Using physics-based modeler outputs to train probabilistic neural networks for unexploded ordnance (UXO) classification in magnetometry surveys

A Feasibility Study on Using Physics-Based Modeler Outputs to Train Probabilistic Neural Networks for UXO Classification

Classification of Unexploded Ordnance

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Genetic Programming for Discrimination of Buried Unexploded Ordnance (UXO)

According to the Department of Defense, over 10 million acres of land in the US need to be cleared of buried unexploded ordnance (UXO). Worldwide, UXO injures thousands each year. Cleanup costs are prohibitively expensive due to the difficulties in discriminating buried UXO from other inert non-UXO objects. Government agencies are actively searching for improved sensor methodologies to detect a...

Experiences with unexploded ordnance discrimination using magnetometry at a live-site in Montana

Advanced discrimination methods and careful optimization of operational procedures are critical for efficient remediation of unexploded ordnance (UXO) contaminated sites. In this paper, we report on our experiences with a 200 acre magnetic survey that was collected and processed under production survey conditions at Chevallier Ranch, Montana. All anomalies with fitted moments above 0.05 Am were...

Measurements and Modeling of Acoustic Scattering from Unexploded Ordnance (UXO) in Shallow Water

Introduction: The gradual development of coastal areas throughout the world in proximity to military training ranges and areas of past conflict has created a need to locate underwater unexploded ordnance (UXO). These ordnance, which may have veered off course and failed to detonate when they were initially released, may still pose a risk of explosion if they are subject to handling. This compli...