The objective of this project was to demonstrate that a multi-sensor active electromagnetic (AEM) system can be built to detect and extract essential information about a metallic object in the ground so as to discriminate unexploded ordnance (UXO)-like bodies from non-UXO scrap. The demonstration was conducted as part of the ESTCP UXO Discrimination Study, which was designed to test and validate UXO detection and discrimination capabilities of currently available and emerging technologies on real sites under operational conditions. In addition, the ESTCP Office and their demonstrators investigated, in cooperation with regulators and program managers, how UXO discrimination technologies can be implemented in cleanup operations.

Technology Description

ESTCP has supported Lawrence Berkeley National Laboratory (LBNL) in the development of the Berkeley UXO Discriminator (BUD) that not only detects an object itself but also quantitatively determines its size, shape, and orientation. BUD performs target characterization from a single position of the sensor platform above a target. BUD was designed to detect UXO in the 20 mm to 155 mm size range for depths between 0 and 1.5 m, and to characterize them in a depth range from 0 to 1.1 m. The system incorporates three orthogonal transmitters and eight pairs of differenced receivers. The transmitter-receiver assembly, together with the acquisition box and the battery power and global positioning system (GPS) receiver, is mounted on a small cart to assure system mobility. System positioning was provided by the state-of-the-art Real Time Kinematic (RTK) GPS receiver. The survey data acquired by BUD was processed by software developed by LBNL.

Demonstration Results

The demonstration objective was to determine the discrimination capabilities, cost, and reliability of BUD. LBNL performed a detection and discrimination survey of the SE1 area (approximately 5 acres) of the Camp Sibert Formerly Used Defense Site (FUDS) in Alabama. The SE1 area discrimination set contained 266 objects. The ground truth indicated that 56 of them were single 4.2 in mortars. The team produced two priority dig lists. The first one with the ‘stop digging’ point when any object past this mark had very little probability of being 4.2 in mortar, and the second one with the ‘stop digging’ point at 90% probability of any object being 4.2 in mortar. In the first priority dig list, the team indicated that 75 objects had to be dug, while 191 objects could be left in the ground. Scoring results from the Institute of Defense Analyses (IDA) showed that all 56 4.2 in mortars were correctly identified, and 19 times scrap was identified as 4.2 in mortar (19 false positives). The second priority list classified 63 objects as ‘need to dig’ and 203 objects as scrap. Scoring results from IDA showed that in this case all 56 4.2 in mortars were correctly identified, and false positives were reduced to 7. In addition, BUD was used in a cued mode to interrogate 200 selected anomalies within Site 18 (SE1, SE2, and SW areas). The data were collected in accordance with the overall study demonstration plan including system characterization with the emplaced calibration items and targets in the Geophysical Prove Out (GPO).

Implementation Issues

BUD is the first AEM system that can not only detect UXO but also discriminate it from non-UXO/scrap and give its characteristics (location, size, polarizability). Moreover, the object can be characterized from a single position of the sensor platform above the object. BUD was designed to detect UXO in the 20 mm to 155 mm size range buried anywhere from the surface down to 1.5 m depth. Any objects buried at a depth more than 1.5 m will have a low probability of detection. BUD was designed to characterize UXO in the same size range in depths between 0 and 1.1 m. Any objects buried at a depth more than 1.1 m will have a low probability of discrimination. With the existing algorithms in the system computer at the time, it was not possible to recover the principal polarizabilities of large objects close to the system. Detection of large shallow objects was assured, but at that time discrimination was not. Post processing of the field data was required for shape discrimination of large shallow targets.