Developing a Velocity Model for an Underground Coal Mine Using a Compressed Load Column Seismic Source

Since the first recorded rock burst occurred in England in 1738, more than 20 countries have reported rock bursts (Kabiesz and Makowka 2009; Ortlepp and Stacey 1994; Patynska and Kabiesz 2009; Patynska 2013; Uszko 2009), including Germany, South Africa, Poland, the Czech Republic, Canada, Japan, France, etc. In China, rock bursts have become a common safety issue in underground coal mining. The number of coal mines experiencing rock bursts has increased annually (Jiang et al. 2010; Li et al. 2015). To date, 142 coal mines in China have suffered rock bursts which resulted in large economic losses and heavy casualties. For instance, the rock burst on 15 March 2013 in Junde Coal mine, Hegang City, caused the closure of a 200 m gateway, trapped 24 people, and killed four (Lu et al. 2015). The rock burst on 3 November 2011 in Qianqiu Coal mine, Yima City trapped 75 people underground and killed 10 people (Li et al. 2015). The rock burst on 14 February 2005 in Sunjiawan Coal mine, Fuxin City, caused a serious gas explosion and killed 214 people (State Administration of Work Safety, State Administration of Coalmine Safety 2005). The temporal and spatial evolution of mining-induced tremors reveals the process of initiation, development, and expansion of micro-fractures inside the coal-rock mass together with energy accumulation and release. This process may develop to cause either a tremor or a rock burst (Li et al. 2014; Wang et al. 2013). Tremors, rock bursts, or both are more likely to be induced in high-stress regions and the energy released therein is much higher. Therefore, rock burst risk can be evaluated by locating high-stress, and high-energy, regions. Microseismic (MS) monitoring is applicable when detecting the location and energy of mining-induced tremors. Recently, seismic velocity tomography (SVT) has been widely used for inference of high-stress distribution zones in underground mines by introducing seismic signals received by MS monitoring systems. For instance, Luxbacher et al. (2008) and Hosseini et al. (2012, 2013) conducted SVT by introducing mining-induced seismic signals, and found that high-velocity regions agreed well with high-abutment stress regions as predicted by numerical modelling, both of which were observed to redistribute as the coalface advanced. Dou et al. (2012), Banka and Jaworski (2010), and Lurka (2008) conducted SVT at regular time intervals during longwall mining, and found that rock bursts, or strong seismic events (i.e. tremors in underground mining), mainly occurred in high-velocity regions. Meanwhile, the bursting strain energy (BSE) index, which views rock bursts as a process of energy accumulation and release in the coal-rock mass as mining activities disturb the in situ stress field, was proposed here to characterise the spatial distribution of tremors [refer to Cai et al. (2015) for more details]. It was found that the & Si-yuan Gong libcumt@163.com

The U.S. Bureau of Mines has developed preliminary recommendations for handling materials in low-seam coal mines in an effort to reduce the incidence and cost of low-back pain in underground coal mines. Three main approaches to reducing back injuries are discussed: a) research and development of task specific materials-handling hardware that would reduce the number of manual lifts in underground coal mines, b) examining the supply-handling systems currently in use at low-seam coal mines through task analysis, and c) examining the lifting capacity of low-seam coal miners in the restricted postures that must be used in underground mines. Bureau of Mines research findings and recommendations are presented and discussed.

Industrial and urban activity has inevitably changed the water environment and caused significant impacts on water resources’ quality and quantity. The identification of related impacts is particularly important in the context of increasing water shortages due to climate change. Overlapping industrial impacts and drought occurrence have resulted in the long-lasting deterioration of surface water status. Therefore, the mitigation of negative impacts is crucial for relevant and sustainable water management in river basins. One of the most impactful branches of industry is underground coal mining, which requires dewatering deposits and excavations. Mine waters discharged into rivers have induced significant increases of salinity, while urban wastewaters have increased biogenic contamination in surface waters. Sustainable development goals require water protection, energy transition, and circularity; therefore, coal will be repurposed in favor of alternative sources of energy. The phasing out of coal and cessation of dewatering of mines would rapidly reduce mine waters’ impact on the environment. However, in heavily industrialized urban basins, the share of natural waters in river flows is exceptionally low—due to significant and long-lasting transformations, industrial and urban wastewaters are the main constitutive components in certain river hydrological regimes. The case study of Bytomka in the Upper Silesian Coal Basin, Southern Poland is a vivid example of a river basin significantly impacted by urban and industrial activity over a long-term period. The Bytomka River’s water status and the development of its watershed area is an example of complex and overlapping impacts, wherein sustainable water management requires proper recognition of prevailing factors such as mine water discharges, climate change and drought periods, wastewater impacts, and urbanization of the water basin area. The presented study reveals key findings showing that future coal mine closures would result in significant water resource shortages due to a reduction of mine water discharges, significant biogenic (N and P) pollution increases, and hazards of harmful algal blooms. Therefore, there is an urgent need to increase the retention potential of the watershed, use nature-based solutions, and mitigate negative impacts of the coal mining transition. The increase in treatment capability of industrial wastewater and sewage discharge would help to cope with the natural water vulnerability induced by the impacts of climate change.

Dust is an inherent byproduct of mining activities that raises notable health and safety concerns. Cumulative inhalation of respirable coal mine dust (RCMD) and respirable crystalline silica (RCS) can lead to obstructive lung diseases. Despite considerable efforts to reduce dust exposure by decreasing the permissible exposure limits (PEL) and improving the monitoring techniques, the rate of mine workers with respiratory diseases is still high. The root causes of the high prevalence of respiratory diseases remain unknown. This study aimed to investigate contributing factors in RCMD and RCS dust concentrations in both surface and underground mines. To this end, a data management approach is performed on MSHA’s database between 1989 and 2018 using SQL data management. In this process, all data were grouped by mine ID, and then, categories of interests were defined to conduct statistical analysis using the generalized estimating equation (GEE) model. The total number of 12,537 and 9050 observations for respirable dust concentration are included, respectively, in the U.S. underground and surface mines. Several variables were defined in four categories of interest including mine type, geographic location, mine size, and coal seam height. Hypotheses were developed for each category based on the research model and were tested using multiple linear regression analysis. The results of the analysis indicate higher RCMD concentration in underground compared to RCS concentration which is found to be relatively higher in surface coal mines. In addition, RCMD concentration is seen to be higher in the Interior region while RCS is higher in the Appalachia region. Moreover, mines of small sizes show lower RCMD and higher RCS concentrations. Finally, thin-seam coal has greater RCMD and RCS concentrations compared to thicker seams in both underground and surface mines. In the end, it is demonstrated that RCMD and RCS concentrations in both surface and underground mines have decreased. Therefore, further research is needed to investigate the efficacy of the current mass-concentration-based monitoring system.

Underground coal mining causes ‘damage–fracture–instability’ of the overlying rock in the stope, resulting in movement and deformation of the mined rock mass that eventually extends to the surface, posing serious threats to buildings, transportation, pipelines, etc. To study the evolution rules of the overlying rock and disaster-causing mechanisms after coal seam mining, this paper analysed the surface subsidence of the West 1st District of Linsheng Coal Mine in the southern coalfield, Shenyang, China. Continuous models were initially used to analyse seven mining times in the coal mine. The overlying rock and surface displacement evolution rules were analysed, and then discrete models were used to further study the rock mass deformation and damage mechanism and rules for surface subsidence caused by coal mining. The results showed that the mining of inclined coal seams has a smaller impact on the surface in the shallower direction of the coal seam, but a larger impact on the surface in the deeper direction of the coal seam, as the deeper coal seam mining causes longer tensile fractures cutting the surface. The vertical surface displacement polyline caused by the mining of the upper coal seam with smaller inclination angle and shallow burial depth was slightly symmetrical on both sides, and the surface subsidence caused by each mining was small. In such cases the rock strata disturbance, including tensile and shear fractures and the associated large-scale rock mass subsidence, caused by the coal seam mining is restricted to localized areas. When mining the lower coal seam with a large inclination angle and great burial depth, affected by the surface deformation that had occurred, the surface displacement value becomes larger and appeared asymmetrical like a parabola. The subsidence range at the end of the mining area with greaterburial depth was larger, as additional fractures are formed across the upper rock mass above the coal seams, eventually causing larger areas of rock mass subsidence. The discrete model can more realistically simulate the deformation and damage characteristics of the rock mass, and its simulated maximum surface settlement value caused by rock layer rupture and instability was greater than that of the continuous model. This study will provide a theoretical basis for monitoring, early warning, prevention, control and treatment of geological hazards in coal mining subsidence areas.

Thermal stability prediction of frozen rocks under fluctuant airflow temperature in a vertical shaft based on finite difference and finite element methods

Underground coal mines are susceptible to many kinds of hazards. Mine fire is one of the most vulnerable threats that leads to loss of human lives and other resources inside the mines. Hence, continuous monitoring of underground coal mines is very important for early detection of mine fire. Nowadays, wireless sensor networks are being widely used for monitoring underground coal mines. However, the collected data from sensor nodes are inaccurate in nature, and there is a need for analyzing such data for taking preventive measure. In this article, we propose an optimized fuzzy logic-based fire monitoring system for wireless underground sensor network, which strengthens reliability of making decision in preventing mine fire. However, storing of large fuzzy rule set in the memory constraint sensor nodes and their traversal for every sensor reading are very challenging. Therefore, we use the binary particle swarm optimization (BPSO) algorithm to optimize the proposed fuzzy system that eliminates redundant rules but preserves event detection accuracy of the monitoring system. The proposed system is simulated using BPSO algorithm and Fuzzy Logic Toolbox inbuilt in the MATLAB. The simulation results demonstrate that the proposed system outperforms the existing monitoring systems for underground coal mines.

Regional surface subsidence induced by underground coal mining is very common and is a serious environmental problem in China. The mining subsidence not only causes damages to surface buildings but also change the pattern of surface drainage in a densely urbanized area. Monitoring and Analyzing the spatial distribution of the endangered surface may be helpful for land-use planning and for land reclamation. Interferometry SAR (InSAR) can be used to effectively monitor the succession of the spatial extent and the magnitude of subsidence in coal mining areas. In this paper, the multi-temporal D-InSAR method was applied for the generation of deformation map in coal mining area. With the "Interferometry Coherence Estimation Minimum Span Tree (ICEMST)" model, the optimized SAR images combination for D-InSAR processing for long term surface subsidence monitoring were predicted. With the estimation of ICEMST, several scenes SAR SLC data with time spanned more than half a year and spatial baseline more than 400m long were combined for D-InSAR processing to study the succession of land subsidence induced by underground coal mining and groundwater exploration in the test site, Kailuan coal mine area, a typical mining industrial area in north China, which has 125 years coal mining history. After being processed with the conventional "2 Pass" differential InSAR method, the deformation caused by underground coal mining in the line of sight (LOS) was transformed into vertical subsidence map. The experiment shows that the short time span is more suitable for D-InSAR application in mining areas than the long time span, for the lower correlation due to densely growing vegetation, seasonal changing factors and large water plashes. The time decorrelation, spatial decorrelation and the D-InSAR error resources were analyzed and discussed, and the Connor Reflectors method integrated with D-InSAR and GPS are presented, which will be a key practical technology for information obtain in digital mine.

Underground coal mining in western China causes heavy land subsidence and alters the soil ecology. However, the effects of land subsidence on soil fertility are not currently known, and the key factors governing its impact remain unclear in sandy land. We investigated the effects of land subsidence induced by underground mining on the soil quality in western China. Soil samples were collected at 0–15 cm and 15–30 cm from control and subsidence areas in three coal mines. The results showed that the soil water content (SWC), clay and silt percentage, total nitrogen (TN), dissolved organic carbon (DOC), ammonia nitrogen (NH4+-N), nitrate nitrogen (NO3--N), available phosphorus (AP), and available potassium (AK) of the subsidence areas were significantly lower than those of the control areas. The saccharase, urease, and alkaline phosphatase activities in the subsidence areas decreased compared to those in the control areas, while the sand percentage of soil tended to increase. Soil nutrient contents, bacterial quantities, and activities of soil enzymes were positively correlated with SWC. Redundancy analysis (RDA) showed that the soil particle size distribution, SWC, and electrical conductivity (EC) were the major environmental factors driving changes in soil properties. These results indicated that land subsidence induced by coal mining caused losses in surface soil water and nutrients, and ultimately led to soil quality degradation. Therefore, the reclamation of mining subsidence land might be necessary, especially in arid and semi-arid areas.

Two years of satellite observations were used to quantify methane emissions from coal mines in Queensland, the largest coal-producing state in Australia. The six analyzed surface and underground coal mines are estimated to emit 570 ± 98 Gg a-1 in 2018-2019. Together, they account for 7% of the national coal production while emitting 55 ± 10% of the reported methane emission from coal mining in Australia. Our results indicate that for two of the three locations, our satellite-based estimates are significantly higher than reported to the Australian government. Most remarkably, 40% of the quantified emission came from a single surface mine (Hail Creek) located in a methane-rich coal basin. Our findings call for increased monitoring and investment in methane recovery technologies for both surface and underground mines.

A considerable number of coal mines employed room and pillar mining in the last century in northern China, where the goaf remained stable for a period of time; however, with the increased exposure of coal pillars, their collapse may gradually increase. The stability assessment of these old rooms and pillar goafs is challenging due to their concealment, irregular mining patterns, and the long passage of time. The methodology developed in this study, based on “space-sky-earth” remote sensing such as InSAR to trace historical deformation, the UAV observation of current surface damage, and comparison of mining spaces, can rapidly detect on a large scale the collapse of old goafs and the trend of damage. This study is conducted with an example of a coal mine in Yulin, Northern China, where obtained quantitative surface deformation values were integrated with qualitative surface damage interpretation results, followed by a yearly analysis of the overlying rock movement in accordance with the underground coal mining process. The results show that from 2007 to 2021, corresponding surface deformation and damage occurred following mining progress. However, the room and pillar goaf areas had not undergone any surface deformation, nor had there been incidents of landslides or ground fissures; therefore, it was speculated that no roof collapse had occurred in this region. The surface deformation and damage associated with underground coal mining are complex and influenced by the coal seam occurrence, mining methods, strata lithology, terrain slope, temporal evolution, and anthropogenic modifications. These phenomena are representative of the coal mining area, and this methodology can provide a reference for similar endeavors.

Advancements in machine learning techniques for coal and gas outburst prediction in underground mines

Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

There is an opportunity to reduce methane emissions from Australian, open pit, metallurgical coal (MC) mines and substantially reduce the carbon intensity of a high-value Australian export industry. MC is critical to the production of steel products and is essential to meet international urbanisation and energy system transformation goals. Initial research on this topic, outlined the application of subterranean barriers in coal mining towards the reduction of gas influx and whole-of-life coal mine methane (CMM) emissions in underground applications. Initially, the technology lacked economic viability for a majority of cases and especially after carbon pricing was eliminated in 2014. Today, with the imperative for the industry to reduce emissions, the technology becomes significant in its potential for emissions reduction for both underground and open-cut coal mines.The basis of barriers is similar to naturally occurring barriers (i.e., igneous intrusions or dikes) that have shown effective separation of drained and undrained mine works (as observed in adjoining mines in Central Queensland). Barriers have also been applied in the environmental, civil engineering and oil and gas industries, to manage or restrict subterranean flow patterns or contain contaminants. In this case, subterranean barriers are being implemented in conjunction with mine pre-drainage to reduce methane emissions from an open pit, MC mine. In addition, barriers can be used in underground mining operations.to improve mine pre-drainage and safety as well as reduce methane emissions.This paper describes the design, execution, and evaluation workflows and the relative importance of variables required for a barrier implementation in subterranean applications for open-cut and underground mining applications. This paper details the results of ongoing planning and modelling to implement and assess a barrier application for reducing gas migration from unmined in-seam or underground mine sections into open-cut MC mining operations. Finally, this paper builds on previous research and available technologies and is complementary to current pre-drainage for surface or underground mining operations. Barrier implementation in conjunction with the beneficial use of gas provides a working framework to reduce mine emissions toward necessary methane reductions by 2030.

Strata control plays a critical role in ensuring the safety and stability of undergr ound coal mining operations, especially in India’s increasingly complex geo-mining environments. With the growing shift from conventional to mechanised mining methods, the industry faces intensified challenges related to stress redistribution, reduced pillar stability, and frequent occurrences of roof falls. Strata control is a major challenge in underground coal mining and needs special attention throughout the mining operations. Since ancient times, safety has been given top priority in underground coal mines, where accidents related to strata control, like the fall of the roof and sides, have occurred frequently. These accidents and fatality figures have caused concern for the Indian coal mining industry and have drawn attention to focus on advanced technologies for safe extraction in mines. This paper investigates the primary challenges associated with strata control in underground coal mines and emphasises the importance of real-time monitoring and advanced instrumentation in mitigating these risks. It explores the limitations of traditional support systems, the complexities of predicting rock mass behaviour under dynamic conditions, and the inadequacies of conventional monitoring tools in harsh underground conditions. Furthermore, the study advocates for the adoption of integrated, data-driven approaches involving IoT-enabled sensors, wireless monitoring networks, and AI-based predictive analytics to enhance strata management. Major Findings: The findings underscore the urgent need for research and development in durable instrumentation, improved data interpretation systems, and the development of adaptive support technologies to ensure safer, more efficient, and sustainable underground mining practices in India.