Groundwater is an important source of drinking water in coastal regions with predominantly unconsolidated sediments. To protect and manage drinking water extraction wells in these regions, reliable estimates of groundwater flow velocities around well fields are of paramount importance. Such measurements help to identify the dynamics of the groundwater flow and its response to stresses, to optimize water resources management, and to calibrate groundwater flow models. In this article, we review approaches for measuring the relatively high groundwater flow velocity measurements near these wells. We discuss and review their potential and limitations for use in this environment. Environmental tracer measurements are found to be useful for regional scale estimates of groundwater flow velocities and directions, but their use is limited near drinking water extraction wells. Surface-based hydrogeophysical measurements can potentially provide insight into groundwater flow velocity patterns, although the depth is limited in large-scale measurement setups. Active-heating distributed temperature sensing (AH-DTS) provides direct measurements of in situ groundwater flow velocities and can monitor fluctuations in the high groundwater flow velocities near drinking water extraction wells. Combining geoelectrical measurements with AH-DTS shows the potential to estimate a 3D groundwater flow velocity distribution to fully identify groundwater flow towards drinking water extraction wells.

Tumuli, ancient burial mounds, stand as intriguing archaeological features, offering valuable insights into past cultures and burial practices. This paper explores the significance of tumuli inspection and utilizes electrical resistivity tomography (ERT) as a noninvasive and powerful tool for inspecting these enigmatic structures, using a nonconventional array. Tumuli, spanning various shapes and sizes, serve as repositories of cultural and funerary traditions, and understanding their internal composition is crucial for unravelling historical narratives. ERT has emerged as a promising geophysical method for investigating subsurface structures, including tumuli. By imaging the electrical resistivity of the ground, ERT enables archaeologists to map variations in soil composition and identify buried features without excavation. This paper reviews the principles of ERT and its application in tumulus studies, showcasing a case study where ERT has successfully revealed internal structures, burial chambers and associated artefacts. The use of 2D ERT is common in tumuli inspection, ignoring accurate 3D effects from the topography. Here we highlight the benefits of the 3D inversion, while we provide a different way to measure which is cost efficient and provides increased spatial resolution to the area of interest. The integration of 3D ERT into archaeological investigations not only enhances our understanding of tumuli construction but also preserves these cultural heritage sites by minimizing the need for invasive excavation. This research contributes to the evolving methodologies in archaeology, emphasizing the synergy between modern technology and traditional archaeological inquiry to uncover the secrets held within tumuli.

Hosting over 10% of the world’s population, low elevation (<10 m above mean sea-level) coastal zones are susceptible to saline groundwater intrusion—making fresh groundwater an often scarce and threatened commodity. To inform suitable coastal groundwater management strategies, regional-scale mapping of fresh and saline groundwater occurrence is extremely beneficial. This mapping is usually based on conventional ground-based methods. However, these are not only slow and expensive, but result in localized and disconnected information which is uneconomical and impractical on the larg scales required. Airborne electromagnetic (AEM) surveys have been proven a frugal and rapid way to overcome these shortcomings. Consequently, AEM methods are increasingly being used globally. Little is known about the effects of flightline spacing and additional ground-based data on the quality of mapping results, and in general the accuracy of AEM, other than validation against often sparse ground measurements. Understanding this is therefore invaluable as input to groundwater management strategies, survey planning and decision making. Here, we use a regional scale (900 km²), high-resolution (50 m × 50 m × 0.5 m) 3D synthetic model of electrical conductivity and geological properties, to investigate the effects of data availability on the accuracy of regional-scale groundwater salinity mapping. This was undertaken by simulating commonly used AEM parameters and realistic data acquisition methods. Two key data components are considered: (1) the AEM survey itself, and (2) geological information used to convert the AEM results into groundwater salinity. Spatially, different data-densities of these two components are quantitatively compared to highlight ideal geometrical configurations for given accuracy requirements. Our results indicate that in terms of optimising costs versus benefits, the value of additional lithological information is dependent on how well the initial distribution of electrical conductivity is resolved by the acquisition and inversion process.

In the Interreg-funded Polder2C’s project, large-scale overflow experiments were conducted from 2020 to 2022 on levee slopes along the Scheldt River in Belgium and the Netherlands. These tests assessed surface erosion resistance under varied conditions, including levee sections containing animal-induced damages. Electrical Resistivity Tomography (ERT) was employed as a non-invasive monitoring tool to observe subsurface changes, particularly those linked to erosion-prone animal burrows. A unique system configuration enabled detailed imaging of the levee’s internal dynamics during overflow testing. Post-processed ERT data effectively captured subsurface changes during these events, including water infiltration into existing burrows, cavity formation and collapse, and the interconnection of subsurface voids. The study demonstrates ERT’s ability to identify critical subsurface features, with low resistivity zones indicating water-saturated areas and high resistivity zones marking air-filled voids. Time-lapse ERT imaging successfully captured dynamic resistivity shifts, correlating with key processes like soil displacement around burrows. Despite potential limitations, such as environmental noise and the influence of synthetic road plates used as protective coverings, ERT proved effective in detecting internal erosion patterns and pre-existing structural weaknesses. The results indicate that ERT offers a feasible, scalable approach, also for real-time levee monitoring in overflow scenarios, enhancing its applicability for validation of erosion models. Future studies should investigate the effect of cumulative damage during overflow testing and optimize forms of data presentation to improve interpretability, ultimately refining ERT’s potential as a reliable tool for predicting levee vulnerabilities.

In this work, we explore the optimization of 3D Electrical Resistivity Tomography (ERT) measurement protocols for a 3D borehole grid configuration. Currently, there is no widely accepted standard measurement scheme for such setups. The use of numerous electrodes and the possibility of cross-borehole configurations lead to an extremely large number of potential electrode combinations. However, not all these combinations contribute significantly to the final resistivity model, and a complete measurement cycle requires substantial time to perform. This becomes particularly problematic in dynamic subsurface conditions, where changes may occur during data acquisition. In such cases, the measurements collected within a single cycle may reflect different subsurface states. Conversely, attempting to shorten acquisition time can result in too few measurements to resolve the subsurface structure at high resolution. Furthermore, most existing approaches assume a uniform half-space model and treat all measurements equally, failing to prioritize those that are most sensitive to actual subsurface changes. To address these challenges, we propose a 3D measurement optimization approach that yields an efficient acquisition scheme. This method produces inversion results comparable to those obtained from much larger datasets while reducing both measurement and processing requirements. Our optimization is based on a sensitivity-driven selection algorithm that accounts for the real subsurface structure rather than assuming a generic half-space. The proposed methodology is validated using synthetic data and tested with experimental data obtained from a laboratory tank setup. These experimental measurements were used to monitor permeation grouting; a technique applied to reduce permeability and/or increase the strength of granular soils through targeted injection.

Defining subsurface soil conditions in complex coastal settings requires the use of both geophysical and geotechnical datasets, each with different resolution and sensitivity. This study combined helicopter-borne electromagnetic (HEM) data, where large areas are spatially covered with limitations to vertical resolution, with cone penetration test (CPT) data, where high resolution can be achieved while the spatial resolution often is very sparse due to drilling associated costs. Τo formulate a continuous three-dimensional model of subsurface soil properties for levee risk assessment, these datasets were integrated. HEM data provides extensive covering resistivity profiles, while CPT provides high resolution, spatially limited measurements of mechanical soil behaviour.It is known that resistivity as a soil property depends on many parameters (mostly water quality and soil type), and there is no straightforward method to directly translate it to soil, hence the use of ML. To deal with these complexities, we employed machine learning methods – Random Forests and neural networks – to merge heterogeneous datasets and predict continuous soil behaviour indices and discrete lithological types. We propose the use of multiple features, such as spatial coordinates, depths, distance from coast, soil types and local geological conditions. After pre-processing, machine-learning models were trained to fuse the datasets to ensure spatial consistency in the coastal environment. Afterwards, the Soil Behaviour Type Index (SBT) (Robertson, 1990) was calculated using the CPT measurements and then was discretized into lithological units.A classical machine learning algorithm (Random Forest) and a PyTorch-based neural network were trained for regression (predicting the continuous SBT index) and classification (predicting soil types) tasks, and their performance was evaluated using standard statistical and visual metrics. Final models were retrained on the full dataset to increase generalizability and robustness. The final product is to map 𝐼𝑐 values and lithological classes at every HEM point and ultimately to make a 3D subsurface soil model. The outcome for each process was validated against an 80%-20% test to ensure reasonable results.While regression models had similar RMSE scores, classification models generally produced models with greater accuracy of dominant soil types but captured fewer underrepresented mixed lithologies. This work focuses on the interpretability of soil models through integrating data (i.e., not just purely statistical but spatial output) and ultimately continuity in the spatial domain (where engineers are most concerned). The goal of this study is to develop a framework where continuous geophysical data, collected either by helicopters or drones can be combined with additional geological boreholes and CPTs and other geotechnical information, to enable us to image the subsurface beyond resistivity. One of the products of this study serves to represent an approach to providing a better product to those grappling with levee design and safety.