Attribute Analysis Of Electromagnetic Waves In Different Mediums Using Ground Penetrating Radar
Ground-penetrating radar (GPR) has been widely used to detect shallow subsurface geological features and man-made buried objects such as utilities and archaeological structures. It is critical to interpret GPR data thoroughly to provide reliable interpretations of near-surface structures, as certain...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://eprints.usm.my/59696/1/24%20Pages%20from%20IFFAH%20ZALIKHA%20BINTI%20ROSLAN%20-%20TESIS.pdf http://eprints.usm.my/59696/ |
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| Summary: | Ground-penetrating radar (GPR) has been widely used to detect shallow subsurface geological features and man-made buried objects such as utilities and archaeological structures. It is critical to interpret GPR data thoroughly to provide reliable interpretations of near-surface structures, as certain types of buried objects frequently generate similar GPR reflection signals. Thus, this research aims to identify the factors affecting electromagnetic wave (EM) propagation in the subsurface using GPR and to analyze the EM wave attribute in several media and survey geometry as well as to produce a basic guideline for GPR practitioners in interpreting the GPR data. The data acquisition in this study is divided into two parts; 1) experimental model and 2) field study. In the experimental model, radar-wave signals were investigated with varying object geometry. Results indicated that when the upper layer/medium had a lower relative permittivity than the layer/medium underneath, the polarity is normal. By contrast, the reflection polarity is reversed when it is reflected from a material with a lower relative permittivity than the material above it. The greater the relative permittivity contrast, the greater the reflection coefficient, and hence the easier it is to delineate layers and discover subsurface features. The reflection coefficient of reflectivity contrast quality is divided into weak (<0.3), good (0.3-0.5) and excellent (>0.5). |
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