Journal Article

Deep-Learning-Based Method for Estimating Permittivity of Ground-Penetrating Radar Targets

Liu, Qinghua, Ouyang, Shan, Liao, Kefei, Wang, Hui, Zhou, Lijun

2022

Overview

Correctly estimating the relative permittivity of buried targets is crucial for accurately determining the target type, geometric size, and reconstruction of shallow surface geological structures. In order to effectively identify the dielectric properties of buried targets, on the basis of extracting the feature information of B-SCAN images, we propose an inversion method based on a deep neural network (DNN) to estimate the relative permittivity of targets. We first take the physical mechanism of ground-penetrating radar (GPR), working in the reflection measurement mode as the constrain condition, and then design a convolutional neural network (CNN) to extract the feature hyperbola of the underground target, which is used to calculate the buried depth of the target and the relative permittivity of the background medium. We further build a regression network and train the network model with the labeled sample set to estimate the relative permittivity of the target. Tests were carried out on the GPR simulation dataset and the field dataset of underground rainwater pipelines, respectively. The results show that the inversion method has high accuracy in estimating the relative permittivity of the target.

• Publisher: MDPI AG
• Subject: Approximation / Artificial neural networks / Buried structures / data collection / Datasets / Deep learning / Dielectric properties / Electrical properties / Feature extraction / Fourier transforms / Geological structures / geometry / Ground penetrating radar / ground-penetrating radar (GPR) / Hyperbolas / Machine learning / Neural networks / parameter inversion / Permittivity / Radar / Radar targets / rain / Rain water / regression network / Remote sensing / Velocity