# 3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion

arXiv: https://arxiv.org/abs/2305.05425 · HTML: https://mlsanity.com/paper/2305.05425/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2023.

## Built on by (most cited first, 6 of 6)

- [Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems](https://mlsanity.com/paper/2507.16321/) (2025)
- [RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments](https://mlsanity.com/paper/2406.16907/) (2024)
- [Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems](https://mlsanity.com/paper/2409.01315/) (2024)
- [Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems](https://mlsanity.com/paper/2512.09333/) (2025)
- [Investigating the Capabilities of Deep Learning for Processing and Interpreting One-Shot Multi-offset GPR Data: A Numerical Case Study for Lunar and Martian Environments](https://mlsanity.com/paper/2410.14386/) (2024)
- [Lightweight framework for underground pipeline recognition and spatial localization based on multi-view 2D GPR images](https://mlsanity.com/paper/2512.20866/) (2025)

## Abstract (arXiv)

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.