Mapping Subsurface Utility Pipes by 3-D Convolutional Neural Network and Kirchhoff Migration Using GPR Images

In this article, we focus on ground-penetrating radar (CPR) for subsurface utility pipe detection. Due to the dense and high-speed 3-D monitoring, GPR is a promising tool. However, because of enormous amount of radar data and difficulty of interpretation, inspection time and cost are the bottlenecks. In this article, we propose a novel detection algorithm by the combination of 3-D convolutional neural network (3-D-CNN) and Kirchhoff migration. A 3-D-CNN architecture was trained utilizing transverse and longitudinal pipes' measurement data. The classification accuracy of the developed model was about 91%, accurately estimating the pipes' existences and directions. The 3-D-CNN improved the classification accuracy by about 6% compared to 2-D-CNN in the case of transverse pipes by considering the 3-D geometries of the pipes. After box by-box search by 3-D-CNN, Kirchhoff migration was applied to cross section images and peaks were extracted. From the result of experimental field data, the algorithm provides the clear understandings of pipes' 3-D positions and arrangement with reasonable calculation time.

Automated summary

This article introduces a novel algorithm that combines 3-D convolutional neural network (3-D-CNN) and Kirchhoff migration for subsurface utility pipe detection using ground-penetrating radar (CPR). The developed model achieved a classification accuracy of about 91%, accurately estimating the pipes' existences and directions. The algorithm provides a clear understanding of pipes' 3-D positions and arrangement with reasonable calculation time based on experimental field data.