When exploring subsurface environments using electromagnetic (EM) induction (EMI) tools, approximate forward-modeling methods based on a homogeneous half-space kernel have been extensively evaluated in the past. For large-scale exploration methods, such as magnetotellurics, marine EM, airborne EM, transient EM, and large offset loop-loop harmonic EM, such forward-modeling approaches are limited because the kernel depends strongly on the subsurface distribution of electrical conductivity. However, the response of small portable EMI loop-loop sensors applied in a low-induction number (LIN) context are known to be more linearly related to the true distribution of electrical conductivity. Thus, data collected using such sensors are more adapted to an approximate forward-modeling with a conductivity-independent kernel. We have evaluated the bias of such an approximate modeling for the case of portable multiconfiguration system measurements in 1D, 2D, and 3D contexts. Our result shows that the approximate approach tends to underestimate the conductivity of more conductive targets but is able to reproduce the right structural information. Compared with previous algorithms presented in the literature, we solved the approximate forward-modeling problem in the hybrid spectral-spatial domain to speed up the computation. Considering the level of accuracy in structural modeling as well as the computational efficiency of our hybrid spectral-spatial approach, we conclude that this method is especially suitable for near-surface, large-scale mapping applications in LIN environments as typically encountered in soil sciences and archaeological studies. For such applications, our approach can be implemented in rapid multichannel deconvolution procedures.

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