Experimental study on real-time monitoring of surrounding rock 3D wave velocity structure and failure zone in deep tunnels

Abstract

An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction. The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio (SNR) and anti-interference capability compared to conventional borehole placement. Subsequently, a 3D wave velocity evolution model that incorporates construction-induced disturbances was established, enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity. It finds significant wave velocity reduction near the tunnel face, with roof and floor damage zones extending 40–50 m; wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side; pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right, with a clear stress concentration or transition zone located 10–15 m; and systematically lower velocities behind the face than in front, indicating asymmetric rock damage development. These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies, enabling real-time adjustment of support parameters, and establishing safety early warning systems in deep-buried tunnel engineering.

Keywords

Deep-buried tunnel / Microseismic monitoring / Wave velocity tomography / Surrounding rock damage zone / Real-time monitoring

CRediT authorship contribution statement

Hongyun Yang: Writing – original draft, Software, Methodology, Data curation. Chuandong Jiang: Software, Formal analysis, Data curation. Yong Li: Resources, Methodology, Funding acquisition. Zhi Lin: Supervision, Project administration. Xiang Wang: Methodology, Data curation. Yifei Wu: Resources, Data curation. Wanlin Feng: Software, Investigation, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors would like to acknowledge the support of the National Natural Science Foundation of China (No. 52274176), the Guangdong Province Key Areas R&D Program (No. 2022B0101070001), Chongqing Elite Innovation and Entrepreneurship Leading talent Project (No. CQYC20220302517), the Chongqing Natural Science Foundation Innovation and Development Joint Fund (No. CSTB2022NSCQ-LZX0079), the National Key Research and Development Program Young Scientists Project (No. 2022YFC2905700), the Chongqing Municipal Education Commission ‘‘Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area” Science and Technology Innovation Project (No. KJCX2020031), the Fundamental Research Funds for the Central Universities (No. 2024CDJGF-009), and the Key Project for Technological Innovation and Application Development in Chongqing (No. CSTB2025TIAD-KPX0029).

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