Inversion of Self Potential Anomaly for Coal Seam Fire Prediction Using Genetic Algorithm

Singh, Prasoon Kumar; Pal, Sanjit Kumar; Kumar, Sahadev

doi:10.3997/2214-4609.201901125

Cited by 3 publications

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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%

“…They established it from the thermal band of Landsat ETM+ data and in-situ temperature observations using thermal imaging camera. 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.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Pal

Kumar

Srivastava

2021

Preprint

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A Review of Geophysical Modeling Based on Particle Swarm Optimization

2021

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This paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

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Inversion of self-potential data using generalized regression neural network

Durdağ

Pekşen

2022

Acta Geod Geophys

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Inversion of Self Potential Anomaly for Coal Seam Fire Prediction Using Genetic Algorithm

Cited by 3 publications

References 0 publications

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

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

A Review of Geophysical Modeling Based on Particle Swarm Optimization

Inversion of self-potential data using generalized regression neural network

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