Practical Discrimination Strategies for Application to Live Sites

摘要

This project addressed one of the Department of Defense's (DoD) most pressing environmental problems -- the efficient and reliable identification of unexploded ordnance (UXO) without the need to excavate large numbers of non- ordnance items. Electromagnetic (EM) sensors have been shown to be a very promising technology for detecting UXO, but they also tend to detect many other nonhazardous metallic items. Current cleanup practice is to excavate all anomalies with peak amplitude above a predefined threshold. Such techniques are inefficient and costly, with at times over 100 nonhazardous items excavated for each UXO. Much research over the past few years has been focused on the discrimination problem whereby features from physics-based model-fits to anomalies are used to determine the likelihood that the buried item is a UXO. Statistical and rule-based classification techniques, when calibrated with good training data, have been shown at numerous test-trial sites to be very effective at discrimination. However, guidelines and standard operating procedures for their application to live sites have yet to be established. The principal objectives of the work conducted here were to develop a practical strategy for discrimination of UXO that can be reliably applied to real sites along with the protocols and tools to test performance. Three different demonstrations were conducted under this project. The first demonstration of the methodology was conducted at the Former Lowry Bombing and Gunnery Range (FLBGR) in Colorado during the 2006 field season. The focus of the FLBGR demonstration was on verification of the single inversion process used to extract physics-based parameters from magnetic and electromagnetic induction (EMI) anomalies, and on the statistical classification algorithms used to make discrimination decisions from those parameters. Two sites were visited at FLBGR.