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@INPROCEEDINGS{TchantchoAminTazifor:905157,
      author       = {Tchantcho Amin Tazifor, Martial and Zimmermann, Egon and
                      Huisman, Johan Alexander and Dick, Markus and Mester, Achim
                      and van Waasen, Stefan},
      title        = {{MODEL}-{BASED} {CORRECTION} {METHOD} {FOR}
                      {TEMPERATURE}-{DEPENDENT} {MEASUREMENT} {ERRORS} {IN} {EMI}
                      {SYSTEMS}},
      school       = {Universität Duisburg-Essen},
      reportid     = {FZJ-2022-00449},
      year         = {2021},
      abstract     = {Electromagnetic induction (EMI) is a non-invasive and fast
                      geophysical measurement technique that provides information
                      about the uppermost meters of the subsurface with a spatial
                      resolution in the sub-meter range. Frequency domain EMI
                      systems measure the apparent electrical conductivity (ECa)
                      of the soil by inducing a time-varying primary
                      electromagnetic field into the ground using a sender. Since
                      the subsurface is electrically conductive, the primary field
                      produces eddy currents that lead to the generation of
                      secondary electromagnetic fields. The superposition of the
                      secondary and the primary electromagnetic field is measured
                      at a receiver, and the imaginary part of this superposed
                      magnetic field is related to the ECa of the subsurface.Data
                      measured using 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 poor
                      predictive performance and non-reproducibility of results.
                      Typical approaches to mitigate drift effects in EMI
                      instruments are performing a temperature drift calibration
                      where the instrument is heated up to specific temperatures
                      in a controlled environment and the observed drifts are
                      collected in a lookup table for a static ECa correction.An
                      enhanced correction method is presented that models the
                      dynamic characteristics of drift and later uses it for
                      correction. The model is tested with a custom-made EMI
                      device equipped with ten temperature sensors that
                      simultaneously measure the internal ambient temperature
                      across the device. The device was used to perform outdoor
                      calibration measurements over a period of 16 days within a
                      wide range of temperatures. In order to reduce the
                      influences of soil variation over time, the instrument
                      measured ECa at a height of 0.7 m with an intercoil spacing
                      of 1.2 m. In contrast to typical approaches involving static
                      thermal ECa error correction based on a look-up table, this
                      new approach models the dynamic thermal characteristics of
                      the drift and actively uses it for correction.The results
                      are showing that modelling the dynamic thermal
                      characteristics of the drift helps to improve accuracy by a
                      factor of five compared to purely static characterization
                      with a look-up table. In addition, the modelling parameters
                      used for drift correction are very stable for all sixteen
                      datasets. For instance, the average temperature-dependent
                      ECa drift of about 2.45 mSm-1K-1 fluctuates only by 0.04
                      mSm-1K-1 between measurements for a temperature variation of
                      about 30 °C. These results suggested that our enhanced
                      correction method using the modelling of dynamic thermal
                      characteristics of EMI systems is a relevant method and
                      beneficial for usage to improve drift correction.},
      month         = {Sep},
      date          = {2021-09-06},
      organization  = {World Multidisciplinary Earth Sciences
                       Symposium, Prague (Czech Republic), 6
                       Sep 2021 - 10 Sep 2021},
      subtyp        = {After Call},
      cin          = {ZEA-2 / IBG-3},
      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)24},
      url          = {https://juser.fz-juelich.de/record/905157},
}