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.

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.

Use of electromagnetic (EM) methods to discriminate frag and geology from unexploded ordnance (UXO) has shown steady improvement over time as demonstrated by test site results. Performance of off- the-shelf and customized EM sensors have increased both in terms of UXO probability of detection and false alarm rate. Parallel with the increase in capabilities of UXO detection and discrimination is the Increasing use of digital geophysical techniques on live sites. However, a significant gap exists in capabilities for UXO detection and discrimination between prove-out / test sites and actual UXO contaminated sites. In addition, the future deployment of more sophisticated sensors with discrimination capabilities is limited by the requirement for greater data fidelity for wide application on most sites. One issue of importance for improving data quality is the need for improved spatial representation of the geophysical signature of subsurface UXO. Presently, survey data are inadequately spatially quantified with simple XY sensor locations and an assumed constant Z elevation. To accurately represent the geophysical signature of subsurface UXO, more complete and accurate inion%ation is required, including sensor elevation inion%ation, orientation data (yaw, pitch and roll), and sensor velocity and acceleration data. Therefore, Strategic Envirom%ental Research and Development Progm-m (SERDP) Project UX- 1310 "Sensor Orientation Effects on UXO Geophysical Target Discrimination" was designed to research how the response of geophysical sensors is affected by variations in sensor orientation and motion, to develop an approach to measure these effects in the field, and to develop modeling and analysis techniques to mitigate the effects of sensor orientation and motion.

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.

The chapters in this volume were presented at the July–August 2008 NATO Advanced Study Institute on Unexploded Ordnance Detection and Mitigation. The conference was held at the beautiful Il Ciocco resort near Lucca, in the glorious Tuscany region of northern Italy. For the ninth time we gathered at this idyllic spot to explore and extend the reciprocity between mathematics and engineering. The dynamic interaction between world-renowned scientists from the usually disparate communities of pure mathematicians and applied scientists which occurred at our eight previous ASI’s continued at this meeting. The detection and neutralization of unexploded ordnance (UXO) has been of major concern for very many decades; at least since the First World war. UXO continues to be the subject of intensive research in many ?elds of science, incl- ing mathematics, signal processing (mainly radar and sonar) and chemistry. While today’s headlines emphasize the mayhem resulting from the placement of imp- vised explosive devices (IEDs), humanitarian landmine clearing continues to draw signi?cant global attention as well. In many countries of the world, landmines threaten the population and hinder reconstruction and fast, ef?cient utilization of large areas of the mined land in the aftermath of military con?icts.

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.