Large-Scale 3D Multi-Layer Electromagnetic Induction Data Inversion

Abstract

Multi-coil electromagnetic induction (EMI) systems sense different depth levels and offer the potential to estimate the vertical subsurface electrical conductivity distribution with a high spatial resolution. However, due to the complicated and overlapping sensitivity functions of each coil configuration, it is not straightforward to characterize a layered subsurface. Moreover, EMI measurements are influenced by external conditions such as the operator, field set-up, cables or any other current conducting material close the system, such that the recorded value is shifted, which hinders a quantitative interpretation of the measured apparent electrical conductivities (ECa). Therefore, measured ECa need to be calibrated and inverted to obtain a reliable layered subsurface electrical model. The calibration is performed by a linear regression between predicted ECa, obtained from an electromagnetic (EM) forward model that numerically solves the Maxwell equations to predict ECa using inverted electrical resistivity tomography (ERT) data recorded at a small transect as input, and collocated measured EMI-ECa. The coil specific regression parameters are then used to calibrate the large-scale EMI data. Next, the calibrated multi-coil EMI data are re-gridded to a common grid, such that a one dimensional (1D) multi-layer conductivity inversion can be performed. To invert the quantitative ECa, we use a parallelized version of the shuffled complex evolution (SCE) optimization and minimize the misfit between the measured and modelled data obtained from the full-solution EM forward model using the L1-norm while assuming a horizontally layered earth. The obtained 1D-models at each grid node are stitched together to form a 3D subsurface volume. We applied this method to a data set obtained at an experimental field covering an area of 11400 m2. The smoothly changing lateral and vertical electrical conductivity model was validated with grain size distribution maps and two previously measured 120 m long ERT transects. Overall, the subsurface model obtained with the quasi-3D EMI inversion and the independent ERT inversions showed similar subsurface structures. Differences in absolute electrical conductivity values within certain layers are probably due to the varying soil moisture content. These findings indicate that EMI can be successfully used to quantitatively characterize the lateral and vertical electrical conductivity structures at the field scale and beyond