Current time-domain electromagnetic induction instruments generally only utilize data acquired after the cessation of the
transmitted field. During this "off time", signals are dominated by induced eddy currents and magnetic surface modes,
but do not fully capture the magnetostatic response of permeable and conductive metallic ordnance. In this paper, we
investigate the response of EMI systems that measure signals during excitation of the primary magnetic field (the so-called
"on-time"). Our analysis shows that on-time signals have great potential to yield useful information that is not
often exploited in current EMI systems. We compare analytical models to data from state-of-the-art time-domain EM
sensors that have the capability to sample receivers during the on-time. We present modeling results that represent the
responses from different current ramps and on-time waveforms for objects and ground. We consider target and clutter
objects and grounds having a range of material properties, shapes and sizes, and configurations and investigate signal
processing and inversion methods for target detection and discrimination. Specifically, correlations between on-time
and off-time signals are shown to be a powerful tool for discriminating ferrous and non-ferrous metallic objects.
In UXO contaminated sites, there are often cases in which two or more targets are likely close together and
the electromagnetic induction sensors record overlapping signals contributed from each individual target. It is
important to develop inversion techniques that have the ability to recover parameters for each object so that
effective discrimination can be performed. The multi-object inversion problem is numerically challenging because
of the increased number of parameters to be found and because of the additional nonlinearity and non-uniqueness.
An inversion algorithm is easily trapped in a local minimum of the objective function that is being minimized.
To tackle these problems we exploit the fact that, based on an equivalent magnetic dipole model, the measured
electromagnetic induction signals are nonlinear functions of locations and orientations of equivalent dipoles and
linear functions of their polarizations. Based on these conditions, we separate model parameters into nonlinear
parts (source locations and orientations) and linear parts (source polarizations) and proceed sequentially. We
propose a selected multi-start nonlinear procedure to first localize multiple sources and then get the estimated
polarization tensor matrix for each item through a subsequent or a nested linear inverse problem. It follows that
the orientations of the objects are estimated from the computed tensor matrix. The resultant parameter set is
input to a complete nonlinear inversion where all of the dipole parameters are estimated. The overall process can
be automated and thus efficiently carried out both in terms of human interaction and numerical computation
time. We validate the technique using synthetic and field data.