Ground Penetrating Radar

Ground-penetrating radar (GPR) is a non-destructive technique used to image subsurface structures by analyzing electromagnetic wave reflections. Current research emphasizes improving GPR data analysis through machine learning, particularly convolutional neural networks (CNNs) and support vector machines (SVMs), to enhance the accuracy and efficiency of detecting and classifying subsurface features like soil properties, pavement layers, and subgrade distresses. This is driven by the need for more robust and automated interpretation of GPR data across diverse applications, ranging from agriculture and civil engineering to archaeology and environmental monitoring. The development of novel data augmentation techniques, such as using synthetic data to fine-tune CNNs, and multi-view fusion methods for 3D GPR data are also significant areas of advancement.