A Simple Magnetic Charge Model for Classification of Multiple Buried Metallic Objects In Cases With Overlapping Signals

This paper presents an application of a simple surface magnetic charge model to discriminate<br>between objects of interest such as unexploded ordnance (UXO) and innocuous items, in cases when<br>signals from buried objects are a mixture of responses from two or more items. In the low frequency<br>(ten’s of Hertz up to several hundred’s of kHz) electromagnetic induction (EMI) sensing considered<br>here, both conduction and displacement currents may be neglected within the medium surrounding a<br>metallic object. Therefore, the scattered magnetic field outside the object is represented in terms of<br>scalar potential fields, from which one can obtain all scattered magnetic fields. While these are time<br>dependent by virtue of forcing functions or boundary conditions, they correspond in structure to static<br>fields. Such fields are appropriately, and readily, produced mathematically by equivalent elementary<br>magnetic charges placed on a convenient fictitious closed surface [1, 2, 3]. This forward model is<br>physically complete in the sense that all heterogeneity, near and far field, and internal interaction effects<br>within the object are included. It is very fast; in particular it can be implemented in inversion<br>calculations on a PC. According to the Gauss’s law,∇⋅B=ρeq , the net flux of magnetic field through any<br>closed surface equals the total (equivalent) magnetic charge ρeq inside the surface. The frequency<br>spectrum of this total induced equivalent charge is used here as a discriminant. Based on measured data<br>from two scatterers together, an iterative two step procedure is used in conjunction with the differential<br>evolution (DE) algorithm [4, 5]. One step determines of each object’s location and orientation and the<br>other determines the amplitudes of the responding fictitious magnetic charges. Once the objects are<br>isolated, the total magnetic charge for each is calculated as a function of frequency and compared to<br>cataloged/library data. Finally, blind classification analyses are performed for a single object as well as<br>for multiple subsurface scatterers, when two objects appear simultaneously within the field of view of<br>the sensor.