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

Mishra, Rakesh; Roy, Prasanta; Singh, Virendra; Pandey, Jai Krishna

doi:10.1007/s12040-018-1010-8

Cited by 16 publications

(6 citation statements)

References 21 publications

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“…Aiming at easing open-pit mine management, Saini combined thermal infrared and visible light images captured by drones to quickly detect and accurately locate the thermal anomaly level of open-pit mining areas 11 . Mishra et al took advantage of the thermal imaging and remote sensing technology to detect the fire area of the Jharia coalfield in India, and verified the effectiveness of this technology 12 . Chatterjee et al studied the coal fire dynamics of the Jharia coalfield during the 1990s based on medium-resolution satellite thermal IR data such as Landsat-5 TM and Landsat-7 ETM + data (acquired in 10.4–12.5 µm spectral region) 13 .…”

Section: Introductionmentioning

confidence: 99%

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

Liu,

Qi,

Luo

et al. 2024

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

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

Liu,

Qi,

Luo

et al. 2024

Sci Rep

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“…However, King (1987) conducted a transient-electromagnetic study over Cindered coal seams in Australia and identi ed coal res to a depth of 340m. Further, over the last decades, several techniques have been developed to delineate and monitor the coal res such as, index gas measurements (Xie et al 2011), radon gas measurements (Xie et al 2011), ground temperature measurement (Litschke et al, 2008;Kuenzer et al, 2012), self-potential (SP) method (Rodriguez 1983;Revil and Jardani 2013;Karaoulis et al 201;Shao et al 2014;Singh , 2019Srivardhan et al, 2016, Mishra et al 2018Kumar and Pal 2020), magnetic method (Sternberg and Lippincott 2004;Kumar et al 2015;Shao et al 2014;Pal 2015, 2016;Pal et al 2016Pal et al ,2017Mishra et al 2018), ground penetrating radar method (Gudelach, 2010), controlled source audio magnetotelluric (CSAMT) method (Bartel, 1982), the time-domain electromagnetic technique (King 1987;Schaumann et al 2008) and electrical resistivity pro ling/imaging (Corwin and Hoover 1979;Gangopadhyay 2003;Rodriguez 1983;Karaoulis et al 2014;Xin et al 2015;Pal et al, 2016;Bhattacharya and Shalivahan 2016;Bharti et al 2016a, Shao et al 2016Mishra et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Further, in recent years geophysical studies, mainly, magnetic (Vaish and Pal 2013Kumar et al 2015;Pal et al 2016Pal et al , 2017 , SP (Bharti et al 2014;Singh et. , 2019Srivardhan et al 2016, Mishra et al 2018Kumar and Pal 2020;Srivardhan et al, 2016) and electrical resistivity (Gangopadhyay 2003;Pal et al 2016;Mishra et al 2018) have been carried out to map coal re in the Jharia coal eld and surroundings. In this paper, the rst time in India we have attempted an integrated geophysical approach comprising surface temperature measurement, magnetic, and electrical resistivity tomography (ERT) studies for detection of concealed subsurface coal re zones over Akashkinaree Colliery to impart a review for suitability of coal re study in Jharia coal eld using geophysical methods.…”

Section: Introductionmentioning

confidence: 99%

Integrated geophysical approach for Coal mine fire in Jharia coalfield, India

Pal

Kumar

Srivastava

2021

Preprint

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Jharia coalfield is one of India's largest and most productive coal fields, home of the oldest-burning coal seam fires in the world. This paper highlights the combined study comprising Surface Temperature measurement, Magnetic, and Electrical Resistivity Tomography (ERT) methods for delineation and mapping of coal fires with their depths, extension and propagation direction. Surface temperature mapping allows straight forward demarcation of coal fire areas. Magnetic data provide a map showing the earlier burned, currently burning and unburned patches based on the contrast between magnetic effects of the subsurface and fluctuations with temperatures exceeding or under the Curie temperature. The pyrolysis of coal is one of the main processes in coal fires, which results in very high conductivity. Thus, electrical resistivity tomography (ERT) is also a promising technique for the characterization of a coal fire-affected area. Most of the features of low and high resistivity associated with active fires and voids have been delimited well in the ERT sections that are generated from combine inversion of all arrays. These are also delineated well by the surface temperature mapping, and Magnetic methods. The field photographs, available coal seam and borehole lithology clearly show the suitability of the combined study for characterization of coal seam fire. A model of the coal fire, char formation and void formation resulting from coal seam fire has been established. Finally, a fire propagation model of the study area has been established based on the combined geophysical data that can be effectively used for mine hazard mitigation.

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“…Coal mine thermal disaster prediction and early warning are very important tasks in the coalfield. e early disaster prediction methods mainly include oxygen measurement, temperature measurement, and index gas analysis [4]. e oxygen measurement method is mainly used to measure the oxygen content in the coal mine and determine whether it reaches the threshold.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks

et al. 2021

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The spontaneous combustion of residual coals in the mined-out area tends to cause an explosion, which is one kind of severe thermodynamic compound disaster of coal mines and leads to serious losses to people's lives and production safety. The prediction and early warning of coal mine thermodynamic disasters are mainly determined by the changes of the index gas concentration pattern in coal mine mined-out areas collected continuously. The time series anomaly pattern detection method is mainly used to reach the state change of gas concentration pattern. The change of gas concentration follows a certain rule as time changes. A great change in the gas concentration indicates the possibility of coal spontaneous combustion and other disasters. To emphasize the features of collected maker gas and overcome the low anomaly detection accuracy caused by the inadequate learning of the normal mode, this paper adopted a method of anomaly detection for time series with difference rate sample entropy and generative adversarial networks. Because the difference rate entropy feature of abnormal data was much larger than that of normal mode, this paper improved the calculation method of the abnormal score by giving different weights to the detection points to enhance the detection rate. To verify the effectiveness of the proposed method, this paper employed simulation models of the mined-out area and adopted coal samples from Dafosi Coal Mine to carry out experiments. Preliminary testing was performed using monitoring data from a coal mine. The experiment compared the entropy results of different time series with the detection results of generative adversarial networks and automatic encoders and showed that the method proposed in this paper had relatively high detection accuracy.

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

Cited by 16 publications

References 21 publications

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

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

Integrated geophysical approach for Coal mine fire in Jharia coalfield, India

Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks

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