Mapping the Buried Cable by Ground Penetrating Radar and Gaussian-Process Regression

TL;DR

This paper introduces a novel method combining Ground Penetrating Radar and Gaussian-process regression to accurately locate underground cables, accounting for noise and environmental variations, with demonstrated effectiveness on real datasets.

Contribution

The paper proposes a new approach integrating GPR data analysis and Gaussian-process regression for underground cable detection, improving robustness and accuracy.

Findings

Effective cable location with confidence intervals

Robustness to noise and environmental factors

Validated on real-world datasets

Abstract

With the rapid expansion of urban areas and the increasingly use of electricity, the need for locating buried cables is becoming urgent. In this paper, a noval method to locate underground cables based on Ground Penetrating Radar (GPR) and Gaussian-process regression is proposed. Firstly, the coordinate system of the detected area is conducted, and the input and output of locating buried cables are determined. The GPR is moved along the established parallel detection lines, and the hyperbolic signatures generated by buried cables are identified and fitted, thus the positions and depths of some points on the cable could be derived. On the basis of the established coordinate system and the derived points on the cable, the clustering method and cable fitting algorithm based on Gaussian-process regression are proposed to find the most likely locations of the underground cables. Furthermore, the confidence intervals of the cable's locations are also obtained. Both the position and depth noises are taken into account in our method, ensuring the robustness and feasibility in different environments and equipments. Experiments on real-world datasets are conducted, and the obtained results demonstrate the effectiveness of the proposed method.