Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163834
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
Mohamed Lokman Mohd Yusof
Yucel, Abdulkadir C.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Dai, Q., Lee, Y. H., Sun, H., Qian, J., Ow, G., Mohamed Lokman Mohd Yusof & Yucel, A. C. (2022). A deep learning-based GPR forward solver for predicting B-scans of subsurface objects. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. https://dx.doi.org/10.1109/LGRS.2022.3192003
Journal: IEEE Geoscience and Remote Sensing Letters
Abstract: 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.
URI: https://hdl.handle.net/10356/163834
ISSN: 1545-598X
DOI: 10.1109/LGRS.2022.3192003
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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