# GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures
## 6 Conclusions  
In this work, we introduce a GAN-based end-to-end inversion framework to translate crosshole GPR images to the corresponding defect reconstruction images automatically. This method uses, as building blocks, a low-resolution GAN, to extract global features from crosshole GPR images and infer a preliminary defect reconstruction image with low resolution, and a high-resolution GAN, to refine and improve the resolution of the inverted defect reconstruction image. We built an FDTD dataset based on the defect detection of diaphragm walls to train and test the proposed method. The GAN-based framework can be trained to the ideal state in which the discriminators fail to distinguish the fake images output by the generators from the real images in the training dataset. By evaluating the inversion performance of the network on a test dataset, the accuracy, SSIM, and RAscore of the optimized inversion network reached 91.36%, 0.93, and 91.77, respectively. Applications in synthetic and real-world crosshole GPR data inversion also demonstrate the applicability of the proposed method.  
Our work makes contributions to the field of crosshole GPR data inversion by introducing a novel end-to-end inversion framework based on GANs. Our framework can handle complex scenarios, such as non-linearity and ill-posedness in crosshole GPR data inversion, and produce a fast, accurate, and robust method that can automatically translate crosshole GPR images to defect reconstruction images without requiring any prior knowledge or human intervention. Our framework also has the potential to be extended to other geophysical inversion problems.  
We also note that when there are multiple targets, the inversion accuracy of our network declines evidently, especially when the targets are placed horizontally or vertically. In our future research, we will improve our method to accommodate multiple-target situations. Additionally, different target categories (targets with different dielectric properties) will be considered more comprehensively in our subsequent work.  
Conceptualization, methodology, writing--original draft preparation, D.Z.; data curation, software, Z.W.; writing (review and editing), funding acquisition, H.Q.; formal analysis, validation, T.G.; supervision, S.P. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (grant No. 41904095), Special Funds for Central Government Guidance to Local Governments for Science and Technology Development in Shenzhen (grant No. 2021 Szvup20), Central Guidance on Local Science and Technology Development Fund of Liaoning Province (grant No. 2023H6/100100054), and Guided Independent Research Fund of State Key Laboratory of Coastal and Offshore Engineering (grant No. SL2203). The source code and data used in this research can be publicly accessed at [[https://github.com/ZhangDonghao1907/GAN-based-inversion-network-for-crosshole-GPR](https://github.com/ZhangDonghao1907/GAN-based-inversion-network-for-crosshole-GPR)]([https://github.com/ZhangDonghao1907/GAN-based-inversion-network-for-crosshole-GPR](https://github.com/ZhangDonghao1907/GAN-based-inversion-network-for-crosshole-GPR)) (accessed on 2 May 2023). The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.