Collaborative Imaging of Subsurface Cavities Using Ground-Pipeline Penetrating Radar

Hai Liu, Junhong Chen, Xiaoyu Zhang, Dingwu Dai, Jie Cui, Billie F. Spencer

Research output: Contribution to journalArticlepeer-review

Abstract

Cavities beneath urban roads pose a growing threat to traffic safety, mainly due to leakage from subsurface pipelines. Ground penetrating radar (GPR) and pipe penetrating radar (PPR) have become widely adopted tools for the detection of cavities. However, a notable limitation of both GPR and PPR lies in their inability to clearly delineate the top and bottom of cavities. This letter introduces a collaborative detection technique that employs both GPR and PPR. Subsequently, a collaborative imaging method is proposed, derived separately from GPR and PPR data, utilizing a reverse-time migration (RTM) algorithm with a zero-lag cross correlation imaging condition. Laboratory experimental results show that the artifacts caused by the RTM are significantly suppressed through the application of cross correlation imaging. As such, the proposed technique allows clear imaging of both the top and bottom of cavities around a pipeline. It is concluded that the proposed technique can enhance the capability of GPR in the detection and characterization of subsurface cavities.

Original languageEnglish (US)
Article number3002205
Pages (from-to)1-5
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume21
DOIs
StatePublished - 2024
Externally publishedYes

Keywords

  • Collaborative imaging
  • ground penetrating radar (GPR)
  • pipe penetrating radar (PPR)
  • reverse-time migration (RTM)

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

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