Study on electrical potential inversion imaging of abnormal stress in mining coal seam

  • Zhonghui Li
  • Yue NiuEmail author
  • Enyuan WangEmail author
  • Miao He
Original Article


During the mining activities, coal–rock dynamic disasters have caused grievous casualties and massive property losses. It is the severe problem for regional monitoring of abnormal stress. Electrical potential (EP) can be produced on coal rock under loading, and its response is related closely with the loading stress and damage evolution. Meanwhile, electric field inversion has the advantage to realize regionalization monitoring in the space. To identify the abnormal stress localization characteristics in mining coal seam, after theoretical derivation, the strategy of EP inversion imaging on is studied under bilateral model. Further, simulation experiment of coal rock under loading is conducted. The abnormal probability zones of sample can be identified with EP inversion imaging, while it corresponds with severe damage zones with significant crack propagation. It can be utilized to reveal the localized characteristics of damage and failure of coal rock spatially. Finally, the results of EP inversion in the mining coal seam indicate that the abnormal probability zones can be considered as stress concentration and dynamic hazard areas. Its effectiveness is verified by microseismic monitoring and rock-burst hazard assessment. The study provides a new idea to monitor abnormal stress zone regionally and forecast dynamic disasters in the field.


Electrical potential Inversion imaging Abnormal stress Coal seam Mining 



This work is supported by the State Key Research Development Program of China (Grant No. 2016YFC0801404), General Program of National Natural Science Foundation of China (51674254), State Key Laboratory of Coal Resources and Safe Mining, CUMT (SKLCRSM15X03), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We thank Dr. Lanbo Liu, from University of Connecticut, for editing the English text of a draft of this manuscript.


  1. Aldridge DF, Oldenburg DW (2010) Direct current electric potential field associated with two spherical conductors in a whole-space1. Geophys Prospect 37:311–330CrossRefGoogle Scholar
  2. Aydin A, Dobbs MR, Reeves HJ, Kirkham MP, Graham CC (2013) Stress induced electric field measurements of different rock lithology using the Electric Potential SensorGoogle Scholar
  3. Benisch K, Köhn D, Hagrey SA, Rabbel W, Bauer S (2015) A combined seismic and geoelectrical monitoring approach for CO2 storage using a synthetic field site. Environ Earth Sci 73:3077–3094CrossRefGoogle Scholar
  4. Cao W, Shi J-Q, Si G, Durucan S, Korre A (2018) Numerical modelling of microseismicity associated with longwall coal mining. Int J Coal Geol 193:30–45CrossRefGoogle Scholar
  5. Cartwright-Taylor A, Vallianatos F, Sammonds P (2014) Superstatistical view of stress-induced electric current fluctuations in rocks. Physica A 414:368–377. CrossRefGoogle Scholar
  6. Chong C, Ma L, Li Z, Ni W, Song S (2015) Logarithmic mean Divisia index (LMDI) decomposition of coal consumption in China based on the energy allocation diagram of coal flows. Energy 85:366–378CrossRefGoogle Scholar
  7. Colangelo G, Lapenna V, Perrone A, Piscitelli S, Telesca L (2006) 2D self-potential tomographies for studying groundwater flows in the Varco d’Izzo landslide (Basilicata, southern Italy). Eng Geol 88:274–286CrossRefGoogle Scholar
  8. Corwin RF, Hoover DB (1979) The self-potential method in geothermal exploration. Geophysics 44:226–245CrossRefGoogle Scholar
  9. Crespy A, Revil A, Linde N, Byrdina S, Jardani A, Bolève A, Henry P (2008) Detection and localization of hydromechanical disturbances in a sandbox using the self-potential method. J Geophys Res. CrossRefGoogle Scholar
  10. Darnet M, Marquis G, Sailhac P (2006) Hydraulic stimulation of geothermal reservoirs: fluid flow, electric potential and microseismicity relationships. Geophys J Int 166:438–444CrossRefGoogle Scholar
  11. Düzgün H (2005) Analysis of roof fall hazards and risk assessment for Zonguldak coal basin underground mines. Int J Coal Geol 64:104–115CrossRefGoogle Scholar
  12. Düzgün HS, Leveson N (2018) Analysis of soma mine disaster using causal analysis based on systems theory (CAST). Saf Sci 110:37–57CrossRefGoogle Scholar
  13. Fan C, Li S, Luo M, Du W, Yang Z (2017) Coal and gas outburst dynamic system. Int J Min Sci Technol 27:49–55CrossRefGoogle Scholar
  14. Guo ZQ, You J, Li G, Shi X (1989) The model of compressed atoms and electron emission of rock fracture. Chin J Geophys 32:173–177Google Scholar
  15. Haas AK, Revil A, Karaoulis M, Frash L, Hampton J, Gutierrez M, Mooney M (2013) Electric potential source localization reveals a borehole leak during hydraulic fracturing. Geophysics 78:D93–D113. CrossRefGoogle Scholar
  16. He M (2014) Research on the potential inversion imaging method based on seam stress abnormalities. Dissertation, China University of Mining and TechnologyGoogle Scholar
  17. Hosseini N (2017) Evaluation of the rockburst potential in longwall coal mining using passive seismic velocity tomography and image subtraction technique. J Seismol 21:1101–1110CrossRefGoogle Scholar
  18. Iqbal N, Al-Shuhail AA, Kaka SI, Liu E, Raj AG, McClellan JH (2017) Iterative interferometry-based method for picking microseismic events. J Appl Geophys 140:52–61CrossRefGoogle Scholar
  19. Lawson HE, Tesarik D, Larson MK, Abraham H (2017) Effects of overburden characteristics on dynamic failure in underground coal mining. Int J Min Sci Technol 27:121–129CrossRefGoogle Scholar
  20. Le TD, Oh J, Hebblewhite B, Zhang C, Mitra R (2018) A discontinuum modelling approach for investigation of Longwall Top Coal Caving mechanisms. Int J Rock Mech Min Sci 106:84–95CrossRefGoogle Scholar
  21. Li Z (2007) Study on surface potential effect and its mechanism of coal during deformation and fracture under load. Dissertation, China University of Mining and TechnologyGoogle Scholar
  22. Ma C, Li H, Niu Y (2018) Experimental study on damage failure mechanical characteristics and crack evolution of water-bearing surrounding rock. Environ Earth Sci 77:23CrossRefGoogle Scholar
  23. Marland S, Merchant A, Rowson N (2001) Dielectric properties of coal. Fuel 80:1839–1849CrossRefGoogle Scholar
  24. Nguyen S, Vu M-H, Vu M (2015) Extended analytical approach for electrical anisotropy of geomaterials. J Appl Geophys 123:211–217CrossRefGoogle Scholar
  25. Ogaya X, Ledo J, Queralt P, Jones AG, Marcuello Á (2016) A layer stripping approach for monitoring resistivity variations using surface magnetotelluric responses. J Appl Geophys 132:100–115CrossRefGoogle Scholar
  26. Patella D (1997) Introduction to ground surface self-potential tomography. Geophys Prospect 45:653–681CrossRefGoogle Scholar
  27. Porwal A, Carranza EJM (2015) Introduction to the special issue: GIS-based mineral potential modelling and geological data analyses for mineral exploration. Ore Geol Rev 71:477–483CrossRefGoogle Scholar
  28. Revil A, Mahardika H (2013) Coupled hydromechanical and electromagnetic disturbances in unsaturated porous materials. Water Resour Res 49:744CrossRefGoogle Scholar
  29. Revil A, Ehouarne L, Thyreault E (2001) Tomography of self-potential anomalies of electrochemical nature. Geophys Res Lett 28:4363–4366. CrossRefGoogle Scholar
  30. Revil A, Titov K, Doussan C, Lapenna V (2006) Applications of the self-potential method to hydrological problems. In: Applied hydrogeophysics. Springer, Berlin, pp 255–292Google Scholar
  31. Revil A, Mao D, Shao Z, Sleevi MF, Wang D (2017) Induced polarization response of porous media with metallic particles—part 6: the case of metals and semimetals. Geophysics 82:E97–E110CrossRefGoogle Scholar
  32. Shao Z, Wang D, Wang Y, Zhong X, Tang X, Xi D (2015) Electrical resistivity of coal-bearing rocks under high temperature and the detection of coal fires using electrical resistance tomography. Geophys J Int 204:1316–1331CrossRefGoogle Scholar
  33. Shao Z, Revil A, Mao D, Wang D (2017) Induced polarization signature of coal seam fires. Geophys J Int 208:1313–1331CrossRefGoogle Scholar
  34. Soengkono S, Bromley C, Reeves R, Bennie S, Graham D (2013) Geophysical techniques for low enthalpy geothermal exploration in New Zealand. Explor Geophys 44:215–227CrossRefGoogle Scholar
  35. Song X, Li X, Li Z, Zhang Z, Cheng F, Chen P, Liu Y (2018) Study on the characteristics of coal rock electromagnetic radiation (EMR) and the main influencing factors. J Appl Geophys 148:216–225CrossRefGoogle Scholar
  36. Srivardhan V, Pal S, Vaish J, Kumar S, Bharti AK, Priyam P (2016) Particle swarm optimization inversion of self-potential data for depth estimation of coal fires over East Basuria colliery, Jharia coalfield, India. Environ Earth Sci 75:688CrossRefGoogle Scholar
  37. Stoll J, Bigalke J, Grabner EW (1995) Electrochemical modelling of self-potential anomalies. Surv Geophys 16:107–120CrossRefGoogle Scholar
  38. Szwedzicki T (2003) Rock mass behaviour prior to failure. Int J Rock Mech Min Sci 40:573–584CrossRefGoogle Scholar
  39. Triantis D, Anastasiadis C, Vallianatos F, Kyriazis P (2007) Electric signal emissions during repeated abrupt uniaxial compressional stress steps in amphibolite from KTB drilling. Nat Hazards Earth Syst Sci 7:149–154CrossRefGoogle Scholar
  40. Vozoff K, Smith G, Hatherly P, Thomson S (1993) An overview of the radio imaging method in Australian coal mining. First Break 11:13–21CrossRefGoogle Scholar
  41. Wang J, Wang Z, Yang S (2017) A coupled macro-and meso-mechanical model for heterogeneous coal. Int J Rock Mech Min Sci 94:64–81CrossRefGoogle Scholar
  42. Yoshida S, Clint OC, Sammonds PR (1998) Electric potential changes prior to shear fracture in dry and saturated rocks. Geophys Res Lett 25:1577–1580. CrossRefGoogle Scholar
  43. Zhang C, Canbulat I, Hebblewhite B, Ward CR (2017) Assessing coal burst phenomena in mining and insights into directions for future research. Int J Coal Geol 179:28–44CrossRefGoogle Scholar
  44. Zhao Y, Jiao Z, Liu H, Jiang Y, Delong QI, Beijing T, Beijing T (2017) Simulation of ground pressure distribution at large mining height face based on GIS techniques. J China Univ Min Technol 46:33–40Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Gas and Fire Control for Coal Mines (China University of Mining and Technology)Ministry of EducationXuzhouChina
  2. 2.State Key Laboratory of Coal Resources and Safe MiningChina University of Mining and TechnologyXuzhouChina
  3. 3.National Engineering Research Center for Coal Gas ControlChina University of Mining and TechnologyXuzhouChina
  4. 4.School of Safety EngineeringChina University of Mining and TechnologyXuzhouChina

Personalised recommendations