Risk assessment for unexploded ordnance remediation

We develop and compare methods for assessing the risk that unexploded ordnance (UXO) have been missed following prioritized digging. A random compliance sampling approach has been suggested for UXO risk assessment, and we extend this approach to account for the bias in prioritized digging, thereby reducing the number of excavations required to test for outlying UXO. We then discuss and compare methods for identification of outliers to the distribution of UXO via generative models of the receiver operating characteristic (ROC). Next, we consider how seeded items emplaced for quality control can be used to increase confidence in the classification process, and we model this process by constraining the ROC model. Finally, we turn to the problem of identifying novel, or unique, UXO with prioritized validation digs. We propose a metric that combines features of the geophysical model estimated for each detected target to identify novel UXO. The metric requires no prior information about the UXO present at a site.