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To augment training data for machine learning models in Ground PenetratingRadar (GPR) data classification and identification, this thesis focuses on thegeneration of realistic GPR data using Generative Adversarial Networks. Aninnovative GAN architecture is proposed for generating GPR B-scans, which is,to the author's knowledge, the first successful application of GAN to GPRB-scans. As one of the major contributions, a novel loss function is formulatedby merging frequency domain with time domain features. To test the efficacy ofgenerated B-scans, a real time object classifier is proposed to measure theperformance gain derived from augmented B-Scan images. The numerical experimentillustrated that, based on the augmented training data, the proposed GANarchitecture demonstrated a significant increase (from 82% to 98%) in theaccuracy of the object classifier.

Applying Generative Adversarial Networks to Intelligent Subsurface Imaging and Identification