  Frequency-domain electromagnetic (FDEM) data of the subsurface are determined
by electrical conductivity and magnetic susceptibility. We apply a Kalman
Ensemble generator (KEG) to one-dimensional probabilistic multi-layer inversion
of FDEM data to derive conductivity and susceptibility simultaneously. The KEG
provides an efficient alternative to an exhaustive Bayesian framework for FDEM
inversion, including a measure for the uncertainty of the inversion result.
Additionally, the method provides a measure for the depth below which the
measurement is insensitive to the parameters of the subsurface. This so-called
depth of investigation is derived from ensemble covariances. A synthetic and a
field data example reveal how the KEG approach can be applied to FDEM data and
how FDEM calibration data and prior beliefs can be combined in the inversion
procedure. For the field data set, many inversions for one-dimensional
subsurface models are performed at neighbouring measurement locations. Assuming
identical prior models for these inversions, we save computational time by
re-using the initial KEG ensemble across all measurement locations.
