Publication Date

5-2014

Date of Final Oral Examination (Defense)

4-15-2014

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Geophysics

Department

Geosciences

Supervisory Committee Chair

John Bradford, Ph.D.

Supervisory Committee Member

John C. Freemuth, Ph.D.

Supervisory Committee Member

Hans-Peter Marshall, Ph.D.

Abstract

Ground penetrating radar (GPR) and seismic reflection methods are useful geophysical tools for near-surface characterization. Analysis of radar or seismic reflection data can combine velocity analysis with common physical transformations to provide subsurface physical properties such as subsurface porosity, density, and contaminant locations. However, reliable quantitative characterization of thin subsurface layers may be impossible using standard reflection data processing techniques, e.g. velocity analysis, if the layer thickness is below the conventional resolution limits of the data. The limiting layer thickness for layer resolution may be up to ½ or even ¾ of the dominant wavelength (λ) of the signal in the medium of interest. This limitation often depends on data noise levels and source characteristics. In many environmental problems, target layers may be below this layer thickness and accurate determination of layer properties becomes problematic. In order to reliably quantify thin-layer parameters in these cases, geophysical practitioners require additional tools such as attribute analyses and inversion methodologies. Full-waveform inversions may be able to quantify layer parameters even in the case of thin (< ½λ) and ultra-thin (< ⅛λ) layers by inverting directly for thin-layer properties. Therefore, I provide a targeted full-waveform inversion algorithm to quantify thin- and ultra-thin layer parameters for multiple relevant environmental problems including oil in and under sea ice and basal conditions of glaciers. I demonstrate the efficacy of this approach on model and field data collected using radar and seismic reflection methods. These methods depend on surface records of reflection information from subsurface interfaces and may fail if reflections are obscured or attenuated in the subsurface. Therefore, I demonstrate that a new dual-polarization system can mitigate the effects of the overburden anisotropy and conductivity attenuation on radar data collected in Arctic conditions. Combining my full-waveform inversion algorithm with improved sea ice radar data collection may enhance reliable quantification of spilled oil in the event of an accidental release in Arctic environments.

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