Unexploded ordnance (UXO) detection and remediation is a high priority tri-service requirement. As the Defense Science Board wrote in 2003: “Today’s UXO cleanup problem is massive in scale with some 10 million acres of land involved. Estimated cleanup costs are uncertain but are clearly tens of billions of dollars. This cost is driven by the digging of holes in which no UXOs are present. The instruments used to detect UXOs (generally located underground) produce many false alarms, - i.e., detections from scrap metal or other foreign or natural objects -, for every detection of a real unexploded munition found.”

There is general agreement that one solution to the false alarm problem involves the use of electromagnetic induction (EMI) sensors that can, in principle, allow the extraction of target shape parameters in addition to the size and depth estimates available from magnetometer measurements. Other researchers have fielded systems with either time-domain or frequency-domain EMI sensors with the goal of extracting reliable target shape parameters and, thus, improving the discrimination capability of the surveys. In practice, the discrimination ability of these sensors has been limited by signal-to-noise limitations. Part of this noise results from sensor design, but a large fraction arises from causes external to the sensor such as location uncertainty, motion-induced noise, ground interaction, and external noise sources.

The objective of this project was to isolate and measure quantitatively the individual components of the noise budget for an EMI survey. These components include inherent sensor noise, motion-induced noise, external noise, ground interactions, and location uncertainties.

Technology Description

This project consisted of a long-running series of field measurements made under carefully controlled conditions at the Army Research Laboratory Blossom Point Facility, Maryland. The magnitudes and effects of several types of background and noise sources on the data collected with several commonly-used EMI sensors were measured. These noise sources included ground response/geology, inherent sensor noise, external noise sources, motion-induced noise, and position uncertainty. By use of Monte Carlo simulations of the fitting process for a canonical object using the collected data, the impact of each noise source on the final fitted parameters result was determined and compared to a similar analysis done for the 4.2-in mortar.

Demonstration Results

The Blossom Point site was found to be a relatively benign site for EMI sensor measurements and provided a good benchmark for a site where it was feasible to conduct a survey-mode EMI survey. The project team developed two recommendations as to a set of EMI sensors and a demonstration protocol to be used for future demonstrations. The first strategy involves a set of measurements and analysis based on the lessons learned from this project to be conducted on a new site over the course of a week. The results of these measurements would provide information to the site manager as to what sensors could be used and what information could be extracted for the site. The second strategy involves the planning tool, the EM61 MkII Simulation Tool, which allows a site manager to visualize the impact of various noise sources a priori to collecting site-specific data for planning purposes.

From these results, one can begin to understand real world EMI survey noise sources both individually and in combination, and start to quantify these effects in terms of survey configuration parameters which are under site manager and data collection operator control, such as lane spacing and survey mode.

Implementation Issues

End users of the information derived from this study are the site managers and regulators who oversee the nation’s Formerly Used Defense Sites, the contractors who routinely conduct EMI surveys for the purposes of site investigation and clearance, and the algorithm developers working to improve discrimination techniques. The utility of the results to the UXO detection and discrimination community will depend on the extent to which they are disseminated.

  • Electromagnetic Induction (EMI) ,

  • Sensors