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|Title:||Multisensor methods for buried unexploded ordnance detection, discrimination, and identification|
|Authors:||Wright State University.|
Geotechnical Laboratory (U.S.)
Strategic Environmental Research and Development Program (U.S.)
Butler, Dwain K.
Cespedes, Ernesto R.
Cox, Cary B.
Wolfe, Paul J.
|Publisher:||Environmental Laboratory (U.S.)|
Engineer Research and Development Center (U.S.)
Abstract: 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 multisensory data integration for discrimination and advanced development of new and emerging technology for enhanced discrimination and identification. The goal of multisensory 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 multisensory 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 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. Approaches using parameter space analysis techniques for multisensory data integration are emphasized, with examples from UXO test site and demonstration program datasets. A multichannel, multicomponent, time-domain electromagnetic system and a multifrequency, frequency-domain electromagnetic system are highlighted as emerging technologies with potential to advance capabilities for UXO discrimination and identification.
|Rights:||Approved for public release; distribution is unlimited.|
|Appears in Collections:||Technical Report|