UXO Classification Demonstrations at Live Sites Using UX-Analyze
Dr. Dean Keiswetter | Acorn SI
The objective of this project is to advance classification capabilities of the production community by enhancing and streamlining UX-Analyze’s workflow and functionality; by offering training documents, datasets, and workshops; by baselining classification performances through classification demonstrations; and by collaborating with multiple production firms, each of whom hold key geophysical services contracts for the government, as they perform data analysis services in support of future classification demonstrations.
The technology and analysis algorithms to be demonstrated were developed in ESTCP projects MR-200810 and MR-200910. By virtue of the past projects, UX-Analyze has been fully integrated into Oasis montaj as a menu-driven set of functions for geophysical target classification, modeling, and analysis. These functions permit users to effectively discriminate munitions targets. At its core, UX-Analyze provides four fundamental capabilities. These relate to 1) visualizing the measured data and anomaly extracting (not needed for cued data collections); 2) performing data inversions; 3) deriving a classification decision metric; and 4) documenting the decision. UX-Analyze was designed to automate analysis tasks that do not require user expertise, quantify the decision process, and produce transparent decisions. In collaboration with built-in Oasis montaj functionality and capabilities, UX-Analyze provides the tools, procedures, and graphics necessary to characterize, classify, and rank order source objects based on their electromagnetic induction (EMI) signature when interrogated by an advanced EMI sensor. Among other things, UX-Analyze inverts measured data for the targets’ intrinsic magnetic polarizabilities, which, in turn, provide information regarding the targets’ size, shape, wall thickness, and material type. It also classifies anomalies based on how similar their polarizabilities are to unexploded ordnance (UXO) signatures. Although this process sounds complex, perhaps because of the data inversion and classification step, UX-Analyze makes it quite simple to implement.
The principle benefits that successful classification methodologies bring to the table are financial. By correctly identifying high confidence clutter objects based on their EMI signatures prior to excavation, savings can be realized by leaving the clutter in the ground or by changing the manner in which the excavations are performed. The estimated cost to clean up UXO on known Department of Defense (DoD) land without classification exceeds $14 billion. Results of past ESTCP-led live site classification demonstrations at seven sites, however, show that on most sites, new technologies can distinguish metallic scrap 70-90% of the time. (Anticipated Project Completion - 2016)
Points of Contact
Dr. Dean Keiswetter
SERDP and ESTCP