Coupled-Learning GAN for Inversion of GPR Pipe Images

Abstract

Underground pipelines are often detected and identified using ground-penetrating radar (GPR), which requires an inversion process to obtain specific pipeline properties. Unfortunately, this process is an ill-posed problem that involves inferring complex subsurface structures from a small number of observations, which leads to diverse and complex descriptions of the target. To address this challenge, this study proposes an inversion method based on a coupled-learning generative adversarial network. First, we extract hyperbolic waves from GPR buried-object B-scan images and filter out non-pipe and non-homogeneous media information from the resulting dielectric constant predictions. The training set comprises two data pairs: simulated clutter-free data with simulated clutter data pairing and simulated clutter-free data with dielectric constant data pairing. An experiment with real measured B-scan sequences showed that our proposed method provides clear and visually analyzed results about the location and direction of underground pipes. This approach enhances the accuracy and reliability of the inversion process and improves the clarity of subsurface pipeline property descriptions.