1887
  1. Home
  2. A-Z Publications
  3. Near Surface Geophysics
  4. Volume 20, Issue 3
  5. Article
Volume 20, Issue 3
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

In karst topography, hidden caves or underground rivers may imperil the safety of tunnels. It is critical to accurately detect the location and geometric shape of hidden karst caves for assessing the safety of tunnels. This paper reports a case where a large karst cave was exposed during the construction of the Nongmo tunnel in Duan County, Guangxi, China. A 2D ground‐penetrating radar survey was employed to preliminarily detect the distribution of hidden karst caves along the tunnel axis. Advanced drilling was used to obtain the velocity of electromagnetic waves and verify the 2D ground‐penetrating radar detection. Ground‐penetrating radar 3D grid detection was carried out for further detecting the geometric shape of the hidden karst caves. Rotary thrust power ratio, which is a function of drilling speed, rotating speed, propulsion pressure and torque, was proposed to reflect the integrity of the surrounding rocks. It was found that the depth of the peak value in the rotary thrust power ratio curve is consistent with that of the amplitude jump in ground‐penetrating radar A‐scans and in time–frequency distributions. The results of 3D attributes analysis indicate that due to the variation of highlights in different ground‐penetrating radar signal attributes, the obtained karst cave shapes using different attributes would be various. A multi‐attributes fusion method was proposed to generate a data volume considering various attributes. The 2D geometric shapes at different depths were obtained using the anomalous body lineament in the generated date volume slices. The 3D geometric shapes were reproduced by combining the obtained 2D geometric shapes at various depths. Based on different detection and analysis methods, the outline shape of karst caves at YK364 + 199 ∼ YK364 + 184 of the Nongmo tunnel was revealed in detail.

© 2022 European Association of Geoscientists & Engineers. © 2022 European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1002/nsg.12207
2022-05-20
2024-04-24

  • From This Site

    /content/journals/10.1002/nsg.12207
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Alimoradi, A., Moradzadeh, A., Naderi, R., Salehi, M.Z. and Etemadi, A. (2008) Prediction of geological hazardous zones in front of a tunnel face using TSP‐203 and artificial neural networks. Tunnelling and Underground Space Technology, 23, 711–717.
     [Google Scholar]
  2. Allroggen, N. and Tronicke, J. (2016) Attribute‐based analysis of time‐lapse ground‐penetrating radar. Geophysics, 81(1), H1–H8.
     [Google Scholar]
  3. Andreychouk, V., Tyc, A. (2013) Karst hazards. In: Bobrowsky, P.T. (Eds.) Encyclopedia of Natural Hazards. Encyclopedia of Earth Sciences Series. Dordrecht:Springer.
     [Google Scholar]
  4. Bradford, J.H. (2015) Reverse‐time prestack depth migration of GPR data from topography for amplitude reconstruction in complex environments. Journal of Earth Science, 26, 791–798.
     [Google Scholar]
  5. Bradford, J.H., Privette, J., Wilkins, D. and Ford, R. (2018) Reverse‐time migration from rugged topography to image ground‐penetrating radar data in complex environments. Engineering, 4(5), 661–666.
     [Google Scholar]
  6. Caselle, C., Bonetto, S., Comina, C. and Stocco, S. (2020) GPR surveys for the prevention of karst risk in underground gypsum quarries. Tunnelling and Underground Space Technology, 95,103137.
     [Google Scholar]
  7. Chen, G., Wu, Z.Z., Wang, F.J. and Ma, Y.L. (2011) Study on the application of a comprehensive technique for geological prediction in tunneling. Environmental Earth Sciences, 62(8), 1667–1671.
     [Google Scholar]
  8. Chopra, S. and Marfurt, K.J. (2005) Seismic attributes: a historical perspective. Geophysics, 70(5), 3–28.
     [Google Scholar]
  9. Cornett, R.L. and Ernenwein, E.G. (2020) Object‐based image analysis of ground penetrating radar data for archaic hearths. Remote Sensing, 12(16), 2539.
     [Google Scholar]
  10. Dalg, S. (2000) Comparison of geological conditions predicted from tunnel boreholes and found in situ. Bulletin of Engineering Geology and the Environment, 58(2), 115–123.
     [Google Scholar]
  11. Fernandes, A.L.Jr, Medeiros, W.E., Bezerra, F.H.R., Oliveira, J.G.Jr and Cazarin, C.L. (2015) GPR investigation of karst guided by comparison with outcrop and unmanned aerial vehicle imagery. Journal of Applied Geophysics, 112, 268–278.
     [Google Scholar]
  12. Fletcher, R.P., Du, X. and Fowler, P.J. (2009) Reverse time migration in tilted transversely isotropic (TTI) media. Geophysics, 74(6), WCA179–WCA187.
     [Google Scholar]
  13. Forte, E., Pipan, M., Casabianca, D., Di Cuia, R. and Riva, A. (2012) Imaging and characterization of a carbonate hydrocarbon reservoir analogue using GPR attributes. Journal of Applied Geophysics, 81, 76–87.
     [Google Scholar]
  14. Godio, A., Dall'Ara, A.(2012) Sonic log for rock mass properties evaluation ahead of the tunnel face: a case study in the Alpine region. Journal of Applied Geophysics, 87, 71–80.
     [Google Scholar]
  15. Grandjean, G. and Gourry, J.C. (1996) GPR data processing for 3d fracture mapping in a marble quarry (Thassos, Greece). Journal of Applied Geophysics, 36(1), 19–30.
     [Google Scholar]
  16. Huang, F., Zhao, L.H., Ling, T.H. and Yang, X.L. (2017) Rock mass collapse mechanism of concealed karst cave beneath deep tunnel. International Journal of Rock Mechanics & Mining Sciences, 91, 133–138.
     [Google Scholar]
  17. Jol, H.M. (2009) Ground Penetrating Radar: Theory and Applications. Amsterdam:Elsevier Science and Technology.
     [Google Scholar]
  18. Kahraman, S., Bilgin, N. and Feridunoglu, C. (2003) Dominant rock properties affecting the penetration rate of percussive drills. International Journal of Rock Mechanics and Mining Sciences, 40(5), 711–723.
     [Google Scholar]
  19. Lavoué, F., Brossier, R., Métivier, L., Garambois, S. and Virieux, J. (2014) Two‐dimensional permittivity and conductivity imaging by full waveform inversion of multioffset GPR data: a frequency‐domain quasi‐Newton approach. Geophysical Journal International, 197(1), 248–268.
     [Google Scholar]
  20. Leung, R. and Scheding, S. (2015) Automated coal seam detection using a modulated specific energy measure in a monitor‐while‐drilling context. International Journal of Rock Mechanics & Mining Sciences, 75, 196–209.
     [Google Scholar]
  21. Li, S.C., Liu, B., Xu, X.J., Nie, L.H., Liu, Z.Y., Song, J., Sun, H.F., Chen, L. and Fan, K.R. (2017) An overview of ahead geological prospecting in tunneling. Tunnelling and Underground Space Technology, 63, 69–94.
     [Google Scholar]
  22. Li, S.C., Zhou, Z.Q., Ye, Z.H., Li, L.P., Zhang, Q.Q. and Xu, Z.H. (2015) Comprehensive geophysical prediction and treatment measures of karst caves in deep buried tunnel. Journal of Applied Geophysics, 116, 247–257.
     [Google Scholar]
  23. Liu, B., Liu, Z.Y., Li, S.C., Nie, L.C., Su, M.X., Sun, H.F., Fan, K.R., Zhang, X.X. and Pang, Y.H. (2017) Comprehensive surface geophysical investigation of karst caves ahead of the tunnel face: a case study in the Xiaoheyan section of the water supply project from Songhua river, Jilin, China. Journal of Applied Geophysics, 144, 37–49.
     [Google Scholar]
  24. Liu, M.M., Liu, Z.H., Zhou, D., Lan, R.Y. and Wu, H. (2020) Recognition method of typical anomalies during karst tunnel construction using GPR attributes and gaussian processes. Arabian Journal of Geosciences, 13(16), 1–13.
     [Google Scholar]
  25. Liu, X.F., Zheng, X.D., Xu, G.C., Wang, L. and Yang, H. (2010) Locally linear embedding‐based seismic attribute extraction and applications. Applied Geophysics, 7(4), 365–375.
     [Google Scholar]
  26. Luo, T. X.H., Lai, W.W.L., Chang, R.K.W. and Goodman, D. (2019) GPR imaging criteria. Journal of Applied Geophysics, 165, 37–48.
     [Google Scholar]
  27. McClymont, A.F., Green, A.G., Streich, R., Horstmeyer, H., Tronicke, J., Nobes, D.C., Pettinga, J., Campbell, J. and Langridge, R. (2008) Visualization of active faults using geometric attributes of 3D GPR data: an example from the Alpine Fault Zone, New Zealand. Geophysics, 73(2), B11–B23.
     [Google Scholar]
  28. Millington, T.M., Cassidy, N.J., Nuzzo, L., Crocco, L., Soldovieri, F. and Pringle, J.K. (2011) Interpreting complex, three‐dimensional, near‐surface GPR surveys: an integrated modelling and inversion approach. Near Surface Geophysics, 9, 297–304.
     [Google Scholar]
  29. Moran, M.L., Greenfield, R.J., Arcone, S.A. and Delaney, A.J. (2000) Multidimensional GPR array processing using Kirchhoff migration. Journal of Applied Geophysics, 43, 281–295.
     [Google Scholar]
  30. Okazaki, H., Nakazato, H. and Kwak, Y. (2013) Application of high‐frequency ground penetrating radar to the reconstruction of 3D sedimentary architecture in a flume model of a fluvial system. Sedimentary Geology, 293, 21–29.
     [Google Scholar]
  31. Oliveira, J.G.D.Jr, Medeiros, W.E.D., Santana, F.L.D., Bezerra, F.H.R. and Cazarin, C.L. (2020) Enhancing stratigraphic, structural and dissolution features in GPR images of carbonate karst through data processing. Near Surface Geophysics, 18, 135–148.
     [Google Scholar]
  32. Reis, J.A.Jr, De Castro, D.L., Casas, A., Himi, M. and Lima‐Filho, F.P. (2015) ERT and GPR survey of collapsed paleocave systems at the western border of the Potiguar Basin in northeast Brazil. Near Surface Geophysics,13, 369–381.
     [Google Scholar]
  33. Reis, J.A.Jr, De Castro, D.L., Jesus, T.E.S. and Lima FilhoF.P. (2014) Characterization of collapsed paleocave systems using GPR attributes. Journal of Applied Geophysics, 103, 43–56.
     [Google Scholar]
  34. Salcedo, M., Garambois, S., Bouteiller, P.L., Li, Y., Sénéchal, G., Danquigny, C. and Virieux, J. (2020) Matrix‐free crosshole elliptical‐anisotropy tomography: parametrization analysis and ground‐penetrating radar applications in carbonates. Near Surface Geophysics, 18, 697–712.
     [Google Scholar]
  35. Sass, O., Bell, R., Glade, T. (2008) Comparison of GPR, 2D‐resistivity and traditional techniques for the subsurface exploration of the Oeschingen landslide, Swabian Alb (Germany). Geomorphology, 93, 89–103.
     [Google Scholar]
  36. Schunnesson, H. (1998) Rock characterisation using percussive drilling. International Journal of Rock Mechanics and Mining Sciences, 35(6), 711–725.
     [Google Scholar]
  37. Sénéchal, G., Rousset, D., and Gaffet, S. (2013) Ground‐penetrating radar investigation inside a karstified limestone reservoir. Near Surface Geophysics, 11, 283–291.
     [Google Scholar]
  38. Szerbiak, R.B., Mcmechan, G.A., Corbeanu, R., Forster, C. and Snelgrove, S.H. (2001) 3‐D characterization of a clastic reservoir analog: From 3‐D GPR data to a 3‐D fluid permeability model. Geophysics, 66(4), 1026–1037.
     [Google Scholar]
  39. Utsi, E. (2010) GPR as an imaging device: some problems and potential solutions. 13th International Conference on Ground‐Penetrating Radar (GPR).
  40. Xue, W., Zhu, J.H., Rong, X., Huang, Y.J., Yang, Y. and Yu, Y.Y. (2017) The analysis of ground penetrating radar signal based on generalized S transform with parameters optimization. Journal of Applied Geophysics, 140, 75–83.
     [Google Scholar]
  41. Yue, Z.Q. (2014) Drilling process monitoring for refining and upgrading rock mass quality classification methods. Chinese Journal of Rock Mechanics and Engineering, 33(10), 1977–1996 (in Chinese).
     [Google Scholar]
  42. Zarei, H.R., Uromeihy, A. and Sharifzadeh, M. (2011) Evaluation of high local groundwater inflow to a rock tunnel by characterization of geological features. Tunnelling & Underground Space Technology, 26(2), 364–373.
     [Google Scholar]
  43. Zhao, W., Forte, E., Pipan, M. and Tian, G. (2013) Ground penetrating radar (GPR) attribute analysis for archaeological prospection. Journal of Applied Geophysics, 97, 107–117.
     [Google Scholar]
  44. Zhao, W., Forte, E., Fontana, F., Pipan, M. and Tian, G. (2018) GPR imaging and characterization of ancient Roman ruins in the Aquileia Archaeological Park, NE Italy. Measurement, 113, 161–171.
     [Google Scholar]
  45. Zhao, X., Zeng, Z., Zhang, Q., and Chen, X. (2019) The implication of the geophysical data and drilling records for the formation and paleoenvironment of the Hobq Desert, China. Quaternary International, 519, 42—49.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1002/nsg.12207
Loading
Detection of karst caves during tunnel construction using ground‐penetrating radar and advanced drilling: A case study in Guangxi Province, China
Near Surface Geophysics 20, 265 (2022); https://doi.org/10.1002/nsg.12207
/content/journals/10.1002/nsg.12207
/content/journals/10.1002/nsg.12207
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): 3D; GPR imaging; Karst area

Most Cited This Month Most Cited RSS feed

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