Remediation of unexploded ordnance (UXO) at former target range sites and return of the land to public use is a central environmental policy goal of the U.S. government. Successful remediation requires essentially 100% probability of detection (PD), but economically practical remediation requires dramatic reduction of the false alarm rates caused by clutter items needlessly excavated at great expense. A high-performance integrated solution to this problem must combine a high PD sensor technology with an on-board discrimination capability that processes sensor data for key identifying target features for use in target identification and clutter rejection.
The aim of this effort was to deliver a software product suitable for transition to time-domain electromagnetic (TDEM)-based sensors currently used for UXO discrimination. The methodology would simultaneously address the requirements of high fidelity physics-based modeling for realistic target shapes and vastly accelerated Central Processing Unit efficiency for forward modeling and inversion and subsequent discrimination.
The recently developed physics-based mean field formalism for efficiently computing the time-domain response of compact metallic targets is applied to the solution of model inverse problems for remote classification of buried UXO-like targets. The formalism is first used to compute model forward scattering data, in the form of time-domain decay curves as measured by electromagnetic induction (EMI) or magnetic field, for a sequence of canonical ellipsoidal target shapes of various geometries. This data is subsequently used as input to a genetic algorithm-based inversion routine in which the target parameter model space is efficiently searched to find the best fit to the data. Global search procedures typically require the forward scattering solution for hundreds of candidate target models. To be practical, these forward solutions must be rapidly computable. The solution approach developed in this project has been specifically designed to meet this requirement. Of special interest is the ability of the inversion algorithm to distinguish between UXO-like targets.
Researchers generalized the physics-based mean field EM modeling approach to a broader range of target geometries and EM parameters. A genetic algorithm-based inversion code for target discrimination was developed using the mean field numerical code as the core forward model. A novel rigorous approach to modeling the early-time EMI response of metallic targets, complementing the intermediate- to late-time capability of the mean field approach, also was developed, thereby providing an efficient EM modeling capability covering the entire time-domain measurement range.
The potential economic benefit of this work is reduction in remediation costs due to reductions in false alarm rates and real-time on-site discrimination. The scientific benefit is a completely new method for rapid EM computations that should eventually find application in other areas such as the mining industry and public infrastructure evaluation. (Project Completed – 2005)