|
The objective of this paper is to study the advantage of multi-axis (vector) data over scalar one-dimensional data in the electromagnetic induction (diffusion) regime in both frequency and time domains for discriminating unexploded ordnance (UXO). Particular attention is given to the time domain. Traditional magnetometers and coil-based electromagnetic induction sensors measure only one component of the scattered magnetic field. They provide high sensitivity, but one-component magnetic field measurements provide limited information about the electromagnetic signatures of buried items, particularly for target localization and determination of target parameters. Recently much effort has been directed at developing next-generation electromagnetic geophysical sensors to collect vector data; for example, Geophex has built a new 3D GEM-3 sensor, with one transmitter and three (all Hx, Hy, Hz) receiver coils, and similar capabilities exist in the time domain. In this paper a surface magnetic charge (SMC) model, in conjunction with a differential evolution (DE) algorithm, is used to treat multi-axis data to advance, motivated by potential application to discrimination of buried UXO’s. In the SMC model the scattered magnetic field is produced by a set of magnetic charges distributed mathematically around the target location. The amplitudes of these charges is determined by matching to measured magnetic fields at a selected set of points. When the charge amplitudes are normalized by the corresponding normal component of the primary field at each location, their sum is regarded as an indication of the magnetic capacity of the object and is used as a discriminant. Once the amplitude of this normalized source set is found for each object, it can be stored for subsequent use in a discrimination algorithm. Time domain SMCs are developed for highly permeable and metallic objects buried inside a magnetic half-space. Air/magnetic ground interface effects are taken into account using image theory. Examples of synthetic electromagnetic induction data sets in the time domain are designed to show the advantage of vector over scalar data. The numerical tests for inversion of an object’s location and position from the multi-axis data and single component data will are discussed and analyzed in detail.
|
