Subsurface electrical conductivity models from frequency-domain electromagnetic (FDEM) induction measurements are often derived using computationally efficient one-dimensional piecewise inversion (PWI) approaches. However, PWI does not account for lateral conductivity variations or the measurement overlap between adjacent soundings, which can limit model estimation accuracy. Laterally constrained inversion (LCI) introduces smoothness constraints to reduce lateral variability between neighbouring models, potentially improving continuity. In this study, both PWI and LCI use a 1D forward function, assuming a horizontally layered earth, and a horizontally laying rigid boom instrument, to perform the estimations This study presents a detailed analysis of how various 2.5D and 3D conductivity distributions, including topographic variations and instrument pitch angle, affect FDEM measurements. We examine how these measurement distortions propagate into PWI and LCI inversion results. Under ideal conditions, such as flat terrain, no instrument tilt, and simple two-layer models, both methods recover accurate conductivity structures, with LCI offering little advantage in accuracy. When topography is introduced, however, distortions occur even at slopes as small as 2°, and both methods show degraded performance, particularly in 3D scenarios. In the field example, LCI produces smoother and more stable results than PWI in the presence of noise, but its assumption of lateral smoothness can be restrictive in geologically complex settings. Our findings show that both inversion strategies are sensitive to topographic and 3D effects, and that error propagation significantly influences inversion reliability. These results highlight the need for improved methodologies capable of handling realistic acquisition conditions and measurement uncertainties in FDEM surveys.

The impacts of climate change, combined with population growth, necessitate practical and effective solutions for locating groundwater resources and ensuring drinking water quality. Our contribution explores recent advances in geoelectrical and electromagnetic imaging methods applied to investigate groundwater systems. Geoelectrical and electromagnetic imaging techniques are popular methods for characterising subsurface properties, such as electrical resistivity or dielectric permittivity. These electrical properties are strongly related to the hydrogeological characteristics of the subsurface. Therefore, geoelectrical and electromagnetic investigations can provide valuable insights into finding groundwater resources, assessing the water quality in terms of contaminations and conducting effective groundwater management.

Our study examines state-of-the-art approaches in modelling and instrumentation of induced polarisation and electrical resistivity tomography, as well as time- and frequency-domain electromagnetics and ground-penetrating radar methods. We review recent impactful and innovative groundwater case studies where the above-mentioned methods were applied and further developed. Emphasising the combination of geoelectrical and electromagnetic methods, the studies provide insights into the variation of electrical subsurface properties at different scales, contributing to an improved understanding of the hydrological dynamics in the studied areas. Furthermore, we provide an outlook on the potential for applying geoelectrical and electromagnetic imaging techniques for large-scale groundwater investigations in the exascale computing area.

In agriculture, there is a demand for new methods to monitor the dynamics of fresh rainwater lenses overlaying on saline seeping groundwater. For this purpose, integrating different geoelectrical measurements is a non-invasive and low-cost approach to obtaining subsurface information. Geoelectric methods such as electromagnetic induction (EMI) and electrical resistivity tomography (ERT) have proven effective in characterizing subsoil electrical properties, which can be correlated to petrophysical properties such as fluid salinity. These methods have different sensitivities and can provide complementary information about the electrical conductivity and geometry of the subsurface. This study explores the effectiveness of a methodology that combines EMI measurements with laterally constrained inversion as prior information for ERT inversion. We investigate the usefulness of the method using synthetic data and data from a coastal Dutch polder system. The findings are promising, demonstrating improved delineation of changes in electrical conductivity, potentially linked with salinity fluctuations in the subsoil. This methodology proves effective in mapping in-depth variations in electrical conductivity. It could facilitate the impact assessment of level-controlled drainage systems on augmenting shallow rainwater lenses and mitigating salinization in Dutch polders.

Electromagnetic induction measurements from multi-coil configuration instruments are used to obtain information about the electrical conductivity distribution in the subsurface. The resulting inverse problem might not have a unique and stable solution. In that case, a local inversion method can be trapped in a local minimum and lead to an incorrect solution. In this study, we evaluate the well-posedness of the inverse problem for two and three-layered electrical conductivity models. We show that for a two-layered model, uniqueness is ensured only when both in-phase and quadrature data are available from the measurements. Results from a Gauss–Newton inversion and a lookup table demonstrate that the solution space is convex. Furthermore, we demonstrate that for even a simple three-layered model, the data contained in such measurements are insufficient to reach a correct or stable solution. For models with more than 2 layers, independent prior information is necessary to solve the inverse problem. The insights from the numerical examples are applied in a field case.

Rigid boom electromagnetic surveys that use coil-coil configurations are often used to obtain information about the subsurface conductivity. Semi-analytic solutions help to simulate electromagnetic induction measurements for a large number of horizontally layered models, which can then be stored and used as a lookup table. This procedure is performed once and then used to find the corresponding model that produces the best data fit, eliminating the need for running numerous simulations in every minimization step of an inversion scheme for large field datasets. We apply this methodology to a numerical example and field data acquired in The Netherlands. Our results from both cases using the global search demonstrate its ability to estimate electrical conductivity distributions in two-layered models in a fast and accurate manner. Furthermore, we apply the workflow using a lookup table based on low induction number approximation-derived measurements. The outcome of implementing this methodology using the low induction number lookup table shows poor accuracy in the electrical conductivity estimations for both the numerical example and the field data in comparison to the semi-analytical approach.