Particle swarm optimization inversion of self-potential data for depth estimation of coal fires over East Basuria colliery, Jharia coalfield, India

Srivardhan, V.; Pal, Sanjit Kumar; Vaish, Jitendra; Kumar, Sahadev; Bharti, Abhay Kumar; Priyam, Piyush

doi:10.1007/s12665-015-5222-9

Cited by 31 publications

(8 citation statements)

References 47 publications

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“…We test the efficiency of the algorithm for various test cases including different theoretical SP anomalies of a horizontal cylinder and a sphere produced by using depths varied from 5 to 15 m with 1 m increments. The initial anomalies of these models have been generated using the following Equation [6,34,38,39]:…”

Section: Synthetic Examplementioning

confidence: 99%

Determination of source parameters of simple-shaped geologic subsurface structures from self-potential anomalies using enhanced local wavenumber method

Thanh¹

2019

MaP

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Simple geometry model structures can be useful in quantitative evaluation of self-potential data. In this paper, we solve local wavenumber equation to estimate the horizontal position, the depth and the type of the causative source geometry by using a linear least-squares approximation. The advantages of the algorithm in determining the horizontal position and depth measure are its independency to shape factor of the sources and also its simple computations. The algorithm is built in Matlab environment. The validity of the algorithm is illustrated on variable noise-free and random noise included synthetic data from two-dimensional (2-D) models where the achieved parametric quantities coincide well with the actual ones. The algorithm is also utilized to real self-potential data from Ergani Copper district, Turkey. The results from the actual data application are in good agreement with the published literature for the study area. The source code of the algorithm is available from the authors on request.

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Section: Synthetic Examplementioning

confidence: 99%

Determination of source parameters of simple-shaped geologic subsurface structures from self-potential anomalies using enhanced local wavenumber method

Thanh¹

2019

MaP

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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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“…Since the era of Herodotus, Tajikistan's Ravat coal field fire has been burning for more than 2,000 years [2]. In coal-producing countries, especially in China [3][4][5][6], the United States [7][8][9], Australia [1,10,11], India [12][13][14], South Africa [15,16], and so on, the coal field fire is more serious. Coal fire disaster not only burns a lot of coal resources directly, which threatens the safety of miners and results in large economic losses [17], but also seriously endangers the ecological environment (for example, air pollution, surface heat disasters, and groundwater pollution).…”

Section: Introductionmentioning

confidence: 99%

Experimental Research on the Performance of the Macromolecule Colloid Fire-Extinguishing Material for Coal Seam Spontaneous Combustion

Fan

Wen

Zhang

et al. 2019

Advances in Materials Science and Engineering

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Coal resources are rich in China. They are mainly concentrated in the northwest region. But these resources also make the situation of spontaneous coal combustion hazards most serious in this area. Affected by the arid and semiarid climate, the water resource in this area is relatively scarce. In order to improve the utilization rate of water, a macromolecule colloid for coal mine fire fighting was developed in this research. The main components of the macromolecule colloid and the principle of gel formation were analyzed. The physical and chemical properties of the macromolecule colloid were analyzed by the time of gelation and the water loss. The water content of the macromolecule colloid was as high as 99%. The inhibition performance of the macromolecule colloid on coal fire was studied by the programmed heating device. The experimental results show that the macromolecule colloid has a significant inhibitory effect on the heating rate, oxygen consumption rate, and gas production rate. It inhibits the coal-oxygen composite by increasing the activation energy (Ea) of coal, which also has the effect of wrapping coal to isolate oxygen. The inhibition performance of the macromolecule colloid on coal fire disasters is better than that of water, water glass, loess compound colloids, and other traditional fire-extinguishing materials.

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Particle swarm optimization inversion of self-potential data for depth estimation of coal fires over East Basuria colliery, Jharia coalfield, India

Cited by 31 publications

References 47 publications

Determination of source parameters of simple-shaped geologic subsurface structures from self-potential anomalies using enhanced local wavenumber method

Determination of source parameters of simple-shaped geologic subsurface structures from self-potential anomalies using enhanced local wavenumber method

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

Experimental Research on the Performance of the Macromolecule Colloid Fire-Extinguishing Material for Coal Seam Spontaneous Combustion

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