1887
Volume 73, Issue 8
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Urban underground tunnelling faces challenges from small‐scale unfavourable geological bodies such as boulders and karst caves, the diameters of which are less than 1 m mostly. To address this issue, the tunnel‐array acoustic wave prospecting technique has been proposed. It utilizes piezoelectric transducers to excite acoustic waves with a central frequency of 4000 Hz, enabling the detection of small‐scale unfavourable geological bodies ahead of tunnel. However, due to the excavation by the shield cutterhead, the cracks and fissures in the rock mass near the cutterhead will significantly develop, forming a disturbed zone with high inhomogeneity. The existence of the disturbed zone will cause severe multiple scattering, which induces artefacts in the imaging results and reduces the accuracy of the advanced prospecting results. In terms of above issues, we introduce the idea of multiple signal classification (MUSIC) algorithm into the reflection matrix method and propose a novel MUSIC algorithm–based reflection matrix method. The reflection matrix can achieve the imaging of reflectors through re‐projecting the acquired data into the media at excitation and reception using Green's function. But it cannot deal with the artefacts induced by multiple scattering. The idea of MUSIC algorithm is to calculate the correlation between Green's function and the singular vectors of the signal or noise subspace, which are obtained by singular value decomposition (SVD) of covariance matrix of the acquired data, achieving estimation of the reflectors. Referring to this idea, we further improved the reflection matrix using MUSIC algorithm. The reflection matrix method is applied first, and the reflection matrix is obtained. Then by SVD of covariance matrix of the reflection matrix, we obtain the signal vectors related to the imaging results of reflectors and noise vectors related to artefacts. The signal vectors are used to calculate the correlation with an imaging operator , which is derived from the product of the conjugate of Green's function and itself. When the computing grid within reflectors, the results reach the local maximum; otherwise, it tends to 0. In this way, we mitigate the imaging artefacts introduced by the multiple scattering. Through synthetic experiment, we verified that the proposed method can effectively suppress the imaging noise and improve resolution of the imaging results compared to the reflection matrix method. Finally, the proposed method was applied on field data obtained in Zhanmatun Iron Mine and successfully predicted the interface of the opposite tunnel in the target area.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.70096
2025-10-24
2025-11-09
Loading full text...

Full text loading...

References

  1. Aubry, A., and A.Derode. 2009a. “Detection and Imaging in a Random Medium: A Matrix Method to Overcome Multiple Scattering and Aberration.” Journal of Applied Physics106, no. 4: 044903.
    [Google Scholar]
  2. Aubry, A., and A.Derode. 2009b. “Random Matrix Theory Applied to Acoustic Backscattering and Imaging in Complex Media.” Physical Review Letters102, no. 8: 084301.
    [Google Scholar]
  3. Badon, A., D.Li, G.Lerosey, A. C.Boccara, M.Fink, and A.Aubry. 2016. “Smart Optical Coherence Tomography for Ultra‐Deep Imaging Through Highly Scattering Media.” Science Advances2, no. 11: e1600370.
    [Google Scholar]
  4. Baysal, E., D.Kosloff, and W.Sherwood. 1983. “Reverse Time Migration.” Geophysics48, no. 11: 1514–1524.
    [Google Scholar]
  5. Blondel, T., J.Chaput, A.Derode, M.Campillo, and A.Aubry. 2018. “Matrix Approach of Seismic Imaging: Application to the Erebus Volcano, Antarctica.” Journal of Geophysical Research: Solid Earth123, no. 12: 10–936.
    [Google Scholar]
  6. Bureau, F., J.Robin, A.Le Ber, W.Lambert, M.Fink, and A.Aubry. 2023. “Three‐Dimensional Ultrasound Matrix Imaging.” Nature Communications14, no. 1: 6793.
    [Google Scholar]
  7. Chun, H., and A.Jacewitz. 1981. “Fundamentals of Frequency Domain Migration.” Geophysics46, no. 5: 717–733.
    [Google Scholar]
  8. Claerbout, J. F.1985. Imaging the Earth's Interior. Blackwell Scientific Publications.
    [Google Scholar]
  9. Eranti, P. K., and B. DBarkana. 2022. “An Overview of Direction‐of‐Arrival Estimation Methods Using Adaptive Directional Time‐Frequency Distributions.” Electronics11, no. 9: 1321.
    [Google Scholar]
  10. Giraudat, E., A.Burtin, A.Le Ber, M.Fink, J.Komorowski, and A.Aubry. 2024. “Matrix Imaging as a Tool for High‐Resolution Monitoring of Deep Volcanic Plumbing Systems With Seismic Noise.” Communications Earth & Environment5, no. 1: 509.
    [Google Scholar]
  11. Hu, M., H.Zhou, Y.Zhang, et al. 2019. “Acoustic Emission Monitoring on Damage Evolution of Surrounding Rock During Headrace Tunnel Excavation by TBM.” European Journal of Environmental and Civil Engineering23, no. 10: 1248–1264.
    [Google Scholar]
  12. Lambert, W., L. A.Cobus, M.Couade, M.Fink, and A.Aubry. 2020. “Reflection Matrix Approach for Quantitative Imaging of Scattering Media.” Physical Review X10, no. 2: 021048.
    [Google Scholar]
  13. Lambert, W., L. A.Cobus, J.Robin, M.Fink, and A.Aubry. 2022. “Ultrasound Matrix Imaging—Part II: The Distortion Matrix for Aberration Correction Over Multiple Isoplanatic Patches.” IEEE Transactions on Medical Imaging41, no. 12: 3921–3938.
    [Google Scholar]
  14. Lambert, W., J.Robin, L. A.Cobus, M.Fink, and A.Aubry. 2022. “Ultrasound Matrix Imaging—Part I: The Focused Reflection Matrix, the F‐Factor and the Role of Multiple Scattering.” IEEE Transactions on Medical Imaging41, no. 12: 3907–3920.
    [Google Scholar]
  15. Li, H., Q.Li, and Y.Lu. 2017. “Statistical Analysis on Regularity of Subway Construction Accidents From 2002 to 2016 in China.” Urban Rapid Rail Transit30, no. 1: 12–19.
    [Google Scholar]
  16. Li, S., L.Chen, B.Liu, X.Xu, L.Liu, and Y.Chen. 2022. “Geologic Forward Prospecting Using Improved Tunnel‐Seismic‐While‐Drilling Method: A Case Study of the Water Supply Project at Songhua River. Jilin, China.” Geophysics87, no. 2: B93–B104.
    [Google Scholar]
  17. Li, S., B.Liu, X.Xu, et al. 2017. “An Overview of Ahead Geological Prospecting in Tunneling.” Tunnelling and Underground Space Technology63: 69–94.
    [Google Scholar]
  18. Li, S., B.Liu, X.Xu, L.Chen, C.Fu, and L.Hao. 2023. System and Method for Phased Array Sound Wave Advanced Geological Exploration for Shield Tunneling Machine. (US Patent No. 11,640,007). US Patent and Trademark Office.
  19. Li, S., H.Sun, X.Lu, and X.Li. 2014. “Three‐Dimensional Modeling of Transient Electromagnetic Responses of Water‐Bearing Structures in Front of a Tunnel Face.” Journal of Environmental and Engineering Geophysics19, no. 1: 13–32.
    [Google Scholar]
  20. Liu, B., C.Fu, Y.Ren, Q.Zhang, X.Xu, and Y.Chen. 2020. “Structural Complexity‐Guided Predictive Filtering.” Geophysical Prospecting68, no. 5: 1509–1522.
    [Google Scholar]
  21. Liu, B., F.Zhang, S.Li, et al. 2018. “Forward Modelling and Imaging of Ground‐Penetrating Radar in Tunnel Ahead Geological Prospecting.” Geophysical Prospecting66, no. 4: 784–797.
    [Google Scholar]
  22. Liu, K., J.Wu, J.Cao, et al. 2024. “Denoise Method for Reflection Matrix Optical Coherence Tomography.” Optics Communications569: 130746.
    [Google Scholar]
  23. Schmidt, R.1986. “Multiple Emitter Location and Signal Parameter Estimation.” IEEE Transactions on Antennas and Propagation34, no. 3: 276–280.
    [Google Scholar]
  24. Stolt, R. H.1978. “Migration by Fourier transform.” Geophysics, 43, no. 1: 23–48.
    [Google Scholar]
  25. Su, M., Y.Zhao, Y.Xue, et al. 2021. “Progressive Fine Integrated Geophysical Method for Karst Detection During Subway Construction.” Pure and Applied Geophysics178: 91–106.
    [Google Scholar]
  26. Tang, S., X.Zhang, H.Liu, et al. 2021. “Engineering Difficulties and Key Technologies for Underwater Shield Tunnel in Complex Ground.” Journal of Engineering Geology29, no. 5: 1477–1487.
    [Google Scholar]
  27. Touma, R., A.Aubry, Y.Ben‐Zion, and M.Campillo. 2022. “Distribution of Seismic Scatterers in the San Jacinto Fault Zone, Southeast of Anza, California, Based on Passive Matrix Imaging.” Earth and Planetary Science Letters578: 117304.
    [Google Scholar]
  28. Xu, C., B.Di, and J.Wei. 2016. “A Physical Modeling Study of Seismic Features of Karst Cave Reservoirs in the Tarim Basin, China.” Geophysics81, no. 1: B31–B41.
    [Google Scholar]
  29. Zhang, Q., K.Yang, L.Wang, and S.Zhou. 2020. “Geological Type Recognition by Machine Learning on In‐Situ Data of EPB Tunnel Boring Machines.” Mathematical Problems in Engineering2020, no. 1: 3057893.
    [Google Scholar]
  30. Zhang, Y., Z.Liu, P.Bai, et al. 2023. “Multi‐Borehole Three‐Dimensional Induced Polarization Tomography Method for Tunnel Water Hazards Ahead Prospecting.” Tunnelling and Underground Space Technology133: 104952.
    [Google Scholar]
  31. Zhao, C., E.Mahmoudi, M.Luo, M.Jiang, and P.Lin. 2023. “Unfavorable Geology Recognition in Front of Shallow Tunnel Face Using Machine Learning.” Computers and Geotechnics157: 105313.
    [Google Scholar]
  32. Zheng, D., J.Wang, Y.Xiao, W.Xiao, and L.Gao. 2023. “Seismic Characteristics Analysis of Karst Cavity Reservoirs Based on Seismic Numerical Simulation.” Journal of Southwest Petroleum University (Science & Technology Edition)45: 57–68.
    [Google Scholar]
  33. Zhou, H. W., H.Hu, Z.Zou, Y.Wo, and O.Youn. 2018. “Reverse Time Migration: A Prospect of Seismic Imaging Methodology.” Earth‐Science Reviews179: 207–227.
    [Google Scholar]
/content/journals/10.1111/1365-2478.70096
Loading
/content/journals/10.1111/1365-2478.70096
Loading

Data & Media loading...

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