Quasi-Measurement Pitch Change: A New Framework for Machine Learning on GPR

Summary Ground-penetrating radar (GPR) is a geophysical method for non-destructive inspection of underground infrastructure. The main impediment to machine-learning-based classification of GPR data is gathering enough labeled data. Previous work done to solve this problem generated pseudo-GPR data through the numerical simulations done with expensive GPU clusters. In this paper, we propose a simple yet effective framework for machine learning with GPR data without enormous computational cost. The key idea of our method is quasi-measurement pitch changes (QMPC) that can obtain several times the amount of pseudo-data from real measurements. QMPCs are based on a simple sub-sampling procedure from real data, and no interpolation is applied for the pseudo-data. Thus, special hardware like GPU clusters are not required and no artifacts are produced by such an interpolation. Moreover, using QMPCs for test data allows us to apply ensemble learning at the inference phase of machine learning. The experimental results for the classification problem of buried objects clearly show that our framework can drastically improve accuracy with additional labeled data and without significantly increasing computational cost.