3 research outputs found
3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion
The reconstruction of the 3D permittivity map from ground-penetrating radar
(GPR) data is of great importance for mapping subsurface environments and
inspecting underground structural integrity. Traditional iterative 3D
reconstruction algorithms suffer from strong non-linearity, ill-posedness, and
high computational cost. To tackle these issues, a 3D deep learning scheme,
called 3DInvNet, is proposed to reconstruct 3D permittivity maps from GPR
C-scans. The proposed scheme leverages a prior 3D convolutional neural network
with a feature attention mechanism to suppress the noise in the C-scans due to
subsurface heterogeneous soil environments. Then a 3D U-shaped encoder-decoder
network with multi-scale feature aggregation modules is designed to establish
the optimal inverse mapping from the denoised C-scans to 3D permittivity maps.
Furthermore, a three-step separate learning strategy is employed to pre-train
and fine-tune the networks. The proposed scheme is applied to numerical
simulation as well as real measurement data. The quantitative and qualitative
results show the network capability, generalizability, and robustness in
denoising GPR C-scans and reconstructing 3D permittivity maps of subsurface
objects
DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditions
Traditional ground-penetrating radar (GPR) data inversion leverages iterative
algorithms which suffer from high computation costs and low accuracy when
applied to complex subsurface scenarios. Existing deep learning-based methods
focus on the ideal homogeneous subsurface environments and ignore the
interference due to clutters and noise in real-world heterogeneous
environments. To address these issues, a two-stage deep neural network (DNN),
called DMRF-UNet, is proposed to reconstruct the permittivity distributions of
subsurface objects from GPR B-scans under heterogeneous soil conditions. In the
first stage, a U-shape DNN with multi-receptive-field convolutions (MRF-UNet1)
is built to remove the clutters due to inhomogeneity of the heterogeneous soil.
Then the denoised B-scan from the MRF-UNet1 is combined with the noisy B-scan
to be inputted to the DNN in the second stage (MRF-UNet2). The MRF-UNet2 learns
the inverse mapping relationship and reconstructs the permittivity distribution
of subsurface objects. To avoid information loss, an end-to-end training method
combining the loss functions of two stages is introduced. A wide range of
subsurface heterogeneous scenarios and B-scans are generated to evaluate the
inversion performance. The test results in the numerical experiment and the
real measurement show that the proposed network reconstructs the
permittivities, shapes, sizes, and locations of subsurface objects with high
accuracy. The comparison with existing methods demonstrates the superiority of
the proposed methodology for the inversion under heterogeneous soil conditions