Tazifor Martial, Zimmermann Egon, Huisman Johan Alexander, Dick Markus, Mester Achim, Van Waasen Stefan
Central Institute of Engineering, Electronics and Analytics (ZEA-2), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.
Institute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.
Sensors (Basel). 2022 May 20;22(10):3882. doi: 10.3390/s22103882.
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 mSmK, with a standard deviation (std) of only 30 μSmK 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 to a value of only 0.48 mSm. In comparison, a method using a purely static characterization of drift could only reduce the error to an RMSE of 1.97 mSm. 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.
已知使用电磁感应（EMI）系统测量的数据容易受到与随时间变化的外部环境因素相关的测量影响。温度变化是导致EMI数据漂移的最突出因素之一，会导致测量结果不可重复。减轻EMI仪器中漂移影响的典型方法依赖于温度漂移校准，即在受控环境中将仪器加热到特定温度，并确定观察到的漂移以得出静态热表观电导率（ECa）漂移校正值。在本研究中，提出了一种新颖的校正方法，该方法使用低通滤波器（LPF）对漂移的动态特性进行建模，并将其用于校正。该方法是使用定制的EMI设备开发和测试的，该设备的线圈间距为1.2 m，针对低漂移进行了优化，并配备了十个温度传感器，可同时测量整个设备内部的环境温度。该设备用于在16天的时间内对各种温度进行户外校准测量。未经校正的系统测量的与温度相关的ECa漂移约为2.27 mS/mK，对于约30 K的温度变化，标准偏差（std）仅为30 μS/mK。使用新颖的校正方法可将所有数据集的总体均方根误差（RMSE）从15.7 mS/m降至仅0.48 mS/m的值。相比之下，一种仅使用漂移的纯静态特征的方法只能将误差降低到RMSE为1.97 mS/m。结果表明，与纯静态特征相比，对漂移的动态热特性进行建模有助于将精度提高四倍。得出的结论是，对EMI系统的动态热特性进行建模对于改进漂移校正具有重要意义。
