temporal span, excluding the full mining cycle (e.g., initial and periodic stress manifestation stages), which may introduce bias in weight calculations. Moreover, the selected evaluation indicators do not consider multi-factor coupling effects, such as rock mechanical properties or grouting reinforcement coverage, potentially causing deviations between predicted and actual water inrush risk under complex geological conditions.\u003c/p\u003e\u003cp\u003eFuture research could focus on:\u003c/p\u003e\u003cp\u003eExtending monitoring duration and expanding the indicator system: Cover the full mining cycle and incorporate factors such as floor rock integrity coefficients and grouting reinforcement effectiveness, constructing a \u0026ldquo;dynamic MS \u0026ndash; static geological \u0026ndash; engineering intervention\u0026rdquo; multidimensional evaluation system to improve comprehensiveness.\u003c/p\u003e\u003cp\u003eMechanism study with numerical simulation: Develop a floor stress-fracture numerical model for thick coal seam mining, and compare the simulated fracture depth and energy distribution with MS monitoring data to reveal the \u0026ldquo;mining-induced stress \u0026ndash; rock fracturing \u0026ndash; hydraulic lift\u0026rdquo; coupled disaster thresholds, providing a theoretical basis for weight optimization.\u003c/p\u003e\u003cp\u003eDevelopment of intelligent early-warning systems: Based on the AHP-EWM evaluation model and real-time MS monitoring data, develop an integrated system of data acquisition \u0026rarr; weight calculation \u0026rarr; risk classification \u0026rarr; early-warning issuance, enabling dynamic and automated water inrush risk prediction, thereby supporting safe and efficient mining of thick coal seams.\u003c/p\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eThis study investigates the dynamic evolution characteristics of floor MS events during thick coal seam mining. Using MS monitoring technology and a comprehensive evaluation method, the spatiotemporal evolution of floor MS events was systematically analyzed, revealing their evolutionary patterns, and MS event characteristics were used as indicators for hazard assessment. The main conclusions are as follows:\u003c/p\u003e\u003cp\u003e(1) Spatiotemporal Characteristics of Floor MS Events\u003c/p\u003e\u003cp\u003eContinuous MS monitoring was conducted at the 61607 working face of Longwanggou Coal Mine, and the spatiotemporal response of floor MS events was analyzed. The results indicate that MS events are primarily concentrated in the Taiyuan Formation. As mining progressed, the frequency, depth, spatial extent, and energy of MS events increased. Combined with observed water inflow and mining-induced stress data, indicators such as advance influence zone, floor failure depth, and daily energy release showed clear response patterns, reflecting the floor damage morphology and characteristics under thick coal seam mining.\u003c/p\u003e\u003cp\u003e(2) Key Factors for Early Warning of Floor Water Inrush\u003c/p\u003e\u003cp\u003eBased on the spatiotemporal analysis of floor MS events, thick coal seam mining induced MS events characterized by greater depth, wider spatial distribution, and higher energy release. MS events occurring in the Benxi Formation and deeper strata are critical for identifying deep aquifer disturbances. These events are closely associated with groundwater movement and tectonic activation and provide important early-warning information for MS monitoring systems, effectively enhancing the accuracy of floor water inrush prediction.\u003c/p\u003e\u003cp\u003e(3) Quantitative Evaluation of Floor Water Inrush Hazard\u003c/p\u003e\u003cp\u003eThe study summarizes the key controlling factors of floor water inrush, focusing on MS events activity, spatiotemporal distribution, and intensity characteristics. By integrating multiple influencing indicators and applying the AHP-EWM combined weighting method, a quantitative evaluation system for floor water inrush hazard in thick coal seam mining was established. The evaluation results show good agreement with field monitoring data, demonstrating the method\u0026rsquo;s reliability and practical applicability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author gratefully acknowledges the funding and assistance from proof-readers and editors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that all data presented in this manuscript are available to researchers for noncommercial use, provided participant confidentiality is maintained. Data requests can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaorui Wang conceived the research and wrote the initial draft of the paper, Shangxian Yin and Huiqing Lian provided guidance for the on-site experiments, Min Cao was responsible for data processing and chart drawing, Enke Hou and Xu Wang proofread and translated the paper, and Guofu Xing provided the experimental site. All authors reviewed, revised, and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Key Research and Development Program of China (No. 2022YFC3005905-1); Project under the Central-Guided Fund for Local Science and Technology Development (No. 246Z7604G); Hebei Natural Science Foundation Project (No. D2025508011); The Fundamental Research Funds for the Central Universities (No. 3142025007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experts involved in the experimental part of this study were fully informed of the research objectives, procedures, and intended use of the collected data. Their participation was entirely voluntary, and each provided explicit informed consent before taking part in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTian, G., Shang, X., Li, J. \u0026amp; Wang, Y. A strategic study of coal exploitation in Shanxi, Shannxi and Inner Mongolia. \u003cem\u003eCoal Geol. China\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, 1\u0026ndash;15 (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, Z. et al. Overburden destruction structure model and mechanism of mine pressure in Caojiatan coal mine with 10 m super large mining height. \u003cem\u003eJ. China Coal Soc\u003c/em\u003e. \u003cb\u003e50\u003c/b\u003e, 1951\u0026ndash;1964 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan, C., Xia, B., Zuo, Y., Yu, B. \u0026amp; Ou, C. 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Using the vulnerable index method to assess the likelihood of a water inrush through the floor of a multi-seam coal mine in China. \u003cem\u003eMine Water Environ.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 54\u0026ndash;60 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng, Y. et al. Vulnerability assessment of water bursting from Ordovician limestone into coal mines of China. \u003cem\u003eEnviron. Earth Sci.\u003c/em\u003e \u003cb\u003e75\u003c/b\u003e, 1431 (2016).\u003c/span\u003e\u003c/li\u003e\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Extra-thick coal seam, Floor failure, Microseismic monitoring, Risk evaluation","lastPublishedDoi":"10.21203/rs.3.rs-7988169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7988169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe mining of extremely thick coal seams causes strong disturbances to the floor strata. When a strong aquifer exists beneath the coal seam and the effective aquiclude is weak, the risk of floor water inrush increases significantly. In recent years, microseismic (MS) monitoring technology has been widely applied, and using MS monitoring to assess floor water inrush risk has proven to be a reliable and effective approach. To address the high-risk problem of floor water inrush during the extraction of extremely thick coal seams, this study employed MS monitoring to investigate the evolution characteristics of floor MS activity and its associated water inrush risk. The MS response characteristics induced by thick seam mining were analyzed, and a floor water inrush risk evaluation model was established based on the Analytic Hierarchy Process\u0026ndash;Entropy Weight Method (AHP\u0026ndash;EWM). The evaluation results were consistent with field monitoring data. The results indicate that most floor MS events in the 61607 working face of the Longwanggou Coal Mine were concentrated in the Taiyuan Formation. The MS monitoring indicators, such as the extent of the advanced influence zone, floor failure depth, and total daily released energy, showed strong correlations with observed data including water inflow and mine pressure. Key controlling factors of floor water inrush risk were summarized, focusing on the activity, spatiotemporal, and intensity characteristics of floor MS events. A quantitative risk evaluation system for floor water inrush during extremely thick seam mining was developed using the AHP\u0026ndash;EWM objective\u0026ndash;subjective weighting method, demonstrating good agreement with field observations. This study provides theoretical and data support for analyzing the dynamic process and influencing factors of floor damage and fracture evolution under thick seam mining. It offers a new approach for predicting and forecasting water inrush risk, enriches the application of MS monitoring in mine floor water hazard prevention, and holds significant practical importance for promoting safe coal mine production.\u003c/p\u003e","manuscriptTitle":"Investigation on the Dynamic Evolution and Evaluation of Floor Microseismic Responses during Extra-Thick Coal Seam Mining","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 16:48:52","doi":"10.21203/rs.3.rs-7988169/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-28T06:44:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T04:59:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T15:28:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29942858538908399576818556889732277482","date":"2025-11-07T13:38:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118089797560978289986787784263012863176","date":"2025-11-07T07:44:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-07T07:43:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-06T22:42:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-06T19:37:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-02T11:20:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-02T11:16:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"188a5893-13ac-42a0-9e53-070955426919","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58090739,"name":"Physical sciences/Engineering"},{"id":58090740,"name":"Earth and environmental sciences/Environmental sciences"},{"id":58090741,"name":"Earth and environmental sciences/Hydrology"},{"id":58090742,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-12-29T16:09:22+00:00","versionOfRecord":{"articleIdentity":"rs-7988169","link":"https://doi.org/10.1038/s41598-025-32746-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-25 15:57:36","publishedOnDateReadable":"December 25th, 2025"},"versionCreatedAt":"2025-11-18 16:48:52","video":"","vorDoi":"10.1038/s41598-025-32746-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-32746-9","workflowStages":[]},"version":"v1","identity":"rs-7988169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7988169","identity":"rs-7988169","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}