Ground penetrating radar (GPR) is a non-destructive detection method, which is widely used in shallow underground target detection. The development of machine learning and artificial intelligence technology has promoted the development of GPR data processing at the aspect of automatic and intelligent. This paper proposes a neural network for recognizing the characteristics of underground targets scattering curve from GPR B-scan data. Firstly, GPR B-scan echo data is preprocessed in three steps: data normalization, data standardization and sample division to product samples. Secondly, a new neural network is designed to identify the characteristic points of scattering curve in three adjacent A-scan echo data. According to characteristic point categories, underground targets location and depth can be obtained by adopting the corresponding relationship between time delay and depth. Compared with the existing methods, this method has the advantages of automation, rapid processing and no need for expert decision-making. Simulation and on-site data processing experiments are carried out and its ability on accuracy and higher position estimation accuracy for incomplete data is verified.
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.
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