Urban Subsurface Mapping via Deep Learning Based GPR Data Inversion
Urban Subsurface Mapping via Deep Learning Based GPR Data Inversion
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DOI：
10.1109/wsc57314.2022.10015357
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Mengjun Wang;Da Hu;Shuai Li;Jiannan Cai
中科院分区：
文献类型：
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作者：
Mengjun Wang;Da Hu;Shuai Li;Jiannan Cai
城市地下空间的准确测绘对于管理城市地下基础设施和预防开挖事故至关重要。探地雷达（GPR）是一种非破坏性的检测方法，已被广泛用于定位地下设施。然而，现有的方法不能检索详细的地下公用设施信息（例如，材料和尺寸）。本研究的目的是通过处理探地雷达扫描数据，自动检测和表征地下设施的位置，尺寸和材料。为了实现这一目标，开发了一种基于深度学习的反演GPR数据的方法，以直接从相应的GPR扫描中重建地下结构横截面剖面的介电常数图。大量的合成GPR扫描与地面真实介电常数标签生成训练反演网络。实验结果表明，该方法的平均绝对误差为0.53，结构相似性指数为0.91，R ^{2}$为0.96。
Accurate mapping of urban subsurface is essential for managing urban underground infrastructure and preventing excavation accidents. Ground-penetrating radar (GPR) is a non-destructive test method that has been used extensively to locate underground utilities. However, existing approaches are not able to retrieve detailed underground utility information (e.g., material and dimensions) from GPR scans. This research aims to automatically detect and characterize buried utilities with location, dimension, and material by processing GPR scans. To achieve this aim, a method for inverting GPR data based on deep learning has been developed to directly reconstruct the permittivity maps of cross-sectional profiles of subsurface structure from the corresponding GPR scans. A large number of synthetic GPR scans with ground-truth permittivity labels were generated to train the inversion network. The experiment results indicated that the proposed method achieved a Mean Absolute Error of 0.53, a Structural Similarity Index Measure of 0.91, and an $R^{2}$ of 0.96.