% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{Tazifor:908112,
author = {Tazifor, Martial and Zimmermann, Egon and Huisman, Johan
Alexander and Dick, Markus and Mester, Achim and van Waasen,
Stefan},
title = {{M}odel-{B}ased {C}orrection of {T}emperature-{D}ependent
{M}easurement {E}rrors in {F}requency {D}omain
{E}lectromagnetic {I}nduction ({FDEMI}) {S}ystems},
journal = {Sensors},
volume = {22},
number = {10},
issn = {1424-8220},
address = {Basel},
publisher = {MDPI},
reportid = {FZJ-2022-02382},
pages = {3882 -},
year = {2022},
abstract = {Data measured using electromagnetic induction (EMI) systems
are known to be susceptible to measurement influences
associated with time-varying external ambient factors.
Temperature variation is one of the most prominent factors
causing drift in EMI data, leading to non-reproducible
measurement results. Typical approaches to mitigate drift
effects in EMI instruments rely on a temperature drift
calibration, where the instrument is heated up to specific
temperatures in a controlled environment and the observed
drift is determined to derive a static thermal apparent
electrical conductivity (ECa) drift correction. In this
study, a novel correction method is presented that models
the dynamic characteristics of drift using a low-pass filter
(LPF) and uses it for correction. The method is developed
and tested using a customized EMI device with an intercoil
spacing of 1.2 m, optimized for low drift and equipped with
ten temperature sensors that simultaneously measure the
internal ambient temperature across the device. The device
is used to perform outdoor calibration measurements over a
period of 16 days for a wide range of temperatures. The
measured temperature-dependent ECa drift of the system
without corrections is approximately 2.27 mSm−1K−1, with
a standard deviation (std) of only 30 μSm−1K−1 for a
temperature variation of around 30 K. The use of the novel
correction method reduces the overall root mean square error
(RMSE) for all datasets from 15.7 mSm−1 to a value of only
0.48 mSm−1. In comparison, a method using a purely static
characterization of drift could only reduce the error to an
RMSE of 1.97 mSm−1. The results show that modeling the
dynamic thermal characteristics of the drift helps to
improve the accuracy by a factor of four compared to a
purely static characterization. It is concluded that the
modeling of the dynamic thermal characteristics of EMI
systems is relevant for improved drift correction.},
cin = {ZEA-2 / IBG-3},
ddc = {620},
cid = {I:(DE-Juel1)ZEA-2-20090406 / I:(DE-Juel1)IBG-3-20101118},
pnm = {2173 - Agro-biogeosystems: controls, feedbacks and impact
(POF4-217)},
pid = {G:(DE-HGF)POF4-2173},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:35632291},
UT = {WOS:000802486100001},
doi = {10.3390/s22103882},
url = {https://juser.fz-juelich.de/record/908112},
}