GPR Method for the Detection and Characterization of Fractures and Karst Features: Polarimetry, Attribute Extraction, Inverse Modeling and Data Mining Techniques

dc.contributorEverett, Mark E.
dc.creatorSassen, Douglas Spencer
dc.date.accessioned2011-02-22T22:23:39Z
dc.date.accessioned2011-02-22T23:44:50Z
dc.date.accessioned2017-04-07T19:57:50Z
dc.date.available2011-02-22T22:23:39Z
dc.date.available2011-02-22T23:44:50Z
dc.date.available2017-04-07T19:57:50Z
dc.date.created2009-12
dc.date.issued2011-02-22
dc.description.abstractThe presence of fractures, joints and karst features within rock strongly influence the hydraulic and mechanical behavior of a rock mass, and there is a strong desire to characterize these features in a noninvasive manner, such as by using ground penetrating radar (GPR). These features can alter the incident waveform and polarization of the GPR signal depending on the aperture, fill and orientation of the features. The GPR methods developed here focus on changes in waveform, polarization or texture that can improve the detection and discrimination of these features within rock bodies. These new methods are utilized to better understand the interaction of an invasive shrub, Juniperus ashei, with subsurface flow conduits at an ecohydrologic experimentation plot situated on the limestone of the Edwards Aquifer, central Texas. First, a coherency algorithm is developed for polarimetric GPR that uses the largest eigenvalue of a scattering matrix in the calculation of coherence. This coherency is sensitive to waveshape and unbiased by the polarization of the GPR antennas, and it shows improvement over scalar coherency in detection of possible conduits in the plot data. Second, a method is described for full-waveform inversion of transmission data to quantitatively determine fracture aperture and electromagnetic properties of the fill, based on a thin-layer model. This inversion method is validated on synthetic data, and the results from field data at the experimentation plot show consistency with the reflection data. Finally, growing hierarchical self-organizing maps (GHSOM) are applied to the GPR data to discover new patterns indicative of subsurface features, without representative examples. The GHSOMs are able to distinguish patterns indicating soil filled cavities within the limestone. Using these methods, locations of soil filled cavities and the dominant flow conduits were indentified. This information helps to reconcile previous hydrologic experiments conducted at the site. Additionally, the GPR and hydrologic experiments suggests that Juniperus ashei significantly impacts infiltration by redirecting flow towards its roots occupying conduits and soil bodies within the rock. This research demonstrates that GPR provides a noninvasive tool that can improve future subsurface experimentation.
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7307
dc.language.isoen_US
dc.subjectGround Penetrating Radar
dc.subjectGPR
dc.subjectHydrogeology
dc.subjectecohydrology
dc.subjectunsupervised learning
dc.subjectinverse modeling
dc.subjectimage enhancement
dc.subjectdata mining
dc.subjectcoherency
dc.subjectfractures
dc.subjectkarst
dc.titleGPR Method for the Detection and Characterization of Fractures and Karst Features: Polarimetry, Attribute Extraction, Inverse Modeling and Data Mining Techniques
dc.typeBook
dc.typeThesis

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