  A DNN architecture referred to as GPRInvNet was proposed to tackle the
challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex
permittivity maps of subsurface structures. The GPRInvNet consisted of a
trace-to-trace encoder and a decoder. It was specially designed to take into
account the characteristics of GPR inversion when faced with complex GPR B-Scan
data, as well as addressing the spatial alignment issues between time-series
B-Scan data and spatial permittivity maps. It displayed the ability to fuse
features from several adjacent traces on the B-Scan data to enhance each trace,
and then further condense the features of each trace separately. As a result,
the sensitive zones on the permittivity maps spatially aligned to the enhanced
trace could be reconstructed accurately. The GPRInvNet has been utilized to
reconstruct the permittivity map of tunnel linings. A diverse range of
dielectric models of tunnel linings containing complex defects has been
reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet
is capable of effectively reconstructing complex tunnel lining defects with
clear boundaries. Comparative results with existing baseline methods also
demonstrated the superiority of the GPRInvNet. For the purpose of generalizing
the GPRInvNet to real GPR data, some background noise patches recorded from
practical model testing were integrated into the synthetic GPR data to retrain
the GPRInvNet. The model testing has been conducted for validation, and
experimental results revealed that the GPRInvNet had also achieved satisfactory
results with regard to the real data.
