Results from recent ESTCP live site demonstrations have shown significant improvements, relative to only a few years ago, in the ability to accurately distinguish between hazardous unexploded ordnance (UXO) and non-hazardous shrapnel, range, and cultural debris. These improvements in classification performance have largely been achieved through advances in sensor design and signal processing methods developed in SERDP-sponsored research projects. In particular, geophysical surveys using instrumentation that maximize the amount of target information contained in the measured data are being combined with newly developed robust methods for extracting target parameters at the ESTCP live site demonstrations. Although classification results have been encouraging, these demonstrations have also shown that 100% recovery of all targets of interest (TOI) is not always possible without digging a significant number of non-TOI. In addition, there are still numerous instances where analysts have stopped digging before all TOI have been identified.
As the live site program has progressed to increasingly difficult sites, researchers have typically encountered shortcomings in the underlying UXO classification methodologies and have identified either enhancements to existing algorithms or completely new techniques and algorithms which improve classification performance. The goal of this project is to address these shortcomings and develop a robust and effective suite of processing and classification methodologies with wide applicability. The industry will then be in a position to tackle the full-spectrum of classification challenges encountered at the diverse UXO-contaminated sites spread across the country. Specific project objectives are to:
- Improve and extend a suite of discrimination and classification techniques to improve the efficiency and reliability of UXO classification;
- Verify the utility of these techniques using data acquired during ESTCP live site demonstrations;
- Transition the most effective algorithms to the UXO community by including them into well-established and validated methodologies together with documented workflows.
In this project, the researchers will focus on the following five broad UXO classification topics:
- Methods for processing dynamically collected advanced electromagnetic induction (EMI) data;
- Multi-source processing for data collected at cluttered sites;
- Advanced processing techniques for data acquired at sites with magnetic soils;
- Using figure of merit (FOM) for improving data quality control (QC) and assessing inversion reliability;
- Identification of unique TOI.
The researchers will extend and test techniques that improve confidence in classifying buried items as either munitions and explosives of concern (MEC) or non-hazardous clutter. The researchers will use as a starting point the techniques developed in SERDP project MR-1637, SERDP project MR-1573, and SERDP project MR-1629 that have the most promise for furthering the ability to classify MEC. These techniques will be evaluated using data from the ESTCP live site demonstration program.
As increasingly difficult sites are addressed, the researchers expect to encounter new, currently unforeseen challenges in classification that will necessitate further development and fine-tuning of the techniques and algorithms researchers have developed. Testing and evaluation of these key techniques using live site data will ensure that these techniques can be transitioned from novel concepts to robust components of practical solutions for the wide variety and ever expanding set of UXO classification challenges.
Improvements in the classification of obscured items as either UXO or non-hazardous clutter has the potential to significantly decrease clearance costs. The main outcome of the project will be a suite of processing and interpretation techniques that will improve the efficacy and reliability of UXO classification. These methodologies will be ready for incorporation into appropriate geophysical processing software. (Anticipated Project Completion - 2016)