Quantitative Integration of Multiple Near‐Surface Geophysical Techniques for Reduced Uncertainty in Discrete Anomaly Detection

Abstract

Currently there is no accepted quantitative methodology in place for the integration of two or more data sets collected using near‐surface geophysical techniques. As the need for this type of methodology increases—particularly in the fields of archaeological prospecting, environmental sciences, and forensics—a detailed and refined approach is necessary. The objectives of this research are to investigate quantitative techniques for integrating multi‐tool near‐surface geophysical data to improve subsurface imaging and reduce uncertainty in discrete anomaly detection. These objectives are 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 SAS software to evaluate the multiple integration methodologies; and (4) testing these new methods at several different sites with varied targets (i.e., case studies). Three geophysical techniques utilized in this research are: ground penetrating radar, electromagnetic (ground conductivity) methods, and magnetic gradiometry. Computer models are developed to generate synthetic data with expected parameters such as heterogeneity of the subsurface, type of target, spatial sampling, etc. 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 Agricultural Research Station permits spatial correlation of the data (and associated anomalous bodies) 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. Principal component analysis and cluster analysis are conducted to quantitatively evaluate the effectiveness of the data integration methodologies and determine if there are significant improvements in subsurface imaging.