Any disturbance in the ventilation system of a mine during a coal and gas outburst can lead to secondary disasters. This is because, on the one hand, the expansion power of the outburst source makes the airflow in the ventilation system countercurrent, causing the gas in this system to exceed the allowable limit. On the other hand, the airflow density changes because of the outburst and the consequent airflow mixing in the mine roadway, thereby changing the natural wind pressure. From this, the concept of natural gas wind pressure is proposed, and a calculation method for this pressure in a 3D model mine ventilation system is derived. For the “11.10” major coal and gas outburst that occurred in Shizhuang Coal Mine in Qujing, Yunnan Province, the entire process of the counter flow and gas dispersion flow in the main and auxiliary shafts is analyzed using the TF1M 3D simulation program, including the dynamic change in the natural wind pressure in the mine in each stage. The simulation shows that during the gas outburst period, the natural gas pressure of the countercurrent circuit is greater than that of the main fan. Between 140 s and 225 s following the outburst, the natural wind pressure once overcomes the fan pressure and reverses the airflow in the 1824 transport roadway, and the gas is withdrawn from the 1727 service point. Evidently, the natural pressure of the gas produced by the outburst affects the mine ventilation system. In the event of a coal and gas outburst in a private village coal mine, if the natural gas wind pressure can be reasonably utilized and the main fan stopped or the entire mine reversed in time, casualties may be minimized. Scholars studying coal and gas outburst mines should pay attention to mine airflow disorder due to the varying natural gas wind pressure when an outburst occurs and perform simulation drills of the outburst beforehand. Thus, a scientific emergency management plan for mine disaster prevention and reduction can be formulated.

The gas pressure generated during a coal and gas outburst is an important factor affecting the stability of mine ventilation systems. The gas released from an outburst flows and diffuses in the mine, leading to an uneven distribution of the air in the mine ventilation system and the formation of natural wind pressure. Because of the effect of the gas pressure, the mine ventilation system becomes disordered, leading to a counter flow in the roadway. This increases the complexity of the gas movement, extends the influence range of the gas, enlarges the disaster area in the mine, and exacerbates the destructiveness. In this study, the TF1M simulation program was applied to simulate a coal and gas outburst accident that occurred in the 1747 heading roadway of the Sizhuang Coal Mine, in a bid to reproduce the entire process of the diffusion flow of the counter current and outburst gas in the mine roadway. Moreover, the dynamic influence of the gas pressure on the entire ventilation network was analyzed. An experimental device was set up to test the relationship between the natural wind pressure and the height difference of the roadway and density of the gas flow, and the formation and mechanism of the natural wind pressure were explored. By analyzing the experimental and numerical results, the variation law of the air flow and the law of gas movement under the influence of gas pressure were summarized. The movement law and influencing factors of the gas during the mine outburst period were studied, the influence range of the gas was determined, the distribution law of the gas concentration after the outburst was obtained, and further expansion of the gas was prevented. This study has theoretical and practical significance in enhancing our understanding of the development process of mine gas disasters, which can help establish effective emergency response strategies and reasonably implement postdisaster relief measures.

Coal and gas outbursts represent a complex and dynamic phenomenon. To further clarify dynamic evolutions of airflow under the effect of gas outburst disasters, an experimental device for simulating gas-induced airflow disturbance under the circumstances of outburst disasters was constructed. With the aid of this device, relevant parameters such as outburst intensity, airflow composition, and wind speed were analyzed based on experimental results. Here are the research findings: (1) Gas acts on the entire process of coal and gas outburst disasters. It plays a dominant role in the outburst development and propagation stages, manifested as the action of gas natural wind pressure. (2) The outburst roadway pressure within the mine ventilation network undergoes a cyclic attenuation process. The first moment when the pressure drops to the atmospheric pressure can be regarded as a criterion for judging when the outburst gas source power no longer works and the gas natural wind pressure starts to act. (3) Outburst gas and ventilation power jointly act on the mine ventilation network, resulting in airflow disturbance and backflow. This reveals that gas natural wind pressure and ventilation power coexist and interact with each other during the disaster.

Analyzing the spatiotemporal characteristics of microseismic monitoring data is crucial for the monitoring and early prediction of coal–rock dynamic disasters during deep mining. Aiming to address the challenges hampering the early prediction of coal–rock dynamic disasters in deep mining, in this paper, we propose a method for analyzing the spatiotemporal characteristics of microseismic events in deep mining based on the ST-DBSCAN algorithm. First, a spatiotemporal distance metric model integrating temporal and spatial distances was constructed to accurately describe the correlations between microseismic events in spatiotemporal dimensions. Second, along with the spatiotemporal distribution characteristics of microseismic data, we determined the spatiotemporal neighborhood parameters suitable for deep-mining environments. Finally, we conducted clustering analysis of 14 sets of actual microseismic monitoring data from the Xinjulong Coal Mine. The results demonstrate the precise identification of two characteristic clusters, namely middle-layer mining disturbances and deep-seated activities, along with isolated high-magnitude events posing significant risks.

Gas accumulation triggered by coal and gas outbursts is the core cause of secondary disasters in coal mines. This study focuses on the risk assessment of gas accumulation during disaster scenarios, proposing a multidimensional evaluation method integrating the analytic hierarchy process (AHP), information entropy theory, kernel density estimation, and dynamic risk propagation modeling. A unified intelligent prevention system encompassing “monitoring–prediction–decision making” is established. Leveraging the TFIM3D simulation platform and case studies from the Qunli Coal Mine accident, this research reveals spatiotemporal evolution patterns of gas concentration and explosion risk thresholds. A ventilation optimization strategy based on risk classification is proposed. The results demonstrate that the dynamic risk index (DRI), derived from the coupling of the roadway air volume stability coefficient and gas concentration information entropy, can accurately identify high-risk zones. The findings provide theoretical foundations and practical pathways for dynamic risk management in ventilation systems during coal and gas outburst disasters.

Petroleum storage and transport systems necessitate robust safety measures to mitigate oil spill risks threatening marine ecosystems and sustainable development through ecological and socioeconomic safeguards. We aimed to gain a deeper understanding of the evolution patterns of accidents and effectively mitigate risks. An improved risk assessment method that combines the Bow-Tie (BT) theory and Dynamic Bayesian theory was applied to evaluate the safety risks of petroleum storage and transportation facilities. Additionally, a scenario modeling approach was utilized to construct a model of the event chain resulting from accidents, facilitating quantitative analysis and risk prediction. By constructing an accident chain based on fault trees, the BT model was converted into a Bayesian Network (BN) model. A Dynamic Bayesian Network (DBN) model was established by incorporating time series parameters into the static Bayesian model, enabling the dynamic risk assessment of an oil storage and transportation base in the Zhoushan archipelago. This study quantitatively analyzes the dynamic risk propagation process of storage tank leakage, establishing time-dependent risk probability profiles. The results demonstrate an initial leakage probability of 0.015, with risk magnitude doubling for the temporal progression and concurrent probabilistic escalation of secondary hazards, including fire or explosion scenarios. A novel risk transition framework for the consequences of petrochemical leaks has been developed, providing a predictive paradigm for risk evolution trajectories and offering critical theoretical and practical references for emergency response optimization.