Probabilistic One-Dimensional Inversion of Frequency-Domain Electromagnetic Data Using a Kalman Ensemble Generator

TL;DR

This paper introduces a Kalman Ensemble Generator (KEG) method for probabilistic one-dimensional inversion of frequency-domain electromagnetic data, efficiently estimating subsurface conductivity and susceptibility with uncertainty quantification.

Contribution

The novel KEG approach offers an efficient alternative to Bayesian methods for FDEM inversion, providing uncertainty estimates and depth of investigation without exhaustive computation.

Findings

KEG accurately estimates subsurface properties from synthetic and field data.

The method quantifies uncertainty and depth of investigation effectively.

Reusing initial ensembles reduces computational time across multiple locations.

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 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.