The identification of unexploded ordnance (UXO) using electromagnetic-induction (EMI) sensors involves two essentially
independent steps: Each anomaly detected by the sensor has to be located fairly accurately, and its orientation determined,
before one can try to find size/shape/composition properties that identify the object uniquely. The dependence on the
latter parameters is linear, and can be solved for efficiently using for example the Normalized Surface Magnetic Charge
model. The location and orientation, on the other hand, have a nonlinear effect on the measurable scattered field, making
their determination much more time-consuming and thus hampering the ability to carry out discrimination in real time. In
particular, it is difficult to resolve for depth when one has measurements taken at only one instrument elevation.
In view of the difficulties posed by direct inversion, we propose using a Support Vector Machine (SVM) to infer the
location and orientation of buried UXO. SVMs are a method of supervised machine learning: the user can train a computer
program by feeding it features of representative examples, and the machine, in turn, can generalize this information by
finding underlying patterns and using them to classify or regress unseen instances. In this work we train an SVM using
measured-field information, for both synthetic and experimental data, and evaluate its ability to predict the location of
different buried objects to reasonable accuracy. We explore various combinations of input data and learning parameters in
search of an optimal predictive configuration.