Visual sample plan and prior information: what do we need to know to find UXO?

dc.contributor.advisorWriting Project Advisor: Megan Higgsen
dc.contributor.authorFlagg, Kenneth A.en
dc.date.accessioned2019-02-05T15:17:49Z
dc.date.available2019-02-05T15:17:49Z
dc.date.issued2016en
dc.description.abstractMilitary training and weapons testing activities leave behind munitions debris, including both inert fragments and explosives that failed to detonate. The latter are known as unexploded ordnance (UXO). It is important to find and dispose of UXO items that are located where people could come into contact with them and cause them to detonate. Typically there exists uncertainty about the locations of UXO items and the sizes of UXO- containing regions at a site, so statistical analyses are used to support decisions made while planning a site remediation project. The Visual Sample Plan software (VSP), published by the Pacific Northwest National Laboratory, is widely used by United States military contractors to guide sampling plan design and to identify regions that are likely to contain UXO. VSP has many features used for a variety of situations in UXO cleanup and other types of projects. This study focuses on the sampling plan and geostatistical mapping features used to find target areas where UXO may be present. The software produces transect sampling plans based on prior information entered by the user. After the sample data are collected, VSP estimates spatial point density using circular search windows and then uses Kriging to produce a continuous map of point density across the site. I reviewed the software's documentation and examined its output files to provide insight about how VSP does its computations, allowing the software's analyses to be closely reproduced and therefore better understood by users. I perform a simulation study to investigate the performance of VSP for identifying target areas at terrestrial munitions testing sites. I simulate three hypothetical sites, differing in the size and number of munitions use areas, and in the complexity of the background noise. Many realizations of each site are analyzed using methods similar to those employed by VSP to delineate regions of concentrated munitions use. I use the simulations to conduct two experiments, the first of which explores the sensitivity of the results to different search window sizes. I analyze two hundred realizations of the simplest site using the same sampling plan and five different window sizes. Based on the results, I select 90% of the minor axis of the target area of interest as the window diameter for the second experiment. The second experiment studies the effects of the prior information about the target area size and spatial point density of munitions items. For each site, I use four prior estimates of target area size and three estimates of point density to produce twelve sampling plans. One hundred realizations of each site are analyzed with each of the twelve sampling plans. I evaluate the analysis in terms of the detection rates of munitions items and target areas, the distances between undetected munitions items and identified areas, the total area identified, and other practical measures of the accuracy and efficiency of the cleanup effort. I conclude that the most accurate identification of target areas occurs when the sampling plan is based on the true size of the smallest target area present. The prior knowledge of the spatial point density has relatively little impact on the outcome.en
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/15195en
dc.language.isoenen
dc.publisherMontana State University - Bozeman, College of Letters & Scienceen
dc.rights.holderCopyright 2016 by Kenneth A. Flaggen
dc.subject.lcshBombsen
dc.subject.lcshHazardous waste sitesen
dc.subject.lcshStatisticsen
dc.subject.lcshComputer softwareen
dc.titleVisual sample plan and prior information: what do we need to know to find UXO?en
dc.typeProfessional Paperen
mus.data.thumbpage19en
thesis.degree.committeemembersWriting Project Coordinator: Mark Greenwood.en
thesis.degree.departmentMathematical Sciences.en
thesis.degree.genreProfessional Paperen
thesis.degree.nameMSen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage96en

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