Clastic rock aquifer of the coal seam roof often constitutes the direct water-filling aquifer of the coal seam and its water-richness is closely related to the risk of roof water inrush. Therefore, the evaluation of the water-richness of clastic rock aquifer is the basic work of coal seam roof water disaster prevention. This article took the 4th coal seam in Huafeng mine field as an example. It combined the empirical formula method and generalized regression neural network (GRNN) to calculate the development height of water-conducting fracture zone, determined the vertical spatial range of water-richness evaluation. Depth of the sandstone floor, brittle rock ratio, lithological structure index, fault strength index, and fault intersections and endpoints density were selected as the main controlling factors. A combination weighting method based on the analytic hierarchy process (AHP), rough set theory (RS), and minimum deviation method (MD) was proposed to determine the weight of the main controlling factors. Introduced the theory of unascertained measures and confidence recognition criteria to construct an evaluation model for the water-richness of clastic rock aquifers, the study area was divided into three zones: relatively weak water-richness zones, medium water-richness zones, and relatively strong water-richness zones. By comparing with the water inrush points and the water inflow of workfaces, the evaluation model's water yield zoning was consistent with the actual situation, and the prediction effect was good. This provided a new idea for the evaluation of the water-richness of the clastic rock aquifer on the roof of the mining coal seam.

Water richness evaluation of coal seam roofs is a crucial prerequisite for preventing and controlling water hazards in coal seam roofs. For this purpose, Spearman correlation and GeoDetector were employed for factor optimization to investigate the significance of lithological and structural factors and the impact of interactions among different factors on the water richness of coal seam roofs. Water richness evaluation models of coal seam roofs were separately established via the entropy weight method (EWM), coefficient of variation method (CVM), and random forest method (RFM), and the predictive accuracies of these models were compared. Eleven lithological and structural factors were collected. Through Spearman correlation analysis, 6 factors were identified to have significant influences on water richness. By utilizing the interaction detection of GeoDetector, the effects of interfactor interactions on water richness were explored, and 3 combination factors were selected. AICc was used to determine the model’s superiority. The evaluation results of the study area based on factor optimization and three methods were further compared and validated via pumping tests, workface water inflow tests, and three-dimensional high-density electrical method. The results indicated that the RFM exhibited higher prediction accuracy than did the entropy weight and coefficient of variation methods. Additionally, within each method, factor optimization led to improved model accuracy.

Analyzing and fusing information layers of exploratory parameters is a critical step for enhancing the accuracy of identifying mineral potential zones during the reconnaissance stage of mineral exploration. The Qixia area in Shandong Province is characterized by intricate geological structures and abundant mineral resources. Numerous gold polymetallic deposits have been discovered in this region, highlighting the potential for discovering more such deposits in the ore concentration zone and its adjacent areas. In this study, we focus on the Qixia area and employ the box dimension method to analyze the fractal dimension of fault structures. We investigate the relationship between orebody occurrence and fault incidence within the mining region. Furthermore, we combine fractal analysis with Fry analysis to comprehensively predict the metallogenic potential in the area. This study reveals the fractal dimension values of fault structures, demonstrating that fault structures govern the distribution of ore bodies, with NE and NW fault structures being the primary ore-hosting features. Based on thorough analysis, we hypothesize that gold deposits in this area are generally distributed along the northeastern direction. By considering mineral distribution characteristics, this study identifies five potential metallogenic prospect areas within the study region. Capitalizing on advancements in information technology and big data, digital geology has gained prominence in prospecting and prediction. To this end, we construct a multi-information comprehensive prospecting model based on the structure-geochemical anomaly-mineralization alteration, employing the convolutional neural network (CNN) model for quantitative estimation of regional gold mineral resources. The findings validate the CNN model’s robust prediction performance in this area, leading to the determination of five prediction prospects. We observe a notable congruence between the two methods, offering significant insights for subsequent exploration endeavors in the region.

Water inrush from underlying aquifers under the synergistic action of complex faults and mining activities seriously threatens the mining of coal seams in North China. The failure characteristics of the coal seam floor under complex fault conditions are the keys to understanding the water inrush mechanism during mining. To investigate floor failure characteristics and water inrush mechanisms influenced by complex fault structures, theoretical analysis and numerical simulation were carried out. An improved theoretical model is first proposed, including complex fault structures such as horst, graben, and step fault structures. The calculation formula for waterproof coal pillars under complex fault conditions is established based on the improved theoretical model. The failure and displacement characteristics of the coal floor under complex faults are analyzed via numerical simulation. Compared with those under normal mining conditions, the theoretical and numerical simulation results reveal the following: (1) Under the horst structure, the floor failure characteristics show an increase in the compression failure area on both sides, and shear failure occurs on the large-dip fault side. The simulation results reveal that the failure width on the large-dip fault side increases by 25%, indicating a failure trend toward the large-dip fault side. The width of waterproof coal pillars needs to be increased by 20% to ensure mining safety. (2) Under the graben structure, the floor failure characteristics show an increase in the compression failure area on both sides, and shear failure occurs on the small-dip fault side. The simulation results reveal that the failure width on the small-dip fault side increases by 25%, indicating a failure trend toward the small-dip fault side. The width of waterproof coal pillars needs to be increased by 40% to ensure mining safety. (3) Under the step fault structure, the floor failure characteristics show compression failure on both sides, increasing the floor compression failure area on the large-dip fault side. The simulation results show that the failure width on the small-dip fault side increases by 15%, the large-dip fault side increases by 50%, and the floor failure depth increases by 20%. The width of waterproof coal pillars needs to be increased by 50% to ensure mining safety.

Significant differences exist in the development height of water-conducting fracture zones (WCFZ) and their relationships with influencing factors between Carboniferous-Permian coal seams in central-eastern China and Jurassic coal seams in the western region. Utilizing 147 measured WCFZ height samples from Carboniferous-Permian seams and 111 from Jurassic seams, five influencing factors were selected: mining height, mining depth, working face slope length, hard rock ratio coefficient, and mining method. A Bayesian-optimized random forest regression model (BO-RFR) was then constructed to predict WCFZ heights, with the contributions of individual factors assessed through SHAP (SHapley Additive exPlanations) values. SHAP analysis indicated that, for Carboniferous-Permian seams, the factors influence WCFZ height in descending order as mining height, hard rock ratio coefficient, mining depth, working face slope length, and mining method. Conversely, for Jurassic seams, the ranking is mining height, mining depth, hard rock ratio coefficient, mining method, and working face slope length. This study not only validates the superior predictive capability of the BO-RFR model for WCFZ height but also systematically elucidates the differing mechanisms by which these factors impact WCFZ development across coal seams of two distinct depositional eras. The findings provide targeted theoretical support and practical decision-making guidance for the safety assessment of water-bearing coal mining in central-eastern and western mining regions of China.