Advanced EMI models for survey data processing: Targets detection and classification

摘要

This paper describes procedures and approaches our team took to demonstrate the capability of advanced electromagnetic induction (EMI) forward and inverse models to perform subsurface metallic objects picking and classification at live-UXO sites from dynamic data sets. Over the past seven years, blind classification tests at live-UXO sites have revealed two main challenges: 1) consistent selection of targets for cued interrogation, (e.g., for the recent SWPG2 study, two independent performers that processed the same MetalMapper dynamic data picked different targets for cued interrogation); and 2) positioning of the cued sensor close enough to the actual cued target to accurately perform classification (particularly when multiple targets or magnetic soils are present). To overcome these problems, in this paper we introduced an innovative and robust approach for subsurface metallic targets picking and classification from dynamic data sets. This approach first inverts for target locations and polarizabilities from each dynamic data point, and then clusters the inverted locations and defines each cluster as a target/source. Finally, the method uses the extracted polarizabilities for classifying UXO from non-UXO items. The studies are done for the 2×2 TEMTADS dynamic data set collected at Camp Hale, CO. The targets picking and classification results are illustrated and validated against ground truth.