Quantitative Integration of Multiple Near-Surface Geophysical Techniques for Improved Subsurface Imaging and Reducing Uncertainty in Discrete Anomaly Detection

Currently there is no systematic quantitative methodology in place for the integration of two or more coincident data sets collected using near-surface geophysical techniques. As the need for this type of methodology increases—particularly in the fields of archaeological prospecting, UXO detection, landmine detection, environmental site characterization/remediation monitoring, and forensics—a detailed and refined approach is necessary. The objective of this dissertation is to investigate quantitative techniques for integrating multi-tool near-surface geophysical data to improve subsurface imaging and reduce uncertainty in discrete anomaly detection. This objective is fulfilled by: (1) correlating multi-tool geophysical data with existing well-characterized “targets”; (2) developing methods for quantitatively merging different geophysical data sets; (3) implementing statistical tools within Statistical Analysis System (SAS) to evaluate the multiple integration methodologies; and (4) testing these new methods at several well-characterized sites with varied targets (i.e., case studies). Three geophysical techniques utilized in this research are: ground penetrating radar (GPR), electromagnetic (ground conductivity) methods (EM), and magnetic gradiometry. Computer simulations are developed to generate synthetic data with expected parameters such as heterogeneity of the subsurface, type of target, and spatial sampling. The synthetic data sets are integrated using the same methodologies employed on the case-study sites to (a) further develop the necessary quantitative assessment scheme, and (b) determine if these merged data sets do in fact yield improved results. A controlled setting within The University of Tennessee Geophysical Research Station permits the data (and associated anomalous bodies) to be spatially correlated with the locations of known targets. Error analysis is then conducted to guide any modifications to the data integration methodologies before transitioning to study sites of unknown subsurface features. Statistical analysis utilizing SAS is conducted to quantitatively evaluate the effectiveness of the data integration methodologies and determine if there are significant improvements in subsurface imaging, thus resulting in a reduction in the uncertainty of discrete anomaly detection.