Inversion of dynamically repositioned multi-axis electromagnetic data for ordnance characterization

The challenges associated with removing UXO and explosive remnants of war have led to a variety of methods for detection and discrimination of buried metallic objects using time-domain electromagnetic induction (EMI). Recent work has shown that parameters recovered from physics-based inversions can discriminate and classify buried ordnance from non-ordnance. We present results of applying advanced processing to data from a dynamically repositioned multiaxis EMI instrument. Data are collected using an adaptive sampling process to find the center of the anomaly and collect minimal data while maintaining model fidelity. An ortho-normalized volume magnetic source (ONVMS) model is used to resolve various targets at different depths. The ONVMS model is a generalized volume dipole model, with the single dipole model being a special limiting case. Using the ONVMS model, an object's response to a sensor's primary magnetic field is modeled mathematically by a set of equivalent magnetic dipoles distributed inside a volume containing the object. We assess the utility and veracity of the dynamic sampling strategy coupled with the ONVMS model on data acquired over a set of calibration and simulant targets. Rapid target characterization codes are aggregated into a software package with particular focus on ease of use for non-expert users.