GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 14; p. 3650
• Main Authors: Zhang, Donghao, Wang, Zhengzheng, Qin, Hui, Geng, Tiesuo, Pan, Shengshan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Antennas; crosshole ground-penetrating radar (GPR); Datasets; Deep learning; Defects; Dielectric properties; finite-difference time domain (FDTD); generative adversarial network (GAN); Generative adversarial networks; Ground penetrating radar; Image reconstruction; Inverse problems; Inversion; Liquors; Machine learning; Methods; subsurface structure; Time domain analysis; Tomography; Waveforms
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15143650

The crosshole ground-penetrating radar (GPR) technique is widely used to characterize subsurface structures, yet the interpretation of crosshole GPR data involves solving non-linear and ill-posed inverse problems. In this work, we developed a generative adversarial network (GAN)-based inversion framework to translate crosshole GPR images to their corresponding 2D defect reconstruction images automatically. This approach uses fully connected layers to extract global features from crosshole GPR images and employs a series of cascaded U-Net structures to produce high-resolution defect reconstruction results. The feasibility of the proposed framework was demonstrated on a synthetic crosshole GPR dataset created with the finite-difference time-domain (FDTD) method and real-world data from a field experiment. Our inversion network obtained recognition accuracy of 91.36%, structural similarity index measure (SSIM) of 0.93, and RAscore of 91.77 on the test dataset. Furthermore, comparisons with ray-based tomography and full-waveform inversion (FWI) suggest that the proposed method provides a good balance between inversion accuracy and efficiency and has the best generalization when inverting actual measured crosshole GPR data.