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Ambient temperature and relative humidity-based drift correction in frequency domain electromagnetics using machine learning

(2021) NEAR SURFACE GEOPHYSICS. 19(5). p.541-556
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Abstract
Electromagnetic instrument responses suffer from signal drift that results in a variable response at a given location over time. If left uncorrected, spatiotemporal aliasing can manifest and global trends or abrupt changes might be observed in the data, which are independent of subsurface electromagnetic variations. By performing static ground measurements, we characterized drift patterns of different electromagnetic instruments. Next, we performed static measurements at an elevated height, approximately 4 metre above ground level, to collect a data set that forms the basis of a new absolute calibration methodology. By additionally logging ambient temperature variations, battery voltage and relative humidity, a relation between signal drift and these parameters was modelled using a machine learning (ML) approach. The results show that it was possible to mitigate the effects of signal drift; however, it was not possible to completely eliminate them. The reason is three-fold: (1) the ML algorithm is not yet sufficiently adapted for accurate prediction; (2) signal instability is not explained sufficiently by ambient temperature, relative humidity and battery voltage; and (3) the black-box internal (factory) calibration impeded direct access to raw data, which prevents accurate evaluation of the proposed methodology. However, the results suggest that these challenges are not insurmountable and that ML can form a viable approach in tackling the drift problem instrument specific in the near future.
Keywords
Geophysics, Calibration, Electromagnetic induction, Machine learning, Temperature, SOIL ELECTRICAL-CONDUCTIVITY, CALIBRATION, INSTRUMENTS

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MLA
Hanssens, Daan, et al. “Ambient Temperature and Relative Humidity-Based Drift Correction in Frequency Domain Electromagnetics Using Machine Learning.” NEAR SURFACE GEOPHYSICS, vol. 19, no. 5, 2021, pp. 541–56, doi:10.1002/nsg.12160.
APA
Hanssens, D., Van De Vijver, E., Waegeman, W., Everett, M. E., Moffat, I., Sarris, A., & De Smedt, P. (2021). Ambient temperature and relative humidity-based drift correction in frequency domain electromagnetics using machine learning. NEAR SURFACE GEOPHYSICS, 19(5), 541–556. https://doi.org/10.1002/nsg.12160
Chicago author-date
Hanssens, Daan, Ellen Van De Vijver, Willem Waegeman, Mark E. Everett, Ian Moffat, Apostolos Sarris, and Philippe De Smedt. 2021. “Ambient Temperature and Relative Humidity-Based Drift Correction in Frequency Domain Electromagnetics Using Machine Learning.” NEAR SURFACE GEOPHYSICS 19 (5): 541–56. https://doi.org/10.1002/nsg.12160.
Chicago author-date (all authors)
Hanssens, Daan, Ellen Van De Vijver, Willem Waegeman, Mark E. Everett, Ian Moffat, Apostolos Sarris, and Philippe De Smedt. 2021. “Ambient Temperature and Relative Humidity-Based Drift Correction in Frequency Domain Electromagnetics Using Machine Learning.” NEAR SURFACE GEOPHYSICS 19 (5): 541–556. doi:10.1002/nsg.12160.
Vancouver
1.
Hanssens D, Van De Vijver E, Waegeman W, Everett ME, Moffat I, Sarris A, et al. Ambient temperature and relative humidity-based drift correction in frequency domain electromagnetics using machine learning. NEAR SURFACE GEOPHYSICS. 2021;19(5):541–56.
IEEE
[1]
D. Hanssens et al., “Ambient temperature and relative humidity-based drift correction in frequency domain electromagnetics using machine learning,” NEAR SURFACE GEOPHYSICS, vol. 19, no. 5, pp. 541–556, 2021.
@article{8720065,
  abstract     = {{Electromagnetic instrument responses suffer from signal drift that results in a variable response at a given location over time. If left uncorrected, spatiotemporal aliasing can manifest and global trends or abrupt changes might be observed in the data, which are independent of subsurface electromagnetic variations. By performing static ground measurements, we characterized drift patterns of different electromagnetic instruments. Next, we performed static measurements at an elevated height, approximately 4 metre above ground level, to collect a data set that forms the basis of a new absolute calibration methodology. By additionally logging ambient temperature variations, battery voltage and relative humidity, a relation between signal drift and these parameters was modelled using a machine learning (ML) approach. The results show that it was possible to mitigate the effects of signal drift; however, it was not possible to completely eliminate them. The reason is three-fold: (1) the ML algorithm is not yet sufficiently adapted for accurate prediction; (2) signal instability is not explained sufficiently by ambient temperature, relative humidity and battery voltage; and (3) the black-box internal (factory) calibration impeded direct access to raw data, which prevents accurate evaluation of the proposed methodology. However, the results suggest that these challenges are not insurmountable and that ML can form a viable approach in tackling the drift problem instrument specific in the near future.}},
  author       = {{Hanssens, Daan and Van De Vijver, Ellen and Waegeman, Willem and Everett, Mark E. and Moffat, Ian and Sarris, Apostolos and De Smedt, Philippe}},
  issn         = {{1569-4445}},
  journal      = {{NEAR SURFACE GEOPHYSICS}},
  keywords     = {{Geophysics,Calibration,Electromagnetic induction,Machine learning,Temperature,SOIL ELECTRICAL-CONDUCTIVITY,CALIBRATION,INSTRUMENTS}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{541--556}},
  title        = {{Ambient temperature and relative humidity-based drift correction in frequency domain electromagnetics using machine learning}},
  url          = {{http://dx.doi.org/10.1002/nsg.12160}},
  volume       = {{19}},
  year         = {{2021}},
}

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