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### Proceedings Paper

Dumbbell dipole model and its application in UXO discrimination
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Paper Abstract

Electromagnetic Induction (EMI) is one of the most promising techniques for UXO discrimination. Target discrimination is usually formulated as an inverse problem typically requiring fast forward models for efficiency. The most successful and widely applied EMI forward model is the simple dipole model, which works well for simple objects when the observation points are not close to the target. For complicated cases, a single dipole is not sufficient and a number of dipoles (displaced dipoles) has been suggested. However, once more than one dipole is needed, it is difficult to infer a unique set of model parameters from measurement data, which is usually limited. Inspired by the displaced dipole model, we developed the dumbbell dipole model, which consists of a special combination of dipoles. We placed a center dipole and two anti-symmetric side dipoles on the target axis. The center dipole functions like the traditional single dipole model and the two side dipoles provide the non-symmetric response of the target. When the distance between dipoles is small, this model is essentially a dipole plus a quadrupole. The advantage of the dumbbell model is that the model parameters can be inferred more easily from measurement data. The center dipole represents the main response of the target, the side dipoles act as additional backup in case a simple dipole is not sufficient. Regularization terms are applied so that the dumbbell dipole model automatically reduces to the simple dipole model in degenerate cases. Preliminary test shows that the dumbbell model can fit the measurement data better than the simple dipole model, and the inferred model parameters are unique for a given UXO. This suggests that the model parameters can be used as a discriminator for UXO. In this paper the dumbbell dipole model is introduced and its performance is compared with that of both the simple dipole model and the displaced dipole model.

Paper Details

Date Published: 16 May 2006
PDF: 12 pages
Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 621706 (16 May 2006); doi: 10.1117/12.667997
Show Author Affiliations
K. Sun, Dartmouth College (United States)
K. O'Neill, U.S. Army ERDC Cold Regions Research and Engineering Lab. (United States)
Dartmouth College (United States)
B. E. Barrowes, U.S. Army ERDC Cold Regions Research and Engineering Lab. (United States)
J. P. Fernández, Dartmouth College (United States)
F. Shubitidze, Dartmouth College (United States)
I. Shamatava, Dartmouth College (United States)
K. D. Paulsen, Dartmouth College (United States)

Published in SPIE Proceedings Vol. 6217:
Detection and Remediation Technologies for Mines and Minelike Targets XI
J. Thomas Broach; Russell S. Harmon; John H. Holloway Jr., Editor(s)