Probabilistic 1-D Inversion of Frequency-Domain Electromagnetic Data Using a Kalman Ensemble Generator

Abstract

Frequency-domain electromagnetic (FDEM) data of the subsurface are determined by electrical conductivity and magnetic susceptibility. We apply a Kalman ensemble generator (KEG) to 1-D probabilistic multilayer inversion of the FDEM data to simultaneously derive conductivity and susceptibility. The KEG provides an efficient alternative to an exhaustive Bayesian framework for FDEM inversion, including a measure for the uncertainty of the inversion result. In addition, 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. Synthetic and field data examples 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 1-D subsurface models are performed at neighboring measurement locations. Assuming identical prior models for these inversions, we save computational time by reusing the initial KEG ensemble across all measurement locations.