GPR electromagnetic inversion method based on multi-scan multi-frequency data and deep learning

Shiguang Luo; Qiang Ren; Wentai Lei; Qian Song; Lingqing Mao; Shuo Zhang; Yiwei Wang; Jiabin Luo; Long Xu

doi:10.1117/12.2616084

15 February 2022 GPR electromagnetic inversion method based on multi-scan multi-frequency data and deep learning

Shiguang Luo, Qiang Ren, Wentai Lei, Qian Song, Lingqing Mao, Shuo Zhang, Yiwei Wang, Jiabin Luo, Long Xu

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 1216631 (2022) https://doi.org/10.1117/12.2616084
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

Subsurface imaging technique has good application value in the field of Ground Penetrating Radar (GPR). Electromagnetic Inversion (EI) can reconstruct the shape distribution of buried objects and has become an important research direction of underground target imaging. This paper presents a GPR EI method based on GPR Multi-Frequency (MF) data and A-Unet deep learning framework. Firstly, GPR B-scan data are collected by real aperture or synthetic aperture and then pre-processed by using background removal and denoising technique. Secondly, a A-Unet deep learning network is designed to achieve underground target imaging. It’s input data is multi-scan MF amplitude and phase data extracted from pre-processed GPR B-Scan data, while it’s output is underground dielectric parameters distribution in a designated regime. This A-Unet compose of a data extraction unit and a data expansion unit. The data extraction unit is characterized by replacing the skip-connection structure of Unet with an add-structure, which improves network computing efficiency. The data expansion unit is used to improve the resolution of electrical permittivity distribution. Numerical simulation experiments have proved that this method effectively reconstructs the shape distribution of underground targets, and the training time of add-structure is shortened to 9.09% of the training time of skip-connection unit while without reducing the imaging resolution.

Citation Download Citation

Shiguang Luo, Qiang Ren, Wentai Lei, Qian Song, Lingqing Mao, Shuo Zhang, Yiwei Wang, Jiabin Luo, and Long Xu "GPR electromagnetic inversion method based on multi-scan multi-frequency data and deep learning", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 1216631 (15 February 2022); https://doi.org/10.1117/12.2616084

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KEYWORDS

Electromagnetism

Data modeling

Dielectrics

Scattering

Neural networks

Antennas

Numerical simulations

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