Subsurface Defect Detection in GPR Data Integrating Temporal and Spatial Features

Abstract

Ground penetrating radar (GPR) has emerged as a pivotal tool for subsurface explorations, particularly in detecting subsurface defects that might endanger structural integrity. While GPR B-scan data visually depict underground conditions, it represents the time delay and amplitude of the returned electromagnetic (EM) waves, making them complex to interpret due to both their image-like appearance and their inherent waveform changes. To address this complexity, this article introduces the novel temporal–spatial synthesis network, designed to harness both temporal and spatial features for enhanced subsurface defect detection in GPR B-scan data. The echo state network, underpinned by reservoir computing, is utilized to fit the GPR data and capture its “temporal features,” emphasizing the temporal variations present in the EM waves. Concurrently, the convolutional neural network focuses on discerning “spatial features” from the B-scan images, spotlighting spatial patterns that possibly indicate subsurface defects. After extracting these temporal and spatial features, they are synthesized to form a comprehensive representation of the GPR data. The enhanced synthesized feature facilitates precise classification, resulting in heightened differentiation between normal and defect-contained subsurface areas. Experiments on real-world GPR datasets are conducted, with the results underscoring the efficacy of the proposed approach.

Keywords

• B-scan data
• convolutional neural network (CNN)
• echo state network (ESN)
• ground penetrating radar (GPR)