Layered Media Inversion Network Applied in Ground Penetrating Radar

Renjie Liu, Yixuan Li, Peng Yin, Haoran Sun, Zengdi Bao, Xiaopeng Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Ground-penetrating radar (GPR) is a mainstream detection tool for layered media inversion. However, due to the multiple reflections and complicated refraction effect, traditional inversion method can't achieve accurate parameter inversion. For the issue, a transformer-based layered media inversion network with GPR is proposed in the paper. The network utilizes velocity spectrum as the data set to enhance feature identification and utilizes transformer-based self-attention mechanism as the backbone to reallocate attention resources based on the degree of feature importance. It focuses more attention on reflected features adaptively and maps the velocity spectrum to the permittivity image. Extensive simulated results demonstrate that the proposed network has the superiority in reconstructing the underground structure and inversing the multilayered parameters.

Original languageEnglish
Title of host publication2021 CIE International Conference on Radar, Radar 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2196-2199
Number of pages4
ISBN (Electronic)9781665498142
DOIs
Publication statusPublished - 2021
Event2021 CIE International Conference on Radar, Radar 2021 - Haikou, Hainan, China
Duration: 15 Dec 202119 Dec 2021

Publication series

NameProceedings of the IEEE Radar Conference
Volume2021-December
ISSN (Print)1097-5764
ISSN (Electronic)2375-5318

Conference

Conference2021 CIE International Conference on Radar, Radar 2021
Country/TerritoryChina
CityHaikou, Hainan
Period15/12/2119/12/21

Keywords

  • common middle point (CMP)
  • ground penetrating radar (GPR)
  • layered media inversion network
  • transformer
  • velocity spectrum

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