GPRInvNet: Deep Learning-Based Ground-Penetrating Radar Data Inversion for Tunnel Linings

Abstract

A DNN architecture called GPRInvNet is proposed to tackle the challenge of mapping Ground Penetrating Radar (GPR) B-Scan data to complex permittivity maps of subsurface structure. GPRInvNet consists of a trace-to-trace encoder and a decoder. It is specially designed to take account of the characteristics of GPR inversion when faced with complex GPR B-Scan data as well as addressing the spatial alignment issue between time-series B-Scan data and spatial permittivity maps. It fuses features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. The sensitive zone on the permittivity map spatially aligned to the enhanced trace is reconstructed accurately. GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel lining containing complex defects has been reconstructed using GPRInvNet, and results demonstrate that GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrate the superiority of the GPRInvNet. To generalize GPRInvNet to real GPR data, we integrated background noise patches recorded form a practical model testing into synthetic GPR data to train GPRInvNet. The model testing has been conducted for validation, and experimental results show that GPRInvNet achieves satisfactory results on real data.

Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

“…In this study, for the purpose of further verification of the proposed surrogate model, new data sets are generated by random noise addition [15,[22][23][24][25][26][27][28] to the generated raw Ascans. The literature offers different approaches to noise incorporation and for different purposes such as data augmentation [14,23,24,29], being closer to realistic scenarios [14,22,24,29,30] and obtaining further verification to test the sensitivity and stability of the considered models [15,[25][26][27][28]. The cases studied in [22,23] are arranged to bring the models closer to the real-time applications, specifically by considering noisy data sets.…”

“…3D GPR data generated with along an axis and perpendicular to an axis are analyzed using CNN and LSTM (Long Short-Term Memory) units together in a framework as a cascaded structure for detection of buried explosive object and discrimination target or nontarget alarms [13]. Another approach is permittivity mapping of the subsurface structures for lining detection [14,15] by using customized CNN, deep neural network frameworks. By this approach, an inversion of dielectric images can be obtained from B-scan data.…”

GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection

“…Recently, Leong and Zhu proposed a neural network model for similar inversion purposes that shows promising potential to use deep learning-based 1D zero-offset inversion to predict velocity models from GPR data [31]. Liu et al applied a DNN-based inversion process to invert the dielectric properties of tunnel linings and reconstruct complex defects with irregular geometries in their studies [32]. In contrast, for forward modeling, which predicts the response (data) from a given model, AI-based approaches to simulate the GPR for high-frequency applications have been suggested by Giannakis et al [33,34].…”

Deep Learning-Based Rebar Clutters Removal and Defect Echoes Enhancement in GPR Images

“…Ji et al [26] designed a deep neural network for the relative permittivity inversion from GPR data. Liu et al [27] proposed a GPRInvNet which was specially designed for GPR data inversion of tunnel linings to accurately reconstruct the dielectric properties of tunnel linings containing inner defects. However, at the present time, there is no relevant research regarding the removal of rebar clutters and the enhancement of defect echoes based on deep learning methods.…”