Acta Geophysica

, Volume 67, Issue 3, pp 825–836 | Cite as

Comprehensive prediction of coal seam thickness by using in-seam seismic surveys and Bayesian kriging

  • Mengbo Zhu
  • Jianyuan ChengEmail author
  • Weixiong Cui
  • Hui Yue
Research Article - Applied Geophysics


Quantitative determination of the coal seam thickness distribution within the longwall panel is one of the primary works before integrated mining. In-seam seismic (ISS) surveys and interpolations are essential methods for predicting thickness. In this study, a new quantitative method that combines ISS and Bayesian kriging (BK), called ISS–BK, is proposed to determine the thickness distribution. ISS–BK consists of the following six steps. (1) The group velocity of Love waves is plotted by using the simultaneous iterative reconstruction technique under a constant frequency value. (2) An approximate quantitative relationship between the thickness and the group velocity is fitted based on sampling points of the coal seam thickness, which are measured during the process of entry development. (3) The group velocity map is translated into a primary thickness map according to the above-mentioned fitted equation. (4) By subtracting the ISS prediction result from the actual thickness at a sampling point, the residual variable is created. (5) The residual distribution is interpolated within the whole longwall panel by applying BK. The residual map establishes the interconnection between the ISS survey and BK. (6) A refined thickness distribution map can be obtained by overlapping the primary thickness map and the residual map. The application of this method to the No. 2408 longwall panel of Yuhua Coal Mine using ISS–BK showed a considerable improvement in thickness prediction accuracy over ISS. The residuals of ISS and ISS–BK mainly lie in the intervals (− 3.0, 3.0 m) and (− 1.0, 3.0 m), respectively. The accurate prediction rates [where the residual lies in the interval (0, 0.1 m)] of ISS and ISS–BK are 9.39% and 50.28%, respectively, and the effective prediction rates (where the residual is less than 1.0 m) of ISS and ISS–BK are 61.88% and 77.90%, respectively. All the above statistics reflect a considerable improvement in the ISS–BK method over the ISS method.


In-seam seismic Love wave Dispersion Bayesian kriging Coal seam thickness Longwall panel 



The authors gratefully acknowledge the financial support from the National Key Research and Development Plan (No. 2018YFC0807804) and the Guizhou Science and Technology Major Project (No. [2018]3003-1). Special thanks are given to the anonymous reviewers for their assistance, comments and suggestions.


  1. álvarez-Fernández MI, González-Nicieza C, álvarez-Vigil AE, Herrera García G, Torno S (2009) Numerical modelling and analysis of the influence of local variation in the thickness of a coal seam on surrounding stresses: application to a practical case. Int J Coal Geol 79(4):157–166CrossRefGoogle Scholar
  2. Cheng J, Ji G, Zhu P (2012) Resolution analysis of in-seam seismic tomographic inversion for coal thickness. J China Coal Soc 37(01):67–72Google Scholar
  3. Dresen L, Bochum R (1995) Seismic coal exploration, Part B. In-seam seismics. RuhrUniversität Bochum, Institut für Geophysik, BochumGoogle Scholar
  4. Du W, Peng S (2010) Coal seam thickness prediction with geostatistics. Chin J Rock Mech Eng 29(s1):2762–2767Google Scholar
  5. Dziewonski AM, Bloch S, Landisman M (1969) A technique for the analysis of transient seismic signals. Bull Seismol Soc Am 59:427–444Google Scholar
  6. Gersztenkorn A, Scales JA (1988) Smoothing seismic tomograms with alpha-trimmed means. Geophys J 92(1):67–72CrossRefGoogle Scholar
  7. Hu Z, Zhang P, Xu G (2018) Dispersion features of transmitted channel waves and inversion of coal seam thickness. Acta Geophys 66(5):1001–1009CrossRefGoogle Scholar
  8. Omre H (1987) Bayesian kriging: merging observations and qualified guesses in kriging. Math Geol 19(1):25–39CrossRefGoogle Scholar
  9. RäDer D, Schott W, Dresen L, RüTER H (1985) Calculation of dispersion curves and amplitude-depth distributions of love channel waves in horizontally-layered media. Geophys Prospect 33(6):800–816CrossRefGoogle Scholar
  10. Sahalos JN, Kyriacou G (1985) On the electromagnetic detection of the thickness of a coal or lignite seam with slate backing. J Franklin Inst 320(2):83–101CrossRefGoogle Scholar
  11. Schott W, Waclawik P (2015) On the quantitative determination of coal seam thickness by means of in-seam seismic surveys. Can Geotech J 52:1496–1504CrossRefGoogle Scholar
  12. Slavinskii VM, Shilov VI, Chernyak ZA (1985) The output function and calibration curve for a natural-radioactivity coal-seam thickness gauge. Meas Tech 28(8):704–706CrossRefGoogle Scholar
  13. Sun J, Chen B (2017) Coal-rock recognition approach based on CLBP and support vector guided dictionary learning. J China Coal Soc 42(12):3338–3348Google Scholar
  14. Wang B, Liu S, Jiang Z, Huang L (2011) Advanced forecast of coal seam thickness variation by integrated geophysical method in the laneway. In: First international symposium on mine safety science and engineeringGoogle Scholar
  15. Wang X, Li Y, Chen T et al (2017) Quantitative thickness prediction of tectonically deformed coal using extreme learning machine and principal component analysis: a case study. Comput Geosci 101(C):38–47CrossRefGoogle Scholar
  16. Yuan L (2017) Scientific conception of precision coal mining. J China Coal Soc 42(1):1–7Google Scholar
  17. Zou G, Xu Z, Peng S, Fan F (2018) Analysis of coal seam thickness and seismic wave amplitude: a wedge model. J Appl Geophys 148:245–255CrossRefGoogle Scholar

Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2019

Authors and Affiliations

  1. 1.China Coal Research InstituteBeijingChina
  2. 2.Xi’an Research InstituteChina Coal Technology & Engineering Group CorpXi’anChina

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