Guided <i>P</i>-wave extracting and imaging with underground urban seismic noise

SUMMARY Seismology has advanced significantly with ambient noise interferometry, enhancing the extraction of surface waves for detailed Vs imaging. However, direct Vp measurements remain critical for geological and engineering applications. Guided P waves offer potential for Vp insights, yet their detection from ambient noise is challenging, especially in urban environments. This study explores the feasibility of using underground urban seismic noise, specifically tunnel traffic, for guided P-wave extraction and subsurface imaging. We introduce an adaptive workflow for pre-processing and enhancing guided P-wave signals, applied to data from the Zijingang Tunnel beneath the campus of the Zhejiang University. Our refined data processing workflow significantly improves the quality of retrieved empirical Green’s functions of guided P waves. The joint inversion of surface waves and guided P waves provides high-resolution Vs and Vp profiles, revealing detailed subsurface structures. We also examine the impacts of source depth and type on guided P-wave retrieval. We observed distinct differences in urban ambient noise characteristics between daytime and nighttime, suggesting that daytime traffic noise, with higher frequency content, is more suitable for high-frequency guided P-wave retrieval. Our results demonstrate the effectiveness of using underground urban seismic noise for guided P-wave extraction, highlighting the potential applications of urban ambient noise imaging in geotechnical engineering, urban planning and seismic hazard assessment.

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Publication Details

Journal
Geophysical Journal International
Published
2025-08-12
DOI
https://doi.org/10.1093/gji/ggaf311
Citations
1
Primary Topic
Seismic Waves and Analysis
Type
article
Field-Weighted Citation Impact
1.87

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article

Guided P-wave extracting and imaging with underground urban seismic noise

Caiwang Shi, Zhengbo Li, Feng Cheng, Jianghai Xia et al.
1 citations
Geophysical Journal International
Seismic Waves and Analysis
1.87
article

Guided P-wave extracting and imaging with underground urban seismic noise

Caiwang Shi, Zhengbo Li, Feng Cheng, Jianghai Xia, Xiaofei Chen
article en
1 citations

Abstract

SUMMARY Seismology has advanced significantly with ambient noise interferometry, enhancing the extraction of surface waves for detailed Vs imaging. However, direct Vp measurements remain critical for geological and engineering applications. Guided P waves offer potential for Vp insights, yet their detection from ambient noise is challenging, especially in urban environments. This study explores the feasibility of using underground urban seismic noise, specifically tunnel traffic, for guided P-wave extraction and subsurface imaging. We introduce an adaptive workflow for pre-processing and enhancing guided P-wave signals, applied to data from the Zijingang Tunnel beneath the campus of the Zhejiang University. Our refined data processing workflow significantly improves the quality of retrieved empirical Green’s functions of guided P waves. The joint inversion of surface waves and guided P waves provides high-resolution Vs and Vp profiles, revealing detailed subsurface structures. We also examine the impacts of source depth and type on guided P-wave retrieval. We observed distinct differences in urban ambient noise characteristics between daytime and nighttime, suggesting that daytime traffic noise, with higher frequency content, is more suitable for high-frequency guided P-wave retrieval. Our results demonstrate the effectiveness of using underground urban seismic noise for guided P-wave extraction, highlighting the potential applications of urban ambient noise imaging in geotechnical engineering, urban planning and seismic hazard assessment.

Geophysical Journal InternationalVol. 243(1)
Southern University of Science and Technology (CN), Zhejiang Ocean University (CN)
National Natural Science Foundation of China, Zhejiang University
Openalex Percentile: Top 15%
Seismic Waves and Analysis
1.87
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