Mapping of Peat Thickness Using a Multi-Receiver Electromagnetic Induction Instrument

Abstract

Peatlands constitute extremely valuable areas because of their ability to store large amounts of soil organic carbon (SOC). Investigating different key peat soil properties, such as the extent, thickness (or depth to mineral soil) and bulk density, is highly relevant for the precise calculation of the amount of stored SOC at the field scale. However, conventional peat coring surveys are both labor-intensive and time-consuming, and indirect mapping methods based on proximal sensors appear as a powerful supplement to traditional surveys. The aim of the present study was to assess the use of a non-invasive electromagnetic induction (EMI) technique as an augmentation to a traditional peat coring survey that provides localized and discrete measurements. In particular, a DUALEM-421S instrument was used to measure the apparent electrical conductivity (ECa) over a 10-ha field located in Jutland, Denmark. In the study area, the peat thickness varied notably from north to south, with a range from 3 to 730 cm. Simple and multiple linear regressions with soil observations from 110 sites were used to predict peat thickness from (a) raw ECa measurements (i.e., single and multiple-coil predictions), (b) true electrical conductivity (σ) estimates calculated using a quasi-three-dimensional inversion algorithm and (c) different combinations of ECa data with environmental covariates (i.e., light detection and ranging (LiDAR)-based elevation and derived terrain attributes). The results indicated that raw ECa data can already constitute relevant predictors for peat thickness in the study area, with single-coil predictions yielding substantial accuracies with coefficients of determination (R2) ranging from 0.63 to 0.86 and root mean square error (RMSE) values between 74 and 122 cm, depending on the measuring DUALEM-421S coil configuration. While the combinations of ECa data (both single and multiple-coil) with elevation generally provided slightly higher accuracies, the uncertainty estimates for single-coil predictions were smaller (i.e., smaller 95% confidence intervals). The present study demonstrates a high potential for EMI data to be used for peat thickness mapping.

The Application of Electromagnetic Induction Methods to Reveal the Hydrogeological Structure of a Riparian Wetland

“…These findings agree with the recent work of Beucher et al. (2020) who found that the best approach for determining peat thickness was using a linear regression method and that it performed better than inverse models obtained from using the Aarhus workbench (Auken et al., 2009). Moreover, given that at low conductivity values the ERT calibration is assumed linear, bypassing the ERT calibration of the EMI data does not substantially reduce the performance of the multi‐linear regression prediction method.…”

“…However, recently the usage of frequency-domain electromagnetic induction (EMI) methods for wetland characterization has increased; this is in part due to the ease at which relatively large areas can be surveyed (e.g. von Hebel et al, 2014;Rejiba et al, 2018;Beucher et al, 2020). Furthermore, although the work here focusses on EMI methods it is worth noting that other geophysical methods have been employed successfully in similar wetland environments, e.g.…”

“…In comparison, Beucher et al. (2020) used both sharp and smooth inversions but concluded that predictions from linear regressions with raw data were best for structural characterization when comparing with a limited intrusive data set. In addition to characterizing the subsurface structure, Brosten et al.…”

“…Similar to Frederiksen et al (2017), Boaga et al (2020) used an iso-resistivity method and found that EMI data were able to resolve the boundary between peat and clay deposits with reasonable accuracy. In comparison, Beucher et al (2020) used both sharp and smooth inversions but concluded that predictions from linear regressions with raw data were best for structural characterization when comparing with a limited intrusive data set. In addition to characterizing the subsurface structure, Brosten et al (2011) investigated the link between EMI and hydraulic conductivity with a smooth inversion algorithm.…”

Laterally constrained inversion (LCI) of multi-configuration EMI data with tunable sharpness

“…Portable electromagnetic induction (EMI) sensors using harmonic source waveforms (also known as frequency-domain electromagnetics -FDEM) are commonly used to characterize near-surface geoelectrical properties. Such methods are used in various applications including archaeological prospection (De Smedt 5 et al, 2014;Dabas et al, 2016;von Hebel et al, 2021), precision agriculture (Jadoon et al, 2015;Rudolph et al, 2016;Brogi et al, 2019), hydrological studies (Vereecken et al, 2015;von Hebel et al, 2014;Rezaei et al, 2016;Robinet et al, 2018), and environmental studies including the exploration of peat deposits (Altdorff et al, 2016;Beucher et al, 2020;Clément et al, 2020;10 McLachlan et al, 2020). Modern single-frequency, multi-configuration sensors can simultaneously sense the subsurface electrical conductivity for different volumes of investigation.…”