Yatao Li

To enhance the energy efficiency and energy density of physical energy storage (PES) systems, this study introduces a novel hybrid advanced adiabatic compressed air and gravity energy storage (HAACAGES) system. This system integrates advanced adiabatic compressed air energy storage (AA-CAES) and gravity energy storage (GES) components, utilizing the mass of the compressed air (denoted by Δ m ) to make the HAACAGES system overwhelm the capabilities of individual systems. A numerical analysis model was established to validate the proposed system. Results demonstrate the HAACAGES system outperforms the individual systems in terms of both energy efficiency and density. Specifically, the energy efficiency is always higher than the sum of the individual systems at altitudes from 0 to 3 km. At 3 km altitude, the energy density reaches nearly 1.5 times that of conventional AA-CAES systems. At 1 km altitude, it exceeds 10 times that of conventional GES systems. Overall, the HAACAGES system shows clear potential as a promising solution for the next generation of large-scale PES systems.

With the progressive depletion of coal resources, the recovery of shaft pillars has become an important means of improving resource utilization and reducing waste. Taking the main shaft pillar recovery of the Longxiang Coal Mine at the stage of mine closure as the engineering background, this study systematically investigates ground subsidence prediction and shaft stability control under strip mining with symmetrical extraction. An improved subsidence prediction model was established by integrating the probability integral method with superposition theory, and its validity was verified through numerical simulations and field monitoring data. The results demonstrate that the proposed method can accurately capture the subsidence behavior under complex geological conditions, with prediction errors ranging from 6.4 mm to 399.1 mm. In fully subsided zones, the percentage error was as low as 1.1–3.5%, while larger deviations were observed in areas where subsidence was incomplete, confirming both the reliability and the practical limitations of the method under different conditions. Furthermore, the deformation mechanisms of the shaft during pillar recovery were analyzed. Monitoring results indicated that the maximum subsidence at the east and west sides of the shaft reached 7620.6 mm, accompanied by local cracks exceeding 1500 mm, which caused significant damage to surface structures. To address these risks, a safety control scheme based on an integrated “prediction–monitoring–control” framework is proposed, including shaft wall reinforcement, optimization of mining parameters, and continuous ground subsidence monitoring. By combining real-time monitoring with the superposition of small working face predictions, the scheme enables maximum recovery of shaft pillar coal while ensuring operational safety. This study provides a scientific basis and technical support for shaft pillar recovery in Longxiang Coal Mine and offers valuable theoretical guidance for similar mine closure projects, with significant implications for engineering practice.

Seismic forecasting remains constrained by surface noise and low spatial resolution, limiting reproducible predictions. Although deep borehole stations and underground laboratories improve conditions, they face high costs, sparse coverage, and narrow disciplinary scope. In this work, the strategic reuse of deep closed mines as seismic forecasting laboratories was evaluated. Closed mines, abundant and deep with extensive tunnels and reusable infrastructure, provide ideal low-noise, near-source environments for scalable observation networks. They can lower construction costs, enable simultaneous monitoring of natural and induced earthquakes, and support comparative studies of source mechanisms and forecasting methods. Key challenges include processing massive data volumes, integrating multi-source information, and ensuring equipment reliability in harsh environments. Future directions emphasize building three-dimensional, multiphysics monitoring networks, advancing interdisciplinary and international collaboration, and developing an integrated “observation–warning–prevention” platform. Repurposing closed mines not only expands underground space utilization but also offers a potential paradigm shift in seismic monitoring, providing a novel pathway to overcome longstanding forecasting bottlenecks. Document Type: Perspective Cited as: Li, P., Tang, C., Li, Y., Zhang, Y., Gorjian, M., Cai, M. Repurposing deep closed mines as seismic forecasting research platforms. Advances in Geo-Energy Research, 2025, 18(1): 1-6. https://doi.org/10.46690/ager.2025.10.01

Shear strain energy, a critical factor in the occurrence of earthquakes and rockbursts, plays a vital role in deep mining operations. This study investigates the spatial distribution of shear strain energy (E_s) in mining-induced fault coseismic slip and its implications for rockburst risk assessment, offering a novel perspective. We thoroughly explore the dynamics of E_s, which are critical to seismic activity and rockburst phenomena in deep mining operations. By integrating advanced numerical simulation techniques with observational data from the F16 fault zone, we analyze the interplay among mining distance (D_m), fault cohesion, and their collective impact on E_s variations. Our analysis reveals a nuanced understanding of E_s in mining induced fault slip, particularly highlighting a marked increase in E_s concentrations at the working face as mining approaches the fault. This observation underscores the critical influence of D_m on elevating rockburst risks. Additionally, we discover that enhanced fault cohesion contributes to a decrease in E_s, thereby mitigating rockburst risks. These insights afford a novel perspective on managing rockburst hazards in deep mining operations, offering theoretical and methodological advancements for predicting and curtailing geological hazards.

This study presents a comprehensive numerical analysis of dynamic rupture and fault-induced seismic risks in deep roadway excavation environments, focusing on near-fault conditions. A quantitative assessment was conducted for faults within 5 m of the roadway, a critical range for evaluating heightened seismic risks. Our results demonstrate that within this proximity, fault slip and seismic moment increase significantly, with peak fault slip reaching 17.1 mm and seismic moment exceeding 3.9 × 10¹⁰ Nm. One of the key findings is the amplification effect of wave reflections along the exposed roadway surfaces, particularly in areas near the roof and sidewall. The peak particle velocity (PPV) on the roof reached 0.40 m/s, while the right sidewall recorded 0.49 m/s, highlighting elevated seismic impacts in these critical regions. The study also validated the linear slip weakening model, confirming its effectiveness in capturing the transition from static to dynamic friction during rupture and providing theoretical grounding for observed fault behavior. This research contributes novel insights into the fault-induced dynamic rupture process, particularly in ultra-close fault conditions, and emphasizes the need to account for fault proximity, frictional properties, and wave reflection in seismic hazard assessments. Our findings offer a foundation for improving seismic-resistant design in deep excavation operations, particularly in faulted geological settings.