Unexploded ordnance (UXO) cleanup is the number one priority Army installation remediation restoration requirement. The problem is enormous in scope, with millions of acres and hundreds of sites potentially contaminated. Before the UXO can be recovered and destroyed, it must be located. UXO location requires surface geopbysical surveys. The geophysical anomalies caused by the UXO must be detected, discriminated from geophysical anomalies caused by other sources, and ideally identified or classified. Recent UXO technology demonstrations, live site demonstrations, and practical UXO surveys for site cleanup confirm that most UXO anomalies can be detected (with probabilities of detection of 90 percent or better), however there is little evidence of discrimination capability (i.e., the false alarm rates are high), and there is no identification capability. Approaches to simultaneously increase probability of detection and decrease false alarm rate and ultimately to give identification/classification capability involve rational multisensor data integration for discrimination and advanced development of new and emerging technology for enhanced discrimination and identification. The goal of multisensor data integration is to achieve true joint inversion of data to a best-fitting model using realistic physics-based models that replicate UXO geometries and physical properties of the UXO and surrounding geologic materials. Data management, analysis, and display procedures for multisensor data are investigated. A magnetic modeling capability is developed, validated, and documented that uses a prolate spheroid model of UXO. The electromagnetic modeling of UXO signatures is more problematic, and an intermediate quasi-empirical modeling capability (a simple analytical model modified to reflect measured signature observations) is explored.

Unexploded ordnance (UXO) cleanup is the number one priority Army installation remediation/restoration requirement The problem is enormous in scope, with millions of acres and hundreds of sites potentially contaminated. Before the UXO can be recovered and destroyed, it must be located. UXO location requires surface geophysical surveys. The geophysical anomalies caused by the UXO must be detected, discriminated from geophysical anomalies caused by other sources, and ideally identified or classified. Recent UXO technology demonstrations, live site demonstrations, and practical UXO surveys for site cleanup confirm that most UXO anomalies can be detected (with probabilities of detection of 90 percent or better), however there is little evidence of discrimination capability (i.e., the false alarm rates are high), and there is no identification capability. Approaches to simultaneously increase probability of detection and decrease false alarm rate and ultimately to give identification/classification capability involve rational multisensor data integration for discrimination and advanced development of new and emerging technology for enhanced discrimination and identification. The goal of multisensor data integration is to achieve true joint inversion of data to a best-fitting model using realistic physics-based models that replicate UXO geometries and physical properties of the UXO and surrounding geologic materials. Data management, analysis, and display procedures for multisensor data are investigated. The role of empirical, quasi-empirical, and analytical modeling for UXO geophysical signature prediction are reviewed and contrasted with approaches that require large signature databases (e.g., expert systems, neural nets, signature database comparison) for training or best-fit comparison. A magnetic modeling capability is developed, validated, and documented that uses a prolate spheroid model of UXO.

To fully characterize the inductive response of an isolated conductive object, such as buried unexploded ordinance, one needs to measure its response to stimulation by primary magnetic fields in three linearly independent (e.g., approximately orthogonal) directions. In one embodiment this is achieved by measuring the response to magnetic fields of three independent transmitters arranged to have magnetic fields that are linearly independent. According to the apparatus and methods employing the system of this invention, multiple transmitters and receivers of known relative position and orientation on a single platform are used. In a preferred embodiment, matched sets of receiver pairs connected in gradient mode are positioned adjacent to closely spaced pairs of transmitting coils, such that a minor displacement of one or both of the receiver coil pairs relative to the paired transmitting coils will not affect the detected secondary signals emitted by a buried metallic object.

The overall objective of this project is to develop reliable techniques for discriminating between buried UXO and clutter using multisensors electromagnetic induction sensor array data. The basic idea is to build on existing research which exploits differences in shape between ordnance and clutter to include the effects of other distinctive properties of ordnance items (fuze bodies, driving bands, fin assemblies, etc.). During the course of this project, we will clearly elucidate the underlying physical principles relating to the electromagnetic response of ordnance items, determine the fundamental physical limitations on ordnance/clutter discrimination using multisensors survey data, and devise effective multisensors processing schemes to discriminate between buried UXO and clutter using such data.

Unexploded ordnance (UXO) pose a persistent and expensive problem throughout the world; over 11 million acres are potentially contaminated in the U.S. alone. However, detection requires a very high degree of reliability, the false alarm rate is typically enormous, and cleanup costs are very high. This Tutorial Text addresses the unique challenges of UXO detection and the following topics: fundamental physics and phenomenology; new, successful modeling and analysis methods; the design, development, and testing of new instruments that provide expanded and superior data; innovative processing techniques; and highly successful discrimination performance in blind field tests at standardized sites. The book is written for lay scientists and engineers, as well as specialists in the field, requiring only some familiarity with basic vector calculus and matrix methods, common statistical concepts, and elementary physics.