QUANTITATIVE INTEGRATION OF MULTIPLE NEAR-SURFACE GEOPHYSICAL TECHNIQUES FOR IMPROVED SUBSURFACE IMAGING AND REDUCED UNCERTAINTY IN DISCRETE ANOMALY DETECTION

Currently there is no quantitative methodology in place for the integration of two or more geophysical data sets collected using near-surface geophysical techniques. The primary objectives of this research are to investigate quantitative methodologies for integrating multi-tool surface geophysical data to improve subsurface imaging and reduce uncertainty in discrete anomaly detection. These objectives will be fulfilled by: (1) correlating multi-tool geophysical data with existing well-characterized “targets”; (2) developing methods for quantitatively merging different geophysical data sets; and (3) testing these new methods at several different sites with varied targets (i.e., case studies). Three geophysical techniques primarily utilized in this research are: ground penetrating radar, electromagnetic (ground conductivity) methods, and magnetic gradiometry. The two study sites (Cherokee Farm, and the University of Tennessee Agricultural Extension Plot 4B) located within alluvial sediments along the Tennessee River in eastern Tennessee, USA, serve as case studies to verify methodologies in a terrestrial environment. Computer models have been developed that generate synthetic data with expected parameters such as heterogeneity of the subsurface, type of target, geophysical technique utilized, spatial sampling, etc. The synthetic data sets are then integrated together using the same methodologies as employed with data from 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. Statistical tools within SAS are incorporated to evaluate the multiple integration methodologies.