Underground Coal Fires Research Articles

Machine learning approach for detection of land subsidence induced by underground coal fire using multi-sensor satellite data

ABSTRACT High-rank coal reserves in Jharia Coalfield (JCF, India), are invariably associated with underground coal fires and land subsidence. This study explores multi-sensor time series satellite data (Landsat 8 OLI and Sentinel-1) through machine learning (ML) to determine the regional ground deformation accompanying coal fires and their contextual relationship. The results show that the highest degree of subsidence is closely associated with the active mine benches with overburden dumps. The relationship between the coal fire and land subsidence parameters is considered as a binary classification problem, explored by calculating the probability of subsidence with a desirable categorical outcome through different ML models. The accuracy of the models is validated using performance metrics that shows that the Random Forest (RF) metrics predict the probability of deformation locations in response to the volume reduction of the burning coal fire and vertical compression due to Overburden Dump (OBD) near active mine benches. The estimated displacement trends have been used to forecast the Autoregressive Integrated Moving Average (ARIMA) method, estimated using Line-of-Sight (LOS) displacement values vary around the best fit within the 95% confidence limits. The trend shows ∼15–25% increase in subsidence compared to the cumulative subsidence.

Study on multi-field coupling characteristics of underground coal combustion and nitrogen injection parameter optimization

Underground coal fires, which are widely distributed and cause serious resource waste and environmental hazards, have become a common concern of the international community. This paper aims to reveal the mechanism of coal spontaneous combustion and the expansion law of underground coal fires. A void rate model from the goaf to the ground surface and a multi-field coupling model for underground coal fires were established. Based on which, the combustion process from the beginning of oxidation to the formation of large-scale fire of the remaining coal in goaf was analyzed. Furthermore, the evolution law of underground coal fire after nitrogen injection and the influence of different nitrogen injection parameters were analyzed. The results show that the coal spontaneous combustion can be divided into three stages: slow reaction stage (stage Ⅰ), violent combustion stage (stage Ⅱ), and stable combustion stage (stage Ⅲ), and the transformation of different stages is mainly controlled by the oxidation reaction rate of coal. To obtain the optimal parameters for nitrogen injection for fire suppression, the fire extinguishing efficiency concerning nitrogen injection time, nitrogen injection amount and nitrogen injection position, as well as the distribution law of nitrogen in rock strata were analyzed. Through range analysis, it is determined that the most effective fire extinguishing occurs with the injection rate of 0.15 m3/s in stage I, and the injection position has little influence on the fire extinguishing effect. This understanding contributes to a deeper understanding of the multi-field coupling mechanism of underground coal fires and provides a guidance for extinguishing and preventing underground coal fires.

Dynamic correlation between surface carbon response and underlying emissions from spontaneous combustion goaf: field study of an abandoned coal mine

Abandoned coal mine goaf is affected by air leakages and prone to spontaneous combustion, resulting in environmental pollution and geological disasters. Haizhou Open-pit Mine adopts both underground and open-pit mining methods. During the long-term mining process, the original stable stratum structure is constantly destroyed, and the slope slides, increasing cracks and severe air leakage around the goaf and roadway. The spontaneous combustion of coal is particularly prominent after the mine shut down. At present, there is no suitable indirect monitoring method to effectively explore the spontaneous combustion area in goaf. The study developed an all-weather monitoring plan and conducted multi-point continuous long-term measurements of the spontaneous combustion state in one abandoned coal mine goaf located in the eastern part of the Haizhou Open-pit Mine. We evaluated the dynamic correlation between surface CO2 flux (SCF) and changes in the underground fire areas, determined the scope and evolution trend of the fire areas, and identified the distribution and change laws of SCF. The results show a significant positive correlation between SCF and soil temperature; moreover, the SCF value was found to reflect the CO2 emission intensity of the goaf. The high SCF in the test area showed month-wise expansion and increase, while the CO2 emission gradually increased monthly, and the calculated annual total emission was approximately 7017 t. Hence, the study can further provide guidance for the monitoring of spontaneous combustion in shallow coal seams, goaf and the assessment of CO2 emissions from underground coal fires through the on-site monitoring and analysis results.

Advance in Detection and Management for Underground Coal Fires: A Global Technological Overview

ABSTRACT The ongoing spontaneous combustion of coal seams beneath the earth’s surface leads to the exhaustion of nonrenewable resources and poses a substantial threat to environmental integrity. Precise and efficacious monitoring of subsurface coal fire activities is an indispensable precondition for the prevention and management of coalfield conflagrations, as well as for the exploitation of geothermal energy resources. The accurate detection and localization of covert coal fires depend on the procurement and analytical assessment of distribution data for parameters that are intrinsically linked to the activities associated with coal combustion. To this end, our review work investigated the theoretical foundations, application effects, and inherent limitations of the diverse detection techniques currently available. It has been observed that the ambiguities inherent to individual detection tools can be effectively mitigated through the cross validation of findings derived from multiple detection tools. The role of detection tools can be extended to the entire process of coal fire management, yet the distinct contributions of each tool throughout the various stages of the process warrant further investigation and elucidation. In addition, the potential of emerging technologies such as machine learning algorithms and 5 G networks to promote automation and intelligence in coal fire management work was also discussed. It is our hope that the insights presented herein will serve as a valuable resource for policymakers and stakeholders in the formulation of effective strategies for the prevention and control of coalfield fires.

Seven years of microbial community metagenomes from temperate soils affected by an ongoing coal seam fire.

We examined the dynamics of soil microbiomes under heat press disturbance from an underground coal mine fire in Centralia, PA. Here, we present metagenomic sequencing and assembly data from soil microbiomes across seven consecutive years at repeatedly sampled fire-affected sites along with unaffected reference sites.

Characterization of refractory aerosol particles collected in the tropical upper troposphere–lower stratosphere (UTLS) within the Asian tropopause aerosol layer (ATAL)

Abstract. Aerosol particles with diameters larger than 40 nm were collected during the flight campaign StratoClim 2017 within the Asian tropopause aerosol layer (ATAL) of the 2017 monsoon anticyclone above the Indian subcontinent. A multi-impactor system was installed on board the aircraft M-55 Geophysica, which was operated from Kathmandu, Nepal. The size and chemical composition of more than 5000 refractory particles/inclusions of 17 selected particle samples from seven different flights were analyzed by use of scanning electron microscopy (SEM) and transmission electron microscopy (TEM) combined with energy dispersive X-ray (EDX) microanalysis. Based on chemical composition and morphology, the refractory particles were assigned to the following particle groups: extraterrestrial, silicates, Fe-rich, Al-rich, Hg-rich, other metals, C-rich, soot, Cl-rich, and Ca-rich. Most abundant particle groups within the refractory particles are silicates and C-rich (non-volatile organics). In samples taken above the tropopause, extraterrestrial particles are becoming increasingly important with rising altitude. The most frequent particle sources for the small (maximum in size distribution DP-max=120 nm) refractory particles carried into the ATAL are combustion processes at the ground (burning of fossil fuels/biomass burning) and the agitation of soil material. The refractory particles in the ATAL represent only a very small fraction (< 2 % by number for particles > 40 nm) of the total aerosol particles, which are dominated by species like ammonium, sulfate, nitrate, and volatile organics. During one flight, a large number of very small (DP-max=25 nm) cinnabar particles (HgS) were detected, which are supposed to originate from a ground source such as coal combustion or underground coal fires.

Monitoring and Analysis of the Collapse at Xinjing Open-Pit Mine, Inner Mongolia, China, Using Multi-Source Remote Sensing

The collapse of open-pit coal mine slopes is a kind of severe geological hazard that may cause resource waste, economic loss, and casualties. On 22 February 2023, a large-scale collapse occurred at the Xinjing Open-Pit Mine in Inner Mongolia, China, leading to the loss of 53 lives. Thus, monitoring of the slope stability is important for preventing similar potential damage. It is difficult to fully obtain the temporal and spatial information of the whole mining area using conventional ground monitoring technologies. Therefore, in this study, multi-source remote sensing methods, combined with local geological conditions, are employed to monitor the open-pit mine and analyze the causes of the accident. Firstly, based on GF-2 data, remote sensing interpretation methods are used to locate and analyze the collapse area. The results indicate that high-resolution remote sensing can delineate the collapse boundary, supporting the post-disaster rescue. Subsequently, multi-temporal Radarsat-2 and Sentinel-1A satellite data, covering the period from mining to collapse, are integrated with D-InSAR and DS-InSAR technologies to monitor the deformation of both the collapse areas and the potential risk to dump slopes. The D-InSAR result suggests that high-intensity open-pit mining may be the dominant factor affecting deformation. Furthermore, the boundary between the collapse trailing edge and the non-collapse area could be found in the DS-InSAR result. Moreover, various data sources, including DEM and geological data, are combined to analyze the causes and trends of the deformation. The results suggest that the dump slopes are stable. Meanwhile, the deformation trends of the collapse slope indicate that there may be faults or joint surfaces of the collapse trailing edge boundary. The slope angle exceeding the designed value during the mining is the main cause of the collapse. In addition, the thawing of soil moisture caused by the increase in temperature and the reduction in the mechanical properties of the rock and soil due to underground voids and coal fires also contributed to the accident. This study demonstrates that multi-source remote sensing technologies can quickly and accurately identify potential high-risk areas, which is of great significance for pre-disaster warning and post-disaster rescue.

Catalyzing net-zero carbon strategies: Enhancing CO2 flux Prediction from underground coal fires using optimized machine learning models

Underground coal fires release substantial carbon dioxide (CO2), posing significant environmental and health threats. Accurate prediction of surface CO2 emissions in these areas is crucial for understanding combustion zones and contributes to the global net zero carbon strategy. Traditional data analysis methods have been inadequate for CO2 flux prediction, highlighting the necessity for advanced machine learning (ML) techniques. This study introduces four optimized ML models—General Regression Neural Networks (GRNNs) and Radial Basis Function Neural Networks (RBFNNs) coupled with Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA)—to rapidly predict CO2 flux in areas affected by underground coal fires. Utilizing 223 field test samples, these models consider six key variables: soil temperature at 30 cm depth (ST-30), ambient pressure/temperature/humidity (AP/AT/AH), soil water content (SWC), and wind speed (A-WS). The results underscore the superior predictive accuracy of the GRNN model, with an RMSE of 0.074 and an R2 of 0.9995. Sensitivity analysis reveals A-WS and ST-30 as the most influential factors. Compared to traditional methods, these ML models demonstrate enhanced accuracy and efficiency, marking a significant advancement in the field. The study's findings have broader applications beyond underground coal fires, suggesting potential for these ML models in other environmental monitoring contexts, such as emissions tracking in urban areas or integration with satellite data for global environmental assessment. This methodology represents a pivotal step in environmental management and monitoring, offering scalable and adaptable solutions for various ecological challenges. By rapidly and accurately estimating CO2 flux from underground coal fires, this study contributes significantly to achieving the global net zero carbon target and sets a new benchmark in environmental ML applications.

Research on the Intelligent Planning of Mine Fire Evacuation Routes Based on a Multifactor Coupling Analysis

Efficient evacuation route planning during underground coal mine fires is essential to minimize casualties. This study addresses current shortcomings by proposing a real-time method that integrates a multifactor coupling analysis and the optimized multilayer perceptron regressor-shortest path faster algorithm (MSPFA). This research aims to enhance evacuation route planning by overcoming factors such as inadequate consideration, low accuracy, and information lag in existing methods. This study improves the shortest path faster algorithm (SPFA) for dynamic route planning, mitigates the impact of fixed walking speed parameters using the particle swarm algorithm, and selects the optimal model (MLPRegressor) through the Bootstrap algorithm for estimating personnel walking speeds. Validated through smoke-spread experiments, the MSPFA algorithm dynamically adjusts evacuation routes, preventing toxic passages. Visualization via drawing interchange format (DXF) successfully enhances route comprehension. The MSPFA algorithm outperforms the Dijkstra algorithm with a runtime of 78.5 msand a personnel evacuation time of 3344.74 s. This research establishes a theoretical foundation for intelligent evacuation decision making in underground fire disasters. By introducing the MSPFA algorithm, it provides crucial technical support, significantly reducing the risk of casualties during emergencies.

Active–Passive Remote Sensing Identification of Underground Coal Fire Zones With Joint Constraints of Temperature and Surface Deformation Time Series

Active–Passive Remote Sensing Identification of Underground Coal Fire Zones With Joint Constraints of Temperature and Surface Deformation Time Series

Distribution and 13C Signature of Dicarboxylic Acids and Related Compounds in Fine Aerosols Near Underground Coal Fires in North China: Implications for Fossil Origin of Azelaic Acid

AbstractTo understand the characteristics of chemical components and dicarboxylic acids, oxocarboxylic acids and α‐dicarbonyls in atmospheric aerosols at underground coal fire environment, we collected fine aerosols (PM2.5) from Wuhai, Inner Mongolia, North China, where the underground coal fire often occurs. PM2.5 samples were analyzed for the measurements of carbonaceous components, stable carbon isotopic composition (δ13C) of total carbon, and molecular distribution and compound‐specific δ13C of diacids, oxoacids and α‐dicarbonyls. Oxalic acid (C2) was the most abundant species, accounting for 43.2%–63.1% (avg. 50.8%) of total diacids, followed by phthalic acid (Ph) (13.7%). Azelaic acid (C9) was the third most (7.4%) abundant, followed by succinic (C4) and malonic (C3) acids. Significant correlations of C2 diacid with glyoxylic (ωC2) and 3‐oxopropanoic (ωC3) acids and C3 diacid and their δ13C implied that diacids and related compounds were mainly derived from the underground coal fire emissions and subsequent in situ secondary formation. Based on molecular distribution, seasonal pattern and linear relations of C9 diacid with C2, C3, ωC2 and ωC3 as well as with Ph acid and total diacids, together with the comparability and similarity of its δ13C (−27.0 ± 0.9‰) with that of Ph acid and organic matter in coals, respectively, we found that the C9 diacid has been mainly derived from its precursors such as unsaturated fatty acids emitted from underground coal fires and their subsequent in situ photochemical oxidation. However, further research is needed to confirm such an importance of coal combustion for azelaic acid loading in atmospheric aerosols.

Complexity Study on Multi-Field Coupling Systems for Underground Coal Fires

Underground coal fires are a major disaster that needs to be urgently addressed in the coal mining industry, as they can cause waste of resources, environmental pollution, threaten the life and health of species, and cause serious damage to society and the economy. Currently, the research on the prevention and control of underground coal fires focuses more on the technical level and lacks scientific guidance at the methodological level. Based on this research gap, this paper uses CiteSpace 6.1.R6 to analyze the research hotspots and subjects in the field of underground coal fires and gives a comprehensive research overview of its microscopic chemical reaction substance and macroscopic multi-field coupling characteristics. Then, from the methodological level, it summarizes the complex system characteristics of underground coal fires and puts forward the elements and principles for the prevention and control of underground coal fire disasters under the paradigm of complex system research. This study proposes a new way of thinking for the prevention and control of underground coal fires and helps to build a whole-process prevention and control system.

Numerical study on thermal-hydro coupling characteristics of underground coal combustion under heterogeneous void fraction

Coal seam mining will lead to the movement and fissures of the overlying strata, which makes a complex void condition between the mining area and the ground surface. The void condition plays a key role in the occurrence and expansion of underground coal fires. In this study, the void rate model and the coupled thermal-hydro model of the underground coal fire were constructed. The COMSOL Multiphysics was used to study the coupling law of the temperature field and gas flow field of coal seam spontaneous combustion. By setting different oxygen content and inlet gas velocity, the influence of the external environmental parameters on the expansion law of underground coal fire is analyzed. The results show that the void characteristics of the mining area and the overlying strata have an obvious influence on the expansion of underground coal fire, and there is also a two-way coupling effect of the temperature field and gas flow field. The analyses of the influencing parameters show that reducing the oxygen content or increasing the inlet gas velocity will have a certain inhibitory effect on the expansion of underground coal fires, which can reduce the risk of spontaneous combustion.

The evolution characteristics of fractures in overlying rock for underground coal fires

The evolution characteristics of fractures in overlying rock for underground coal fires

Study of the Microstructure of Coal at Different Temperatures and Quantitative Fractal Characterization.

In order to understand the influence of underground coal fires on coal fractures and pores, mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM) are combined to study the development of coal pore and fracture under high-temperature treatment and calculate the fractal dimension to analyze the relationship between the development of coal pore and fracture and the fractal dimension. The results show that the volume of pores and fractures of the coal sample (C200) treated at 200 °C (0.1715 mL/g) is greater than that of the coal sample (C400) treated at 400 °C (0.1209 mL/g), and both are greater than the original coal sample (RC) (0.1135 mL/g). The volume increase is mainly due to mesopores and macropores, and the proportions of mesopores and macropores in C200 were 70.15 and 59.97% in C400. The MIP fractal dimension shows a decreasing trend with the increase of temperature, and the connectivity of coal samples improved with the increase of temperature. The changes in volume and three-dimensional fractal dimension of C200 and C400 showed the opposite trend and are related to the different stress of coal matrix at different temperatures. The experimental SEM images confirm that the connectivity of coal fractures and pores improves with the increase of temperature. Based on the SEM experiment, the larger the fractal dimension, the more complex the surface is. The SEM surface fractal dimensions indicate that the surface fractal dimension of C200 is the smallest and that of C400 is the largest, which is consistent with the observations made by SEM. The combination of the two fractal dimensions is used to characterize the self-similarity of coal using the fractal dimension difference. When the temperature increased to 200 °C, the unordered expansion of the coal sample resulted in the largest fractal dimension difference and the lowest self-similarity. When heated to 400 °C, the fractal dimension difference of the coal sample is the smallest, and the microstructure of coal shows a regular groove-like development.

Research progress and visualization of underground coal fire detection methods.

Underground coal fires are a widespread disaster prevailing in major coal-producing countries globally, posing serious threats to the ecological environment and restricting the safe exploitation of coal mines. The accuracy of underground coal fire detection directly affects the effectiveness of fire control engineering. In this study, we searched 426 articles from the Web of Science database within 2002-2022 as the data foundation and visualized the research contents of the underground coal fire field using VOSviewer and CiteSpace. The results reveal that the investigation of "underground coal fire detection techniques" is currently the focal area of research in this field. Additionally, the "underground coal fire multi-information fusion inversion detection methods" are considered to be the future research trend. Moreover, we reviewed the strengths and weaknesses of various single-indicator inversion detection methods, including the temperature method, gas and radon method, natural potential method, magnetic method, electric method, remote sensing, and geological radar method. Furthermore, we conducted an analysis of the advantages of the multi-information fusion inversion detection methods, which possesses high precision and wide applicability for detecting coal fires, while highlighting the challenges in handling diverse data sources. It is our hope that the research results presented in this paper will provide valuable insights and ideas for researchers involved in the detection and practical research of underground coal fires.

Atmospheric Mercury Isotope Shifts in Response to Mercury Emissions from Underground Coal Fires.

Pollutant emissions from coal fires have caused serious concerns in major coal-producing countries. Great efforts have been devoted to suppressing them in China, notably at the notorious Wuda Coalfield in Inner Mongolia. Recent surveys revealed that while fires in this coalfield have been nearly extinguished near the surface, they persist underground. However, the impacts of Hg volatilized from underground coal fires remain unclear. Here, we measured concentrations and isotope compositions of atmospheric Hg in both gaseous and particulate phases at an urban site near the Wuda Coalfield. The atmospheric Hg displayed strong seasonality in terms of both Hg concentrations (5-7-fold higher in fall than in winter) and isotope compositions. Combining characteristic isotope compositions of potential Hg sources and air mass trajectories, we conclude that underground coal fires were still emitting large amounts of Hg into the atmosphere that have been transported to the adjacent urban area in the prevailing downwind direction. The other local anthropogenic Hg emissions were only evident in the urban atmosphere when the arriving air masses did not pass directly through the coalfield. Our study demonstrates that atmospheric Hg isotope measurement is a useful tool for detecting concealed underground coal fires.

Spatial-temporal analysis of coal fire risk identification and suppression assessment with satellite time series mapping 2013-2020 in Midong coalfield, Xinjiang, China

ABSTRACT Spontaneous combustion of underground coal seam leads to significant loss of coal resource while causing serious geological disasters, environmental degradation and social security problems. The Midong Coalfield in Urumqi was selected to conduct research on dynamic detection and assessment of underground coal fires from 2013 to 2020 based on time series satellite imagery of Landsat-8. The land surface temperature (LST) was retrieved using RTE. The hotspot SFSE was proposed to map the spatial-temporal distribution of coal fires. The CTIR was also established to solve how to comprehensively evaluate the intensity and spatial variations of coal fire thermal island effect. Based on Sequence Overlap Analysis the integrated dynamic interpretation of thermal anomaly intensity, area and migration of coal fire hazard has been provided. In addition, the TADC was proposed to effectively reflect the active fire centre and aggregation tendency. The results show that: The RTE inversion error is within 4.6 °C, verified with the surveyed LST. The overall accuracy of fire thermal anomaly extraction by HSA by field-tested exceeds 65%. A feature can be judged to be a fire pixel when its recurrence reaches 75% or more of the total number of observation frequency. The maximum and minimum extent extracted of the total fire area is 65.6×105 and 46.6×105 m2, respectively. And the overall expansion rate is 1.29×105 m2 year−1, in 82% agreement with the measurement. The burning intensity of CTIR first increases 0.024 year−1 averagely, but then decreases at a rate of 0.0055 year−1. Fractures and faults in the fire-affected area play a critical role in fire induction and distribution. Frequent mining and excavation activities are confirmed to accelerate the further fire production and propagation. After suppression, the relevant fire area has shrunken by up to 60%, while the overall CTIR is reduced by more than 70%.

Ground Deformation Monitoring over Xinjiang Coal Fire Area by an Adaptive ERA5-Corrected Stacking-InSAR Method

Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas.

Mercury emission from underground coal fires: a typical case in China

Mercury emission from underground coal fires: a typical case in China