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In previous practice, the differential strain analysis (DSA) method, relied upon for estimating vertical principal stress to resolve geological stress, has shown limitations in reliability. Drawing upon observations of the Kaiser effect in rock samples, we introduce the Kaiser-differential strain analysis (Kaiser-DSA) method, amalgamating the Kaiser effect with the DSA method to assess in-situ stress differentials. Comparative evaluation with the stress relief method reveals that the Kaiser-DSA method demonstrates minimal relative error and generally consistent orientation of the maximum horizontal principal stress. Utilizing the findings from in-situ stress testing, this investigation scrutinizes the distribution patterns of in-situ stress in Yaojie Mining Area and investigates the causative factors contributing to the prevailing in-situ stress field. The study elucidates that Yaojie Mining Area is subject to the influence of compressive tectonic stress fields, resulting in elevated residual stress. Influenced by the Qinghai-Tibet thrust nappe action and the clockwise shear action of the F19 fault, the geostress field in this region predominantly manifests a strike-slip stress state typified by σH>σv>σh. Furthermore, the heightened differential stress within this locale exacerbates deformation and damage to the surrounding rock mass during mining activities, thereby exacerbating the frequency of dynamic disaster events.

The occurrence of mine water inrushes is common and poses significant hazards in various mining areas throughout China. However, the existing regulations lack of specific engineering geological and hydrogeological exploration guidelines tailored to the water inrush from separation layers (WISL). Exploring the methods of engineering geological and hydrogeological exploration and assessment for the WISL can contribute to further enhancing the prevention and control of mine water disasters in China. This paper begins by examining the mechanism behind WISL in coal mines. It analyzes the geological conditions governing the progression of WISL from its inception to full-scale occurrence and categorizes three prevalent types of WISL in China, i.e., dynamic water inrush, hydrostatic water inrush, and mud and sand-carrying water inrush, all originating from separation layers. Subsequently, it identifies “water source”“channel”“force source”and “material source” as pivotal concealed factors dictating the nature and severity of WISL. Then, the concept of a “inrush separation zone” referring to composite stratigraphic layers situated above traditional water-conducting fractured zones is introduced, where the WISL may transpire during mining activities. Furthermore, it presents a method for delineating “inrush separation zone” in coal mines, outlining exploration stages and specifying crucial investigative focal points. The exploration of water damage in upper strata of coal mine should include two stages: first, the exploration of basic engineering geology and hydrogeological conditions of overlying rock should be carried out to evaluate the possibility of water damage in the exploration area and determine the horizon of potential mining overlying rock ; second, regarding the “inrush separation zone” and “source” layer as the exploration target layer, the special investigation of the hidden disaster factors of the upper layer water damage should be conducted to assess the type and intensity of the water damage in the upper layer. The hydrodynamic conditions and the evolution of overburden fractures are investigated during the mining period. Lastly, a comprehensive forecast evaluation model is proposed and constructed on the coordinated evolutionary mechanisms arising from the interaction of causative factors like “water source”“channel”“force source”and “material source”. This model predicts and evaluates the types, locations, and inflows of water inrush at mining faces before operations commence.

AbstractThe magnitude and direction of the in-situ stress field significantly influence the Geodynamic disaster. Taking the Gaojiapu Coal Mine in the northwest Binchang mining area as the study area, the distribution law of the in-situ stress field and its relationship with microseismic events were analyzed and discussed through field geological investigation, in-situ measurement, and numerical simulation. The results show that: (1) The Maximum horizontal principal stress (MaxHPS) is mainly concentrated in the turning end of the fold, the axis of the syncline, the undulation end of the anticline, the footwall of the fault, and the two ends of the fault. (2) The MaxHPS in the footwall area of the fault is greater than that in the hanging wall area. (3) The MaxHPS at the axial part of the syncline is greater than that at the wing part, and the MaxHPS at the wing part is greater than that at the anticline. (4) In areas with high MaxHPS and Lateral pressure coefficient (LPC), the frequency of high-energy microseismic events is high. (5) The 105J high-energy microseismic events more easily occur in areas with MaxHPS greater than 38 MPa and LPC greater than 1.50.

Abstract The assessment of Coal burst risk (CBR) is the premise of bump disaster prevention and control. It is the implementation criterion to guide various rock burst prevention and control measures. The existing static prediction and evaluation methods for CBR cannot be effectively combined with the results of underground dynamic monitoring. This study proposed a mining-induced seismicity information quantification method based on the fractal theory. Deep learning methods were used to construct a deep learning framework of coal burst risk (DLFR) based on the fractal dimension of microseismic information. Gray correlation analysis (GRA), information gain ratio (IGR), and Pearson correlation coefficient are used to screen and compare factors. Statistical evaluation indicators such as macro-F1, accuracy rate, and fitness curve were used to evaluate model performance. Taking the Gaojiapu coal mine as a case study, the performance of deep learning models such as BP Neural Network (BP), Support Vector Machine (SVM) and its optimized model based on particle swarm optimization (PSO) algorithm under this framework is discussed. The research results' reliability and validity are verified by comparing the predicted results with the actual results. The research results show that the prediction results of CBR in DLFR are consistent with the actual results, and the model is reliable and effective. The mining-induced seismicity quantification can solve the problem of insufficient training samples for the CBR. With this, different pressure relief measures can be formulated based on the results of the CBR predictions to achieve "graded" precise prevention and control.

Rockburst is one of the main dynamic disasters in coal mines; it is of great significance to accurately evaluate the risk zoning of rockburst for mine safety construction and pressure relief design. Taking the Gaojiapu coal mine in the Bingchang mining area of Shaanxi Province as the study area, this paper quantifies the complex geological structure based on fractal theory and establishes the zoning evaluation model of rockburst risk index in the mining area by LS-FAHP-CRITIC method in combination with the distribution characteristics of field stress field, the distribution of geological structure, and the sedimentary environment of coal seam overburden. A new evaluation method of rockburst risk zoning in the coal mine is proposed, and the rockburst risk in the study area was evaluated. The results show that the dangerous areas in the study area are widely distributed, mainly in the area near the structure with high maximum horizontal principal stress. On the basis of the actual rockburst of the Gaojiapu coal mine, the areas where rockburst occurs are all located in dangerous area and high dangerous area, which verifies the accuracy and rationality of the zoning evaluation results of the new method. This method can be used to guide the safety production of similar mines or adjacent coalfaces.

Accurately determining the favorable areas of geothermal resources and selecting the target positions of exploration wells are extremely important for exploration and efficient development. This study used the Pearson correlation coefficient and Gini gain to analyze five influencing factors related to the presence of economically viable geothermal potential. The evaluation model of the favorable areas was constructed by using different Machine Learning (ML) methods: Bayesian classifier (Bayes), Support Vector Machine, Bootstrap Aggregating (Bagging), BP neural network, Decision Tree and Logistic Regression classification. The quality of each model was verified by statistical evaluation indicators: Accuracy (ACC), F1 score (F1) and Receiver Operating Characteristic curve (ROC curve). The methodology was applied to the case study of Xinjiang Uygur Autonomous Region, China. Due to the results obtained, all ML models showed strong prediction and classification performance on the target area selection of geothermal exploration, as evidenced by each model’s metrics: the ACC was above 80%, the F1 was above 0.8, and the Area Under the ROC Curve (AUC) was greater than 0.85. The metrics obtained by the Bagging method were the highest. Finally, the results of the six ML models were combined to classify the study area’s geothermal potential, which was consistent with the available information. This study provides a specific basis and technical support for applying the method in further surveys and campaigns. [ABSTRACT FROM AUTHOR]