Detection and classification of buried radioactive-metal objects using wideband EMI data

Gamma-ray spectroscopy is frequently used for the detection of radioactive materials. As an alternative, we explore the use of electromagnetic induction (EMI) data for detection and classification of radioactive-metal objects, i.e., depleted uranium (DU), in this study. To reduce false alarms, a pattern recognition approach based on a decision tree structure is proposed. In an initial experiment, the DU rounds were placed in rows at three different depths in a rectangular field and EMI measurements are taken. The DU objects placed up to depth 30 cm below surface were successfully detected and identified along with the depth information. The algorithm also outperformed traditional threshold detection based method in terms of discriminating objects at 30 cm depth.