3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability

Abstract

This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones.

Многоуровневые Комплексные Исследования И Мониторинг Хвостохранилищ Горнодобывающих Предприятий Северо-Западной Части Российского Сектора Арктики

“…Существенно возросло число новаций и в камеральной обработке георадарных данных, прежде всего с реализацией трехмерных систем (M. Kang, N. Kim, S. Im, J. Lee, Y. An [245]).…”

Wavelet Scattering Network-Based Machine Learning for Ground Penetrating Radar Imaging: Application in Pipeline Identification

“…An open source software, "gprMax" [23], is a favorable option to generate GPR profiles. It numerically solves Maxwell's equations by the Finite-Difference Time-Domain method [24] and offers advanced subterranean modeling, succeeding in both academic and industrial applications [25][26][27]. In this research, we simulated a stochastic number (range: 0-16) of cylinder pipelines (random diameters ranging from 5 to 40 cm) buried randomly inside a 2 m × 1 m subsurface domain and then produced 40 such GPR profiles (a non-intensive dataset) with downsampled data resolution 400 × 448.…”

Radar and Sonar Imaging and Processing

“…After space, aeronautical, marine, and land-based applications, it is now the turn of the subsurface application. Kang et al [14] proposed a three-dimensional underground cavity detection network (UcNet) to prevent the collapse of furrows in complex urban roads based on radar images (GPR). UcNet is being developed based on a convulsive neural network (CNN) integrated with the phase analysis of super-resolution GPR images.…”