Bibliographic Details
| Title: |
A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects |
| Authors: |
Dai, Qiqi, Lee, Yee Hui, Sun, Hai-Han, Qian, Jiwei, Ow, Genevieve, Yusof, Mohamed Lokman Mohd, Yucel, Abdulkadir C. |
| Publication Year: |
2022 |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing |
| More Details: |
The forward full-wave modeling of ground-penetrating radar (GPR) facilitates the understanding and interpretation of GPR data. Traditional forward solvers require excessive computational resources, especially when their repetitive executions are needed in signal processing and/or machine learning algorithms for GPR data inversion. To alleviate the computational burden, a deep learning-based 2D GPR forward solver is proposed to predict the GPR B-scans of subsurface objects buried in the heterogeneous soil. The proposed solver is constructed as a bimodal encoder-decoder neural network. Two encoders followed by an adaptive feature fusion module are designed to extract informative features from the subsurface permittivity and conductivity maps. The decoder subsequently constructs the B-scans from the fused feature representations. To enhance the network's generalization capability, transfer learning is employed to fine-tune the network for new scenarios vastly different from those in training set. Numerical results show that the proposed solver achieves a mean relative error of 1.28%. For predicting the B-scan of one subsurface object, the proposed solver requires 12 milliseconds, which is 22,500x less than the time required by a classical physics-based solver. |
| Document Type: |
Working Paper |
| DOI: |
10.1109/LGRS.2022.3192003 |
| Access URL: |
http://arxiv.org/abs/2207.06527 |
| Accession Number: |
edsarx.2207.06527 |
| Database: |
arXiv |
