Electromagnetic induction calibration using apparent electrical conductivity modelling based on electrical resistivity tomography

Abstract

Electromagnetic parameters of the subsurface such as electrical conductivity are of great interest for non‐destructive determination of soil properties (e.g., clay content) or hydrologic state variables (e.g., soil water content). In the past decade, several non‐invasive geophysical methods have been developed to measure subsurface parameters in situ. Among these methods, electromagnetic (EM) induction appears to be the most efficient one that is able to cover large areas in a short time. However, this method currently does not provide absolute values of electrical conductivity due to calibration problems, which hinders a quantitative analysis of the measurement. In this study, we propose to calibrate EM induction measurements with electrical conductivity values measured with electrical resistivity tomography (ERT). EM induction measures an apparent electrical conductivity at the surface, which represents a weighted average of the electrical conductivity distribution over a certain depth range, whereas ERT inversion can provide absolute values for local conductivities as a function of depth. EM induction and ERT measurements were collected along a 120‐metre‐long transect. To reconstruct the apparent electrical conductivity measured with EM induction, the inverted ERT data were used as input in an electromagnetic forward modelling tool for magnetic dipoles over a horizontally layered medium considering the frequencies and offsets used by the EM induction instruments. Comparison of the calculated and measured apparent electrical conductivities shows very similar trends but a shift in absolute values, which is attributed to system calibration problems. The observed shift can be corrected for by linear regression. This new calibration strategy for EM induction measurements now enables the quantitative mapping of electrical conductivity values over large areas.

Improved Geoarchaeological Mapping with Electromagnetic Induction Instruments from Dedicated Processing and Inversion

“…Prior to the field campaign the instrument was calibrated using an approach close to the one suggested by Lavoue [38]. Though, a word of caution is required with respect to calibration using models acquired from ERT measurements, which have inherent equivalences.…”

Methods to estimate changes in soil water for phenotyping root activity in the field

“…The primary purpose of ERT in this work was to calibrate EMI measurements (Shanahan et al 2015) rather than screen large numbers of genotypes. Calibration of EMI is needed because electromagnetic induction systems return only qualitative values for electrical conductivity because of instrument calibration difficulties (Lavoué et al 2010). This drawback can be overcome by adjusting EMI data to match the more reliable ERT measurements as described by (Shanahan et al 2015).…”

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

“…McNeill's linear approach is well suited to the cases characterized by an induction number B (defined as the ratio between the coil distance and the skin depth) much smaller than 1. However, because of the increasing computing power, improved forward modeling algorithms based on more accurate nonlinear approaches are becoming increasingly common (Hendrickx et al, 2002;Deidda et al, 2003Deidda et al, , 2014Lavoué et al, 2010;Monteiro Santos et al, 2010). For example, these more sophisticated forward modeling codes can cope with a wider range of conductivities for which the assumption B 1 is not necessarily met.…”

“…That said, to monitor salinity and water content, it is crucial to correctly infer the depth-distribution of σ b from profileintegrated EC a readings. To date, this issue has been tackled by applying two different strategies: the first is to use empirical calibration relations relating the depth-integrated EC a readings to the σ b values measured by alternative methods -like Time-domain reflectometry (TDR) -within discrete depth intervals (Rhoades and Corwin, 1981;Lesch et al, 1992;Triantafilis et al, 2000;Amezketa, 2006;Yao and Jingsong, 2010;Coppola et al, 2016). The second consists of the 1-D inversion of the observations from the EMI sensor to reconstruct the vertical conductivity profile (Borchers et al, 1997;Hendrickx et al, 2002;Monteiro Santos et al, 2010;Lavoué et al, 2010;Mester et al, 2011;Minsley et al, 2012;Deidda et al, 2014;Von Hebel et al, 2014).…”