Highly Contaminated Uxo Sites: Combination of GPR and Emi for Discrimination of Clustered Scatterers

The most fundamental electromagnetic limitation on discrimination of subsurface unexploded ordnance (UXO) during cleanup operations is that one must use quite low frequencies to penetrate the ground. Operating between some 10’s of Hz and some 100’s kHz, electromagnetic induction (EMI) sensor signals are sensitive to many aspects of target shape and composition. In that band, the signals do not suffer the scattering and absorption loss problems that challenge ground penetrating radar (GPR). However, EMI transmitted wavelengths are many, many orders of magnitude greater than the size of targets of interest. This means that distinct targets cannot readily be picked out by timing the arrival of echoes or by noting the direction they are coming from, as for wave phenomena. Clustered targets will respond simultaneously and their signals overlap. This is a particularly important problem because most UXO cleanup sites contain much metallic clutter. The number of targets and their locations are hard to tell from EMI data only. Our full-polarimetric UWB GPR operates between some 10’s of MHz and about 800 MHz, i.e. at a low enough frequency to penetrate the soil, minimize scattering losses, and elicit essential target resonances, but necessarily too low to form precise target images. What GPR can often do, however, is time the arrival of target echoes from distinct targets, even when they are clustered, and feed into EMI processing some crucial information on number of targets, approximate locations, and other geometrical data. Altogether, EMI signal optimization constrained by GPR data produces separate EMI signature patterns for each item, indicating whether the object is UXO-like or not. Traditional fast EMI forward modeling contains too many free parameters, which is a serious challenge to inversion algorithms, especially for multiple targets. In this paper we propose a three step approach for UXO discrimination: (1) preliminary screening with GPR information to identify or rule out obvious UXO candidates; (2) Analyze EMI data with simple dipole model, using GPR information as prior information. The results are again used to identify or rule out obvious UXO candidates; (3) For cases where final decisions can not be made in step one and two, a pattern matching approach is employed to identify each candidate UXO, using the first two step results as prior information. Study on examples illustrates how this three step approach may help improve UXO discrimination and identification.