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In order to deeply explore the current situation of coal mine water disasters in China and the characteristics of its related factors, the number of serious and major water disasters and the number of deaths in coal mines since the founding of the People’s Republic of China have been counted, and the mathematical statistics method has been used to analyze the correlation degree of major and serious coal mine water disasters with eight factors including raw coal production, water inrush source, occurrence time, occurrence area, changes in laws and regulations, market supervision system mechanism, scientific researchinvestment, education and training, the nature of coal mining enterprises is analyzed. At the same time, the causes of mine water accidents are summarized and analyzed, and the technical countermeasures and management measures are proposed. The research results show that: since the founding of the People’s Republic of China, major coal mine water accidents can be divided into five stages: low frequency period, abnormal peak period, continuous rising period, high fluctuation period, and stable falling period; April to November is the period of high incidence of water accidents, and August is the month of most accidents; Henan, Hunan, Guizhou, Shanxi and Heilongjiang are the places where coal mine flooding accidents occur frequently; goaf water is the main source of water inrush in water disasters; township coal mine is the most serious disaster area of water accident, but the overall trend is downward; changes in laws and regulations, market supervision system and mechanism, scientific research investment, education and training, and other factors also directly affect the frequency of major coal mine water disasters in China.

The underground environment is complex and superimposed with the uncertainty and nonlinearity of the mine water disaster. The situation of water disaster inundation is changeable and difficult to predict. Regarding the difficulty of obtaining the inundation situation in the roadway at various time points during a flood and quickly determining the escape path based on the inundation situation, in this study, the “bucket principle” is used to construct the submergence path tree, determine the direction of the roadway flow bifurcation, analyze the progressive law of the mine water inrush submersion, simulate the water flow in the underground roadway, and visually display the real-time water flow change, water level height and escape path. The research shows that according to the constructed inundation path tree, the water level and other inundation parameters obtained by recursion are used as the basis for designing the escape path. The differential and integral progressive dynamic design of the escape path solves the problem that the optimal escape path cannot be planned in real time due to the complex underground environment and different water inrush conditions at different times.

For the problem of low accuracy and applicability of the model prediction in the study of mine water inflow, a method of mine water inflow prediction based on bidirectional short and long memory network（BiLSTM） and deep residual network （DRN） is proposed. First, the data of mine water inflow is processed by wavelet decomposition and normalization to obtain trend item data and detail item data. Secondly, the trend item data was predicted by DRN network method, and the detail item data was predicted by BiLSTM network method. Finally, the two parts of the prediction results will be combined to get the mine water inflow prediction results. The results show that the DRN-BiLSTM model has higher prediction accuracy than a single model, indicating that the model has better generalization.

The collapse of thick-hard roofs after coal has been extracted is not a consequential process in all cases. Rather, it happens due to the augmentation of high stress conducted at depth, followed by a wider range of damage as the floor cracks. The extent and spread of the cracks in the floor indicate the intensity of the collapse, and the mine will be submerged by the high-pressure water of the coal ash. Therefore, it is particularly important to study the mechanism of the combined effect of high stress on the roof and confined aquifer on the deformation and failure of the coal seam mining floor. This study analyzes and compares the impact of thick-hard magmatic rocks on the destruction of thin floor rock layers in coal seams. Plastic theory calculations are used to determine the plastic zone yield length of floor destruction under hard roof conditions, and the location and height of the maximum floor destruction depth are solved. An empirical formula and BP neural network are used to establish a prediction model for floor destruction. The results of the model's prediction of the depth of floor failure were compared with the measured values, with an absolute error of 2.13 m and a residual of 10.3%, which was closer to the true values. The accuracy of the theoretical model and prediction model is verified using numerical simulation and on-site in situ measurements. Based on this, the deformation and destruction forms of the floor under pressure and the water inrush mechanism are summarized for mining under the condition of a thick-hard roof. Thus, the floor is subjected to high vertical stress, accompanied by significant disturbances generated during coal seam mining, resulting in intense working face pressures. The floor near the working face coal wall will experience severe compression and shear deformation and slide towards the goaf. The floor in the goaf is relieved of high vertical stress, and horizontal stress compression will result in shear failure, leading to floor heave and further increasing the height of the floor destruction zone. After the mining of the working face, the goaf will undergo two stages of re-supporting and post-mining compaction. During the re-supporting stage, the floor rock undergoes a transition from high-stress to low-stress conditions, and the instantaneous stress relief will cause plastic deformation and failure in the coal seam floor. The combined action of primary floor fractures and secondary fractures formed during mining can easily create effective water channels. These can connect to the aquifer or water-conducting structures, making them highly dangerous. The main modes of floor water inrush under the condition of a thick-hard roof are as follows: the high-stress mode, inducing a floor destruction zone connected to the water riser zone; the mining damage mode, connecting to water-conducting faults; the mining damage mode, connecting to water collapse columns; and the coupled water inrush mode, between the mining damage zone and the highly pressurized water floor. [ABSTRACT FROM AUTHOR]

The essence of roof water inrush in coal mines fundamentally stems from the development of water-bearing fracture zones, facilitating the intrusion of overlying aquifers and thereby leading to water hazard incidents. Monitoring rock-fracturing conditions through the analysis of microseismic data can, to a certain extent, facilitate the prediction and early warning of water hazards. The water inflow volume stands as the most characteristic type of data in mine water inrush accidents. Hence, we investigated the feasibility of predicting water inrush events through anomalies in microseismic data from the perspective of water inflow volume variations. The data collected from the microseismic monitoring system at the 208 working face were utilized to compute localization information and source parameters. Based on the hydrogeological conditions of the working face, the energy screening range and its calculation grid characteristics were determined, followed by the generation of kernel density cloud maps at different depths. By observing these microseismic kernel density cloud maps, probabilities of roof water-conducting channel formation and potential locations were inferred. Subsequently, based on the positions of these roof water-conducting channels on the planar domain, the extension depth and expansion direction of the water-conducting channels were determined. Utilizing microseismic monitoring data, a quantitative assessment of water inrush risk was conducted, thereby establishing a linkage between microseismic data and water (inrush) data, which are two indirectly related datasets. The height of microseismic events was directly proportional to the trend of water inflow in the working face. In contrast, the occurrence of water inflow events and microseismic events exhibited a specific lag effect, with microseismic events occurring prior to water inrush events. Abnormalities in microseismic monitoring data partially reflect changes in water-conducting channel patterns. When connected with coal seam damage zones, water inrush hazards may occur. Therefore, abnormalities in microseismic monitoring data can be regarded as one of the precursor signals indicating potential floor water inrushes in coal seams. [ABSTRACT FROM AUTHOR]

The pores and fissures in loaded rock masses are the main channels for underground flow, and may cause serious accidents during the development of groundwater resources. This work presents an efficient method for analyzing the microstructure of the loaded rock mass using fractal theory and computed tomography (CT) scanning. A relation between the microstructure features of the sandstone porosity, fractal dimension, and loading stress is developed using an image identification technique. The results demonstrate that the distribution trends of sandstone samples' slice porosities in the xz- and yz-directions are nearly identical, and the distribution in the xy-direction differs significantly from those in xz- and yz-directions. The total and connected porosities increase with the increase of stress, and the change can be fitted to straight lines. The fractal dimensions of the pores change significantly with stress or loading stress in the xy-direction. [ABSTRACT FROM AUTHOR]