The theme of this dissertation is to advance safety hazard mitigation by detecting and characterizing hidden targets of concern. Ground-penetrating radar (GPR) is used to detect and characterize hidden targets that pose safety hazards at Earth's surface, within shallow soil, and within rock. The resulting images detect unexploded ordnance (UXO) and detect fractures that pose collapse hazards in a mine.
Detecting and characterizing fractures and voids within rock prior to excavation can enable mitigation of mine collapse hazards. GPR data were acquired on the wall of a pillar in an underground mine. Strong radar reflections in the field data correlate with fractures and a cave exposed on the pillar walls. Pillar wall roughness was included in migration, a wavefield imaging algorithm, to quantitatively locate fractures and voids and map their spatial relationships within the rock. Quantifying the radar reflection amplitudes enabled mapping the distance between fracture walls.
Detecting and characterizing UXO and landmines from a safe distance can enable de-mining. Migration was used to improve GPR imaging for unmanned aerial vehicle (UAV) data acquisitions. Existing algorithms were adapted for UAV flight irregularities and surface topography, and a new algorithm was developed that does not depend on the unknown soil wavespeed. Errors associated with wavespeed and raypath assumptions were quantified. The algorithms were tested with real and synthetic datasets. The improved and new algorithms are more successful than previous algorithms.
To detect linear targets at all orientations, fully polarized GPR data are needed. Polarity combinations were investigated to optimize the detection of surface and subsurface small targets and linear targets. Scattering caused by topographic roughness is the primary shallow subsurface noise. For subsurface targets, detection is optimized by migration plus a polarity combination that captures all scattered energy. Strong reflection and scattering from the air-ground boundary can hide surface targets. Detection is optimized by removing the strong isotropic surface scattering, imaging targets by their anisotropic scattering. / Doctor of Philosophy / The theme of this dissertation is to advance safety hazard mitigation by detecting and characterizing hidden targets of concern. Ground-penetrating radar (GPR) is used to detect and characterize hidden targets that pose safety hazards at Earth's surface, within shallow soil, and within rock. The resulting images detect unexploded ordnance (UXO)/landmines and detect fractures that pose collapse hazards in a mine.
Detecting and characterizing fractures and voids within rock prior to mining can enable mitigation of mine collapse hazards. GPR data were acquired on the wall of a pillar in an underground mine. Strong radar reflections in the field data correlate with fractures and a cave exposed on the pillar walls. Pillar wall roughness was included in migration, a wavefield imaging algorithm, to quantitatively locate fractures and voids and map their spatial relationships within the rock. Quantifying the radar reflection amplitudes enabled mapping the distance between fracture walls.
Detecting and characterizing UXO, landmines from a safe distance can enable de-mining. Migration was used to improve GPR imaging for an unmanned aerial vehicle (drone) data acquisition. Existing algorithms were adapted for drone flight irregularities and surface topography, and a new algorithm was developed that does not depend on the unknown soil properties. Errors associated with the algorithms' assumptions were quantified. The algorithms were tested with real and computer-generated datasets. The improved and new algorithms are more successful than previous algorithms.
To detect all targets regardless of their orientation, GPR data need to be acquired with antenna pointing in multiple directions (different polarities). Polarity combinations were investigated to optimize the detection of surface and subsurface small targets and linear targets. Scattering caused by topographic roughness is the primary shallow subsurface noise. For subsurface targets, detection is optimized by migration plus a polarity combination that captures all scattered energy. Strong radar reflection from the air-ground boundary can hide surface targets. Detection is optimized by removing the strong ground surface from the data, and imaging targets by differences in their radar scattering.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/111433 |
| Date | 02 August 2022 |
| Creators | Abbasi Baghbadorani, Amin |
| Contributors | Geosciences, Hole, John Andrew, Holbrook, Steven, Libbey, Bradley, Ripepi, Nino S., Martin, Eileen R. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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