探地雷达
泄漏
雷达
时域有限差分法
反射(计算机编程)
地质学
遥感
岩土工程
环境科学
计算机科学
光学
电信
物理
环境工程
程序设计语言
作者
Mohamed Gamal,Qingyun Di,Jinhai Zhang,Changmin Fu,Shereen M. Ebrahim,Amr Abd El‐Raouf
出处
期刊:Remote Sensing
[Multidisciplinary Digital Publishing Institute]
日期:2023-10-12
卷期号:15 (20): 4924-4924
被引量:10
摘要
Detecting and mapping subsurface utilities in urban areas is crucial for identifying defects or damages in drinking and sewage pipes that can cause leaks. These leaks make it difficult to accurately characterize the pipes due to changes in their reflective properties. This study focused on detecting leaks originating from underground pipes and distinguishing between these various types of pipes. It also aimed to create a visual fingerprint model that displays the reflection characteristics of these pipes during different leak conditions, enabling efficient maintenance and handling procedures on the pipes. To achieve this, a finite-difference time-domain (FDTD) method was used to simulate two types of pipe materials with and without leak areas to construct different scenarios. Additionally, a ground-penetrating radar (GPR) field survey was conducted using a 600 MHz antenna in a part of the El Hammam area on Egypt’s northwest coast. The simulated images produced with numerical modeling were compared with the radar profiles obtained using GPR at particular locations. The numerical simulations and radar profiles demonstrated the noticeable influence of water leaks from the different pipes, wherein the reflection of saturated soil waves was interrupted due to the presence of saturated soil. Envelope and migration techniques were employed in a new application to accurately distinguish between different pipe types, specifically focusing on leak areas. The strong correlation between the real radar profile and the specific signal of a water pipe leak in the simulated models suggests that GPR is a reliable non-destructive geophysical method for detecting water pipe leaks and distinguishing between the different pipe materials in various field conditions. The simulated models, which serve as image-matching fingerprints to identify and map water pipe leaks, help us to comprehend reality better.
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