自适应同步压缩变换在隧道探地雷达超前地质预报中的应用

doi:10.11720/wtyht.2025.0221

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Abstract

Advance geological prediction in tunnels faces technical challenges,including strong non-stationarity of ground-penetrating radar(GPR) signals and insufficient resolution of conventional time-frequency analyses.Hence,this study proposed an improved method based on adaptive local maximum synchrosqueezing transform(LMSST).The proposed method significantly enhanced the time-frequency resolution and noise robustness of traditional LMSST through a dynamic bandwidth optimization algorithm and local extremum search strategies.Theoretical analysis and synthetic signal testing demonstrated the superior time-frequency energy concentration characteristics of the proposed method in analyzing cross-frequency modulation components.Furthermore,the proposed method was applied to the karst tunnel section of a high-speed railway in Southwest China.Combined with the GprMax forward modeling and GPR measurements,the proposed method successfully identified geological anomalies such as karst caves.Subsequent excavation verification confirmed the identification accuracy,with positional errors of anomaly boundaries below 0.3 m.Overall,the results of this study suggest the proposed method's efficiency in enhancing time-frequency resolution and substantial engineering applicability,offering reliable technical support for tunnel construction safety in karst areas.

Key words： ground-penetrating radar(GPR) synchrosqueezing transform(SST) advance geological prediction time-frequency analysis

Received: 22 June 2025 Published: 30 December 2025

ZTFLH:

P631.3

Corresponding Authors: TIAN Ren-Fei E-mail: 30113038@qq.com;tianfei906@163.com

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	Wen-De MA
	Ren-Fei TIAN
	Wei ZHENG

Cite this article:

Wen-De MA,Ren-Fei TIAN,Wei ZHENG. Application of adaptive synchrosqueezing transform in ground-penetrating radar-based advance geological prediction in tunnels[J]. Geophysical and Geochemical Exploration, 2025, 49(6): 1449-1458.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2025.0221 OR https://www.wutanyuhuatan.com/EN/Y2025/V49/I6/1449

Adaptive LMSST algorithm flow chart

Synthetic chirp signal and its corresponding instantaneous frequency

Comparison of different time-frequency analysis methods

Fig.3
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Partial enlargement of the time range 1.0~1.3 s in Fig.3

Comparison of various time-frequency analysis methods with noise

Fig.5
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Partial enlargement of the time range 1.0~1.4 s in Fig.5

Ground-penetrating radar geological model and corresponding forward modeling records

Comparison of time-frequency analysis methods for single-channel ground penetrating radar data

Peak amplitude profiles extracted by different time-frequency analysis methods

Ground-penetrating radar

Comparison of time-frequency analysis methods for measured single-trace ground penetrating radar

Peak amplitude profiles extracted by different time-frequency analysis methods

RGB fusion visualization

Face excavation photo at the working site

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