1887
Volume 23, Issue 1
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

Abstract

The direct current (DC) resistivity method is extensively used to predict water‐inrush disasters in tunnel prospecting. However, during DC resistivity inversion, different initial models can significantly affect the inversion results, often resulting in convergence at a local optimum. To overcome these challenges, we propose a new method for DC data inversion that uses prior information as a reference model. First, the resistivity distribution of the surrounding rock mass was estimated based on detailed geological analysis. Next, an initial homogeneous resistivity model was constructed by averaging the observed tunnel resistivity values. Finally, the initial model was developed by incorporating borehole rock samples and water content data. The effectiveness of the method' was assessed using a series of synthetic models of typical water‐bearing structures. We then applied this approach to the Laomacao Tunnel in the Yunnan Central Water Diversion Project (southwestern China), where drilling data were used as a priori information to optimize the initial model together with the average tunnel resistivity values, successfully identifying the water‐bearing structure ahead of the tunnel face. Overall, the proposed method enhances the understanding of sudden surges, aiding in the prevention and control of water disasters in tunnels.

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/content/journals/10.1002/nsg.12332
2025-01-21
2025-02-19
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References

  1. Alija, S., Torrijo, F.J. & Quinta‐Ferreira, M. (2013) Geological engineering problems associated with tunnel construction in karst rock masses: the case of Gavarres tunnel (Spain). Engineering Geology, 157, 103–111.
    [Google Scholar]
  2. Caterina, D., Hermans, T. & Nguyen, F. (2014) Case studies of incorporation of prior information in electrical resistivity tomography: comparison of different approaches. Near Surface Geophysics12, 451–465.
    [Google Scholar]
  3. Deng, Z., Li, Z., Nie, L., Zhang, S., et al. (2024) Forward and inversion approach for direct current resistivity based on an unstructured mesh and its application to tunnel engineering. Geophysical Prospecting, 72(6), 1–16.
    [Google Scholar]
  4. Ducut, J. D., Alipio, M., Go, P.J., Concepcion Ii, R., Vicerra, R. R., Bandala, A., et al. (2022) A review of electrical resistivity tomography applications in underground imaging and object detection. Displays, 73, 102208.
    [Google Scholar]
  5. Günther, T., Rücker, C. & Spitzer, K., (2006) Three‐dimensional modeling and inversion of DC resistivity data incorporating topography—II. Inversion. Geophysical Journal International, 166, 506–517.
    [Google Scholar]
  6. Holmøy, K.H. & Nilsen, B. (2014) Significance of geological parameters for predicting water inflow in hard rock tunnels. Rock Mechanics and Rock Engineering, 47, 853–868.
    [Google Scholar]
  7. Hu, D., Tezkan, B., Yue, M., Yang, X. & Wu, X. (2022) A new method of 3D direct current resistivity modelling using a long electrode source for forward probing in tunnels. Near Surface Geophysics20(6), 590–606.
    [Google Scholar]
  8. Hu, D., Yang, X., Yue, M., Li, Y. & Wu, X. (2021) Prediction model for advanced detection of water‐rich faults using 3D anisotropic resistivity modeling and Monte Carlo methods. IEEE Access, 9, 18251–18261.
    [Google Scholar]
  9. Junaid, M., Abdullah, R. A., Sa'ari, R., Ail, W., Lslam, A., & Sari, M. (2022) 3D modelling and feasibility assessment of granite deposit using 2D electrical resistivity tomography, borehole, and unmanned aerial vehicle survey. Journal of Mining and Environment, 13,929–942.
    [Google Scholar]
  10. Kim, H. J., Song, Y. & Lee, K. H., (1999) Inequality constraint in least‐squares inversion of geophysical data. Earth, Planets and Space, 51, 255–259.
    [Google Scholar]
  11. Lines, L. R. & Treitel, S. (1984) A review of least‐squares inversion and its application to geophysical problems. Geophysical Prospecting, 32, 159–186.
    [Google Scholar]
  12. Lin, J., Zhu, J., Wang, H., Teng, F. & Zhang, Y. (2020) A review on the progress of the underground nuclear magnetic resonance method for groundwater disaster forecasting detection of tunnels and mines. Journal of Applied Geophysics, 177, 104041.
    [Google Scholar]
  13. Liu, B., Guo, Q., Liu, Z., Wang, C., Nie, L., Xu, X. & Chen, L. (2019) Comprehensive ahead prospecting for hard rock TBM tunneling in complex limestone geology: a case study in Jilin, China. Tunnelling and Underground Space Technology, 93, 103045.
    [Google Scholar]
  14. Loke, M.H. & Barker, R.D. (1996) Rapid least‐squares inversion of apparent resistivity pseudosections by a quasi‐Newton method1. Geophysical Prospecting44, 131–152.
    [Google Scholar]
  15. Mitchell, M.A. & Oldenburg, D.W., (2023) Using DC resistivity ring array surveys to resolve conductive structures around tunnels or mine‐workings. Journal of Applied Geophysics211, 104949.
    [Google Scholar]
  16. Nie, L., Deng, Z., Li, Z.‐Q., Wang, T., Han, L. & Li, Y. (2024) Detection method for water‐bearing structure in tunnel by DC resistivity based on directional indicating variable mesh. Tunnelling and Underground Space Technology, 149, 105820.
    [Google Scholar]
  17. Nie, L., Wang, C., Liu, Z., Xu, Z., Sun, X. & Du, Y., et al. (2023) An integrated geological and geophysical approach to identify water‐rich weathered granite areas during twin tunnels construction: a case study. Tunnelling and Underground Space Technology, 135, 105025.
    [Google Scholar]
  18. Olayinka, A. I. & Yaramanci, U., (2000) Assessment of the reliability of 2D inversion of apparent resistivity data. Geophysical Prospecting, 48, 293–316.
    [Google Scholar]
  19. Parnow, S., Oskooi, B. & Florio, G. (2021) Improved linear inversion of low induction number electromagnetic data. Geophysical Journal International, 224, 1505–1522.
    [Google Scholar]
  20. Park, J., Lee, K.‐H., Park, J., Choi, H. & Lee, I.‐M. (2016) Predicting anomalous zone ahead of tunnel face utilizing electrical resistivity: I. Algorithm and measuring system development. Tunnelling and Underground Space Technology, 60, 141–150.
    [Google Scholar]
  21. Schwenk, J. T., Sloan, S. D., Ivanov, J. & Miller, R. D. (2016) Surfacewave methods for anomaly detection. Geophysics, 81, EN29–EN42.
    [Google Scholar]
  22. Şener, A., Pekşen, E. & Yolcubal, I. (2021) Application of square array configuration and electrical resistivity tomography for characterization of the recharge area of karst aquifer: a case study from Meneksÿe karst plateau (Kocaeli, Turkey). Journal of Applied Geophysics, 195, 104474.
    [Google Scholar]
  23. Slezak, K., Jozwiak, W., Nowozynski, K., Orynski, S. & Brasse, H. (2019) 3‐D studies of MT data in the Central Polish Basin: influence of inversion parameters, model space and transfer function selection. Journal of Applied Geophysics161, 26–36.
    [Google Scholar]
  24. Sloan, S D., Peterie, S L., Miller, R D., Ivanov, J., Schwenk, J. T & Mckenna, J R. (2015) Detecting clandestine tunnels using near surface seismic techniques. Geophysics, 80, EN127–EN135.
    [Google Scholar]
  25. Tao, T., Han, P., Yang, X.‐H., Zu, Q., Hu, K., Mo, S., et al. (2024) Fast initial model design for electrical resistivity inversion by using broad learning framework. Minerals, 14, 184.
    [Google Scholar]
  26. Tietze, K., Ritter, O. & Egbert, G. D. (2015) 3‐D joint inversion of the magnetotelluric phase tensor and vertical magnetic transfer functions. Geophysical Journal International, 203, 1128–1148.
    [Google Scholar]
  27. Varfinezhad, R., Fedi, M. & Milano, M., (2022) The role of model weighting functions in the gravity and DC resistivity inversion. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–15.
    [Google Scholar]
  28. Varfinezhad, R., Parnow, S., Florio, G., Fedi, M. & Mohammadi Vizheh, M. (2023) DC resistivity inversion constrained by magnetic method through sequential inversion. Acta Geophysica, 71, 247–260.
    [Google Scholar]
  29. Vu, M. T. & Jardani, A. (2021) Convolutional neural networks with SegNet architecture applied to three‐dimensional tomography of subsurface electrical resistivity: CNN‐3D‐ERT. Geophysical Journal International, 225, 1319–1331.
    [Google Scholar]
  30. Wang, H. & Lin, C. P., (2018) Cause and countermeasures for the symmetric effect in borehole‐to‐surface electrical resistivity tomography. Journal of Applied Geophysics, 159, 248–259.
    [Google Scholar]
  31. White, A., Wilkinson, P., Boyd, J., Wookey, J., Kendall, J. M., Binley, A., et al. (2023) Combined electrical resistivity tomography and ground penetrating radar to map Eurasian badger (Meles Meles) burrows in clay‐rich flood embankments. Engineering Geology, 323, 107198.
    [Google Scholar]
  32. Wilson, B., Singh, A. & Sethi, A. (2022) Appraisal of resistivity inversion models with convolutional variational encoder–decoder network. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–10.
    [Google Scholar]
  33. Wunderlich, T., Fischer, P., Wilken, D., Hadler, H., Erkul, E., Mecking, R., et al. (2018) Constraining electric resistivity tomography by direct push electric conductivity logs and vibracores: an exemplary study of the Fiume Morto silted riverbed (Ostia Antica, western Italy). Geophysics, 83, B87–B103.
    [Google Scholar]
  34. Xue, Y., Kong, F., Qiu, D., Su, M., Zhao, Y. & Zhang, K. (2021) The classifications of water and mud/rock inrush hazard: a review and update. Bulletin of Engineering Geology and the Environment, 80, 1907–1925.
    [Google Scholar]
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  • Article Type: Research Article
Keyword(s): drilling; electrical resistivity; initial model; tunnel prospecting

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