André, Frédéric
[UCL]
Lambot, Sébastien
[UCL]
We present a closed-form equation for intrinsic modeling of near-field electromagnetic induction (EMI) antennas for planar layered media characterization. Resorting to a decomposition of the backscattered EM field into elementary distributions over the antenna aperture, the EMI transmitting and receiving antennas are modeled using infinitesimal magnetic dipoles and field points, and characteristic frequency-dependent global reflectionand transmission coefficients. Low-frequency propagation of the EM fields in the medium is described using 3-D planar layered media Green’s functions.We performedmeasurements with a loop antenna situated at different heights, ranging from near-field to far-field conditions, above water of known electrical conductivity to determine its intrinsic properties, and a range of salinity conditions was applied to subsequently validate the proposed model. The EMI system was set up using a vector network analyzer equipped with a prototype EMI antenna particularly designed for this application. Themodel showed good accuracy for reproducing the observed data, and model inversion provided good estimates of the medium electrical conductivity. Yet, insensitivity of the EMI signal to water electrical conductivity was encountered for low salinity due to the presence of a copper sheet as the bottom boundary condition of the experimental setup. Moreover, the efficiency of the antenna decreased rapidly as antenna height above water surface increases, leading to increasing discrepancies between estimated and measured water electrical conductivity values as the antenna moves away from the water surface. Although some technical improvements are still needed, the proposed approach is promising for quantitative estimation of soil electrical conductivity from EMI data.
Bibliographic reference |
André, Frédéric ; Lambot, Sébastien. Intrinsic Modeling of Near-Field Electromagnetic Induction Antennas for Layered Medium Characterization. In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, no. 11, p. 7457-7469 (November 2014) |
Permanent URL |
http://hdl.handle.net/2078.1/142662 |