Decision Support Tools for Munitions Response Performance Prediction and Risk Assessment
MR-2226
Objective
New technologies for detection and discrimination of buried unexploded ordnance (UXO) have the potential to significantly reduce the cost of munitions response projects. In particular, electromagnetic (EM) sensors developed specifically for this problem can reliably discriminate between ordnance and non-hazardous metallic clutter. The discrimination process involves fitting a physical model to observed sensor data and then using the parameters of this model to make inferences about the physical properties of a detected target.
ESTCP demonstration projects have shown that advanced discrimination with next generation EM sensors consistently outperforms commercial standard systems. Despite these successes, application of these technologies in production settings has been limited. To further the adoption of advanced sensors and processing by industry, decision support tools are required to help site managers understand:
- How to best deploy available technologies for a particular remediation problem.
- How to ensure with high confidence that all targets of interest are identified following remediation efforts.
While significant advances have been made in the acquisition and processing of geophysical data for discrimination of buried munitions, the success of any discrimination strategy strongly depends on the site characteristics, including range of munitions types and clutter, geological background, topography, and vegetation. The objective of this project is to develop and validate the components of a decision support system (DSS) that will help individual site-managers and teams design surveys and data processing strategies to achieve optimal discrimination performance at the lowest attainable cost for a given site.
Technical Approach
The technical approach will focus on the development and validation of the core components of a DSS for munitions response performance prediction. Specifically, the researchers will:
- Develop a statistical model that can predict discrimination performance and expected costs at a site. This model will consider site characteristics, sensor platform, survey parameters, and discrimination strategy.
- Develop a statistical model to assess the a posteriori probability that targets of interest (TOI) remain in the ground following remediation efforts.
The researchers will validate these statistical models using real data sets from past and ongoing ESTCP demonstration projects. An optional objective will be the integration of these components in a browser interface, which will form the user interface for the DSS.
Benefits
The decision support system will aid managers in designing a cost-effective remediation effort prior to deployment and in adjusting and optimizing survey design and data processing as more information becomes available. These tools will help the wider UXO community understand the potential benefits and limitations of advanced discrimination. (Anticipated Project Completion - 2014)
Points of Contact
Principal Investigator
Dr. Laurens Beran
Sky Research Inc.
Phone: 541-552-5149
Document Types
- Fact Sheet - Brief project summary with links to related documents and points of contact.
- Final Report - Comprehensive report for every completed SERDP and ESTCP project that contains all technical results.
- Cost & Performance Report - Overview of ESTCP demonstration activities, results, and conclusions, standardized to facilitate implementation decisions.
- Technical Report - Additional interim reports, laboratory reports, demonstration reports, and technology survey reports.
- Guidance - Instructional information on technical topics such as protocols and user’s guides.
- Workshop Report - Summary of workshop discussion and findings.
- Multimedia - On demand videos, animations, and webcasts highlighting featured initiatives or technologies.
- Model/Software - Computer programs and applications available for download.
- Database - Digitally organized collection of data available to search and access.