Electromagnetic induction (EMI) systems are widely employed in pipeline inspection to assess the bulk electrical conductivity of the surrounding soil. In this work, a frequency domain EMI system measures the apparent electrical conductivity (ECa) near pipelines by inducing a time-varying primary electromagnetic field into the ground using a transmitter. The electrically conductive soil around generates eddy currents in response, producing secondary electromagnetic fields. The superposed magnetic field is measured at the receiver. The imaginary component of this field is directly related to the soil conductivity (resistivity) which helps in corrosion analysis of buried pipelines.

A limitation for using EMI systems is that the accuracy is affected by time-varying external environmental factors, like ambient temperature. This makes it difficult to reproduce results and predict performances. The aim of this work is to develop a model that mitigates the effects of drifts in EMI data measured in the presence of fluctuating ambient temperature.

Results show that it is possible to improve the accuracy of the drift correction by modelling the dynamic thermal characteristics by a factor of four compared to purely static correction methods. It is therefore concluded that the novel correction method based on the modeling of the dynamic thermal characteristics of electromagnetic induction (EMI) systems using multiple temperature sensors and a low pass filter (LPF) can help to improve of measurement deviations.