1887

Abstract

Summary

Coal seam fire is a challenging issue and many methods and techniques has been developed for its mapping and prediction. The paper discusses the use of an evolutionary algorithm, Genetic Algorithm (GA) on Self-Potential (SP) data for the prediction of coal seam fire. The GA used here is real encoded and is based on Rayleigh Crossover technique. To check the efficiency of algorithm we used GA on synthetic data to characterize the parameters of buried sphere, cylinder and 2D thin sheet. After successfully determining the parameters of synthetic causative bodies, the algorithm was implemented on SP field data of Chattabad Colliery of Jharia Coal field, India. The SP anomaly recorded over the regions of coal fire is mainly due to thermoelectric effect which arise due to the effect of temperature gradient observed in the coal fires. The inverted results helped us in determining the depth of the fire regions as well as other geometrical factors to characterize the fire regions.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201901125
2019-06-03
2020-03-29
Loading full text...

Full text loading...

References

  1. 1.VaishJ, PalSK [2013] Interpretation of Magnetic Anomaly data over East Basuria region using an Enhanced Local Wavenumber (ELW) Technique, 10th Biennial International Conference & Exposition, Society of Petroleum Geophysicists conference, Kochi, Kerala, India
    [Google Scholar]
  2. 2.VaishJ, PalSK [2015a] Subsurface coal fire mapping of East Basuria Colliery. Jharkhand J Geol Soc India86(4), 438–444
    [Google Scholar]
  3. 3.PrakashA, SarafAK, GuptaRP, DuttaM, SundaramRM (1995) Surface thermal anomalies associated with underground fires in Jharia Coal Mines India. Int J Remote Sens16(12), 2105–2109
    [Google Scholar]
  4. 4.PrakashA, GuptaRP, SarafAK [1997] A Landsat TM based comparative study of surface and subsurface fires in the Jharia Coalfield India. Int J Remote Sens18(11), 2463–2469
    [Google Scholar]
  5. 5.RevilA, KaraoulisM, SrivastavaS, ByrdinaS [2013] Thermoelectric self-potential and resistivity data localize the burning front of underground coal fires. Geophysics78(5), B259–B273
    [Google Scholar]
  6. 6.ChambersRG [1977] Thermoelectric effects and contact potentials. Phys Educ12(6), 374–380
    [Google Scholar]
  7. 7.Lim SiewMooi, SulaimanMD. Nasir, SultanAbu Bakar MD., MustaphaAbu Bakar, TejoBimo Ario [2014] A new real coded genetic algorithm crossover: Rayleigh Crossover. Journal of Theoretical and Applied Information Technology, 62(1), 262–268
    [Google Scholar]
  8. 8.R.A.E.Makinen, J.Periaux, J.Toivanen [1999] Multidisciplinary shape optimization in aerodynamics and electromagnetic using genetic algorithms, International Journal for Numerical Methods in Fluids, 30(2), 149–159.
    [Google Scholar]
  9. 9.YüngülS [1950] Interpretation of spontaneous polarization anomalies caused by spheroidal orebodies. Geophysics15(2), 237–246
    [Google Scholar]
  10. 10.BhattacharyaBB, RoyN [1981] A note on the use of nomograms for self-potential anomalies. Geophys Prospect29, 102–107
    [Google Scholar]
  11. 11.MurthyBVS, HaricharanP [1985] Nomograms for the complete interpretation of spontaneous potential profiles over sheet like and cylindrical 2D structures. Geophysics50, 1127–1135
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201901125
Loading
/content/papers/10.3997/2214-4609.201901125
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error