Coal Fire Detection Research Articles

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.

Detection and management of coal seam outcrop fire in China: a case study

The outcrop fire area in Rujigou Coal Mine in Ningxia, China has been burning continuously for over 100 years. This not only results in wastage of resources but also poses significant damage to the ecological environment. Previous research on open fire detection has mainly focused on coalfield fire areas, using single method such as infrared remote sensing or surface temperature measurement, magnetic method, electrical method, radon measurement and mercurimetry. However, the outcrop fire area has migrated to deeper parts over the years, conventional single fire zone detection methods are not capable of accurately detecting the extent of the fire zone, inversion interpretation is faced with the problem of many solutions. In fire management, current research focuses on the development of new materials, such as fly ash gel, sodium silicate gel, etc., However, it is often difficult to quickly extinguish outcrop fire areas with a single technique. Considering this status quo, unmanned aerial vehicle (UAV) infrared thermal imaging was employed to initially detect the scope of the outcrop fire area, and then both the spontaneous potential and directional drilling methods were adopted for further scope detection in pursuit of more accurate results. In addition, an applicable fire prevention and extinguishing system was constructed, in which three-phase foam was injected for the purpose of absorbing heat and cooling. Furthermore, the composite colloid was used to plug air leakage channels, and loess was backfilled to avoid re-combustion. The comprehensive detection and control technologies proposed in this study can be applied to eliminating the outcrop fire area and protecting the environment. This study can provide guidance and reference for the treatment of other outcrop fire areas.

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.

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%.

A Coal Mine Safety from Conventional to Modern Method Through LoRaWAN

In this paper, a data transmission method called Thing Speak is used to construct a coal mine safety system. The system is used to monitor and regulate a number of characteristics in coal mines, including light detection, gas leak detection, temperature and humidity conditions, and coal mine fire detection. These sensors are all installed in coal mines and are regarded as a single unit. For analysis, all sensor values are continuously uploaded to Thingspeak and buzzer is utilized to notify the staff. The created technology is primarily used to enhance worker safety and the working environment in coal mines.

Study on the characteristics of radon exhalation from rocks in coal fire area based on the evolution of pore structure

Radon is of great significance as a tracer for the detection of coal fires due to its distinct variations in radon exhalation properties while heating. The research on radon exhalation performance through pore structure is still in its early stages. In this paper, the pore structure and radon exhalation characteristics of heat-treated limestone are studied using indoor tests such as nuclear magnetic and radon measurements. The study's results demonstrate that the radon exhalation rate of limestone initially increases gradually, followed by a steady decline and subsequent increase with the increase in temperature. The radon exhalation rate at 800 °C reaches 2.42 times that at room temperature. The pore structure change within limestone strongly correlates with the radon exhalation rate. The pore volume of micropores (<0.1 μm) plays an essential role in the radon exhalation capacity, which is directly related to the fractal dimension of micropore structure in the heated limestone. The study's findings can be used to identify coal fires.

Detection of coal fire by deep learning using ground penetrating radar

Coal fire seriously endangers coal resources. Accurate detection of its combustion range is the basis of disaster control. In this paper, a deep learning-based method of recognizing coal fire using ground-penetrating radar (GPR) is proposed, which improves the accuracy and speed of delineating coal fire areas. The self-built coal fire physical model is scanned by the GPR, and the radar images are obtained. The test results are compared with GPR images to summarize the spatial evolution law of coal fire areas and interpret the signal characteristics of the coal fire in radar images. The signal characteristics include combustion cavity, combustion surface, and underground combustion collapse surface. Comparing different algorithms, the results show that the YOLOv5l has the highest detection accuracy, which meets the need for detection of the coal fire. The proposed method lays the foundation for the detection of the combustion range in the coal fire areas.

A spatio-temporal temperature-based thresholding algorithm for underground coal fire detection with satellite thermal infrared and radar remote sensing

Coal fire is a global catastrophe that causes resource waste, economic loss, and environmental pollution. Accurate detection of surface abnormalities induced by coal fires (such as thermal anomalies and land deformation) is critical for coal fire monitoring and extinguishment. However, the conventional thermal anomaly detection methods do not consider the temporal variation of land surface temperature (LST). Moreover, previous studies have not applied the polarimetric persistent scatterer interferometry(PolPSI) technique for coal-fire-related ground deformation monitoring, which is supposed to have better performance than conventional persistent scatterer interferometry (PSI) approaches. To this end, in this study, a spatio-temporal temperature-based thresholding (STTBT) algorithm is proposed to detect thermal anomalies, firstly. Then, the ground deformation obtained by the PolPSI method is combined with the detected thermal anomalies for the coal fire detection. Besides, a two-stage bandpass filter is employed to assist in detecting the suspected coal fire. The results in the Fukang coal fire area, China, demonstrate that, compared with the conventional methods, the proposed STTBT mothed achieves higher accuracy of thermal anomalies detection, thereby can able to detect coal fire areas more effectively. In addition, PolPSI has better performance than PSI in ground deformation monitoring over coal fire areas. The proposed STTBT combined with PolPSI can serve as a powerful tool for detecting and monitoring underground coal fires.

Constructing 3-D Land Surface Temperature Model of Local Coal Fires Using UAV Thermal Images

Coal fires have led to severe ecological disasters worldwide and threatened the health and safety of nearby residents to a certain extent. It is critical to detect the scope and extent of coal fires in order to control and extinguish them. In this paper, the unmanned aerial vehicle (UAV), thermal infrared imaging and oblique photogrammetry technologies were integrated to create a 3D land surface temperature model of the Chunjingwa Coal Fire in Shanxi Province, China. Hundreds of visible and thermal infrared images were collected from 5 flight survey lines. The thermal infrared images were preprocessed in batches to meet the requirements of 3D modeling. The construction of the thermal infrared 3D model of the fires was achieved successfully, allowing for the visualization of the surface temperature field in true 3D. Four high-temperature zones were delineated according to the 3D land surface temperature model. Furthermore, the features of ground objects were clearly depicted by the visible 3D model constructed by visible images and the digital elevation model was established based on the spatial coordinate contained in the visible 3D model. The research results provide technical support for the subsequent firefighting plan, and a reference for the high-precision detection of other coal fires around the world.

Monitoring Persistent Coal Fire Using Landsat Time Series Data From 1986 to 2020

Coal fires pose great threats to valuable energy resources, the ecological environment, and human safety. They are one of the most persistent fires on the Earth, which can burn for an extremely long-term period from decades to hundreds or even thousands of years. Remote sensing detection of coal fires is of significance for mitigating coal fire hazards. Nevertheless, short-term or temporal discrete land surface temperature (LST) data have limited capability in characterizing the persistent coal fire. This study proposed a methodology to monitor persistent coal fires using long-term Landsat thermal images and further to analyze spatiotemporal dynamics of coal fires. A total of 1118 high-quality Landsat images (each image containing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$446\times446$ </tex-math></inline-formula> pixels) spanning 35 years from 1986 to 2020 in the Wuda coalfield area (China) were processed to retrieve the LST. LST time series of each pixel was decomposed into the seasonal, trend, and remainder components. Coal fire areas were demarcated by using the range of the trend components. To trace the trend and change point of the LST time series, the Mann–Kendall test was applied to the trend components, and the Pettitt test was employed to the original time series vectors of those pixels located in the coal fire areas, respectively. The random sample consensus algorithm was utilized to identify the background temperature (inliers) and high temperature (outliers) and, thus, judge the coal fire burning period, and the symbolic aggregate approximation algorithm was used to evaluate the robustness of the judgment. The calibration was conducted according to the filed surveys, obtaining spatiotemporal 3-D coal fire dynamics. The proposed methodology was testified by comparisons with fieldwork and regional anomaly extractor algorithm, demonstrating good performance in comprehensive monitoring of persistent coal fires.

Spatio-temporal variation and propagation direction of coal fire in Jharia Coalfield, India by satellite-based multi-temporal night-time land surface temperature imaging

In this paper, the spatio-temporal variation and propagation direction of coal fire were studied in the Jharia Coalfield (JCF), India during 2006–2015 through satellite-based night-time land surface temperature (LST) imaging. The LST was retrieved from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) night-time thermal-infrared data by a robust split-window algorithm based on scene-specific regression coefficients, band-specific hybrid emissivity, and night-time atmospheric transmittance. The LST-profile-based coal fire detection algorithm was formulated through statistical analysis of the LST values along multiple transects across diverse coal fire locations in the JCF in order to compute date-specific threshold temperatures for separating thermally-anomalous and background pixels. This algorithm efficiently separates surface fire, subsurface fire, and thermally-anomalous transitional pixels. During the observation period, it was noticed that the coal fire area increased significantly, which resulted from new coal fire at many places owing to extensive opencast-mining operations. It was observed that the fire propagation occurred primarily along the dip direction of the coal seams. At places, lateral-propagation of limited spatial extent was also observed along the strike direction possibly due to spatial continuity of the coal seams along strike. Moreover, the opencast-mining activities carried out during 2009–2015 and the structurally weak planes facilitated the fire propagation.

Multi-Scale Coal Fire Detection Based on an Improved Active Contour Model from Landsat-8 Satellite and UAV Images

Underground coal fires can increase surface temperature, cause surface cracks and collapse, and release poisonous and harmful gases, which significantly harm the ecological environment and humans. Traditional methods of extracting coal fires, such as global threshold, K-mean and active contour model, usually produce many false alarms. Therefore, this paper proposes an improved active contour model by introducing the distinguishing energies of coal fires and others into the traditional active contour model. Taking Urumqi, Xinjiang, China as the research area, coal fires are detected from Landsat-8 satellite and unmanned aerial vehicle (UAV) data. The results show that the proposed method can eliminate many false alarms compared with some traditional methods, and achieve detection of small-area coal fires by referring field survey data. More importantly, the results obtained from UAV data can help identify not only burning coal fires but also potential underground coal fires. This paper provides an efficient method for high-precision coal fire detection and strong technical support for reducing environmental pollution and coal energy use.

Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang

Underground coal fires have become a worldwide disaster, which brings serious environmental pollution and massive energy waste. Xinjiang is one of the regions that is seriously affected by the underground coal fires. After years of extinguishing, the underground coal fire areas in Xinjiang have not been significantly reduced yet. To extinguish underground coal fires, it is critical to identify and monitor them. Recently, remote sensing technologies have been showing great potential in coal fires’ identification and monitoring. The thermal infrared technology is usually used to detect thermal anomalies in coal fire areas, and the Differential Synthetic Aperture Radar Interferometry (DInSAR) technology for the detection of coal fires related to ground subsidence. However, non-coal fire thermal anomalies caused by ground objects with low specific heat capacity, and surface subsidence caused by mining and crustal activities have seriously affected the detection accuracy of coal fire areas. To improve coal fires’ detection accuracy by using remote sensing technologies, this study firstly obtains temperature, normalized difference vegetation index (NDVI), and subsidence information based on Landsat8 and Sentinel-1 data, respectively. Then, a multi-source information strength and weakness constraint method (SWCM) is proposed for coal fire identification and analysis. The results show that the proposed SWCM has the highest coal fire identification accuracy among the employed methods. Moreover, it can significantly reduce the commission and omission error caused by non-coal fire-related thermal anomalies and subsidence. Specifically, the commission error is reduced by 70.4% on average, and the omission error is reduced by 30.6%. Based on the results, the spatio-temporal change characteristics of the coal fire areas have been obtained. In addition, it is found that there is a significant negative correlation between the time-series temperature and the subsidence rate of the coal fire areas (R2 reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires.

MODIS land surface temperature time series decomposition for detecting and characterizing temporal intensity variations of coal fire induced thermal anomalies in Jharia coalfield, India

Long-term MODIS time series Land Surface Temperature (LST) observations over coal fire affected areas were utilized for detection and characterization of coal fire as a function of its temporal intensity variation (e.g. growing, temporally consistent or diminishing) on annual basis in Jharia coalfield. LST pixel time series (LPTS) vectors were generated for selected coal fire and non-coal fire locations using 782 LST maps of the duration 2001 − 2017. LPTS vectors were decomposed to extract the nonlinear trend component using Seasonal Trend decomposition based on Loess model. Background-referenced trends were generated for coal fire pixels. Slope, p-value of Mann-Kendall test and average annual deviation parameters were calculated for annually segmented background-referenced coal fire trends to characterize coal fire on annual basis and to separate coal fire induced anomalous trend and non-coal fire trends. The results were validated with that of published literature.

Application of unmanned aerial vehicle (UAV) thermal infrared remote sensing to identify coal fires in the Huojitu coal mine in Shenmu city, China

China is a major coal-producing country that consumes large amounts of coal every year. Due to the existence of many small coal kilns using backward mining methods, numerous worked-out areas have been formed. The coal mines were abandoned with no mitigation, so air penetrates into the roadways and contacts the coal seams; as a result, the residual coal seams spontaneously ignite to form coal fires. These coal fires have burned millions of tons of valuable coal resources and caused serious environmental problems. To implement fire suppression more effectively, coal fire detection is a key technology. In this paper, thermal infrared remote sensing from unmanned aerial vehicle combined with a surface survey is used to identify the range of coal fires in the Huojitu coal mine in Shenmu city. The scopes and locations of the fire zones are preliminarily delineated, which provides an accurate basis for the development of fire suppression projects.

Coal fire detection and evolution of trend analysis based on CBERS-04 thermal infrared imagery

Coal fire hazards not only causes loss of coal resources but also induces serious geological hazards and environmental degeneration. Wuda Coalfield (China), known for its widespread coal fires, was used as the study area for this study. Owing to an inherent noise problem, CBERS (China & Brazil Earth Resource Satellite)-04 thermal infrared images have not been explored and applied in the field of coal fire detection. An adaptive-edge-threshold (AET) method was proposed to delineate coal fire maps based on nighttime CBERS-04 data from 2015 to 2020. Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) was employed to cluster coal fire spots into different classes, then coal fire propagation directions were generated by connecting the clustering centers of the same class in chronological order. The results of quantitative analysis of coal fires showed that AET method achieved the highest accuracy among four coal fire recognition algorithms; the accuracy of AET in field verification reached 81.25%. The total area of coal fires increased by 137%, while the total intensity of coal fires decreased by 18% from 2015 to 2020. Overall accuracies of ISODATA clustering coal fire spots were between 90.25% and 100.00%, the Kappa coefficients were between 0.82 and 1.00. The evolution trend of coal fires obtained based on this clustering accuracy is reliable. The evolution trend of coal fires is as follows: coal fires of Class I spread to the northwest; coal fires of Class II first propagated to the northwest, then to the south and then to the west; coal fires of Class III essentially stayed in the same place; coal fires of Class IV spread to the northwest; coal fires of Class V spread to the southeast and then to the northeast.

Retrieval of precise land surface temperature from ASTER night-time thermal infrared data by split window algorithm for improved coal fire detection in Jharia Coalfield, India

The study proposed a methodology for the retrieval of precise Land Surface Temperature (LST) in Jharia Coalfield from night-time ASTER multispectral thermal infrared (TIR) data by split-window algorithm (SWA) using atmospheric transmittance and band-specific Land Surface Emissivity (LSE). For deriving night-time atmospheric transmittance, water vapor content was retrieved from night-time ASTER TIR data by modified split-window covariance and variance ratio approach. Improved LSE was retrieved by the proposed modified LSE model by integrating refined thermal emission-vegetation cover model, modified normalized difference water index and bandwidth-weighted red band reflectivity model. The retrieved SWA LST was compared with LST obtained by single-channel algorithm (SCA) across three coal fire test sites to demonstrate significant improvement in temperature contrast between coal fire and background pixels. Besides, SWA LST based coal fire thermal anomalies are significantly comparable (including substantially reduced false alarms) with in-situ observations than that of SCA LST.

Detection and Analysis of Coal Fire in Jharia Coalfield (JCF) Using Landsat Remote Sensing Data

Coal fires are common and serious problem in most of the coal producing countries of the world. In India, Jharia Coalfield (JCF) has one of the dense surface–subsurface coal fires in the world. The paper presents a case study of fire mapping and its propagation dynamics of JCF during 2001 to 2016 using Landsat data at consecutive 5 year interval. The study reveals that there are three distinct zones of fire in JCF viz., Eastern zone (Rajapur, Lodna, Kustore, Eastern Jharia, Bastacola, Chasnala (IISCO) and Bhowrah), Western Zone (Barora area of Shatabdi, Muraidih open cast mines) and Central zone (Kustor, Tisra, Bhulanbarari, Gonudih Khas Kusunda Colliery (GKKC), Sijua and Modidih colliery). The maximum temperature observed from Landsat data in JCF for years 2001, 2006, 2011 and 2016 is 50 °C, 53 °C, 57.5 °C and 56.5 °C, respectively. In the year 2016, the eastern part of JCF was relatively more affected by coal fire than the western part, showing prevalence of major surface and subsurface coal fire. The same trend was maintained from 2001 to 2016. The total fire area is 8.85 km2, 9.34 km2, 9.78 km2, 8.42 km2 in the years 2001, 2006, 2011 and 2016, respectively. The total fire area is reduced by 0.43 km2 (from 8.85 to 8.42 km2) from 2001 to 2016.

Integration of satellite remote sensing data in underground coal fire detection: A case study of the Fukang region, Xinjiang, China

Xinjiang in China is one of the areas worst affected by coal fires. Coal fires cannot only waste a large amount of natural resources and cause serious economic losses, but they also cause huge damage to the atmosphere, the soil, the surrounding geology, and the environment. Therefore, there is an urgent need to effectively explore remote sensing based detection of coal fires for timely understanding of their latest development trend. In this study, in order to investigate the distribution of coal fires in an accurate and reliable manner, we exploited both Landsat-8 optical data and Sentinel-1A synthetic aperture radar (SAR) images, using the generalized single-channel algorithm and the InSAR time-series analysis approach, respectively, for coal fire detection in the southern part of the Fukang region of Xinjiang, China. The generalized single-channel algorithm was used for land surface temperature information extraction. Meanwhile, the time-series InSAR analysis technology was employed for estimating the surface micro deformation information, which was then used for building a band-pass filter. The suspected coal fire locations could then be established by a band-pass filtering operation on the obtained surface temperature map. Finally, the locations of the suspected coal fires were validated by the use of field survey data. The results indicate that the integration of thermal infrared remote sensing and radar interferometry technologies is an efficient investigation approach for coal fire detection in a large-scale region, which would provide the necessary spatial information support for the survey and control of coal fires.

Detection and delineation of coal mine fire in Jharia coal field, India using geophysical approach: A case study

Coal mine fire is a serious problem in Jharia coal field, India. The coal mine fire can be detected with different techniques such as borehole temperature measurement, thermo-compositional analysis, remote sensing techniques, thermo-graphic measurement and geophysical methods. In this study, various geophysical methods were used to detect the surface and subsurface coal mine fires. Geophysical techniques used in the present study are apparent resistivity, self-potential (SP), magnetic method and thermography. Geophysical anomalies such as low SP value of \(-60\hbox { mV}\), high negative magnetic response and low apparent resistivity value helped us to detect and delineate the fire and non-fire areas laterally as well as depthwise. Furthermore, the thermography survey was carried out in the coal field using thermal imaging camera in order to substantiate the geophysical methods. This integrated approach was found to be more advantageous for the detection and delineation of surface and subsurface fire with respect to use of any specific techniques. Moreover, the level of threat towards the locality, national railway line was also assessed unambiguously using the above techniques. Hence, proper planning and implementation towards the mitigation of hazard can be achieved on the basis of the reported results.