In hard-rock aquifers, fractured zones constitute adequate drinking water exploitation areas but also potential contamination paths. One critical issue in hydrogeological research is to identify, characterize, and monitor such fractured zones at a representative scale. A tracer test monitored with surface electrical resistivity tomography (ERT) could help by delineating such preferential flow paths and estimating dynamic properties of the aquifer. However, multiple challenges exist including the lower resolution of surface ERT compared with crosshole ERT, the finite time that is needed to complete an entire data acquisition, and the strong dilution effects. We conducted a natural gradient salt tracer test in fractured limestones. To account for the high transport velocity, we injected the salt tracer continuously for four hours at a depth of 18 m. We monitored its propagation with two parallel ERT profiles perpendicular to the groundwater flow direction. Concerning the data acquisition, we always focused on data quality over temporal resolution. We performed the experiment twice to prove its reproducibility by increasing the salt concentration in the injected solution (from 38 to [Formula: see text]). Our research focused on how we faced every challenge to delineate a preferential flow and solute transport path in a typical calcareous valley of southern Belgium and on the estimation of the transport velocity (more than [Formula: see text]). In this complex environment, we imaged a clear tracer arrival in both ERT profiles and for both tests. Applying filters (with a cutoff on the relative sensitivity matrix and on the background-resistivity changes) was helpful to isolate the preferential flow path from artifacts. Regarding our findings, our approach could be improved to perform a more quantitative experiment. With a higher temporal resolution, the estimated value of the transport velocity could be narrowed, allowing estimation of the percentage of tracer recovery.
To date, few studies offer a quantitative comparison of the performance of image appraisal tools. Moreover, there is no commonly accepted methodology to handle them even though it is a crucial aspect for reliable interpretation of geophysical images. In this study, we compare quantitatively different image appraisal indicators to detect artefacts, estimate depth of investigation, address parameters resolution and appraise ERT‐derived geometry. Among existing image appraisal tools, we focus on the model resolution matrix (bold-italicR), the cumulative sensitivity matrix (bold-italicS) and the depth of investigation index (bold-italicDbold-italicObold-italicI) that are regularly used in the literature. They are first compared with numerical models representing different geological situations in terms of heterogeneity and scale and then used on field data sets. The numerical benchmark shows that indicators based on bold-italicR and bold-italicS are the most appropriate to appraise ERT images in terms of the exactitude of inverted parameters, bold-italicDbold-italicObold-italicI providing mainly qualitative information. In parallel, we test two different edge detection algorithms – Watershed’s and Canny’s algorithms – on the numerical models to identify the geometry of electrical structures in ERT images. From the results obtained, Canny’s algorithm seems to be the most reliable to help practitioners in the interpretation of buried structures. On this basis, we propose a methodology to appraise field ERT images. First, numerical benchmark models representing simplified cases of field ERT images are built using available a priori information. Then, ERT images are produced for these benchmark models (all simulated acquisition and inversion parameters being the same). The comparison between the numerical benchmark models and their corresponding ERT images gives the errors on inverted parameters. These discrepancies are then evaluated against the appraisal indicators (bold-italicR and bold-italicS) allowing the definition of threshold values. The final step consists in applying the threshold values on the field ERT images and to validate the results with a posteriori knowledge. The developed approach is tested successfully on two field data sets providing important information on the reliability of the location of a contamination source and on the geometry of a fractured zone. However, quantitative use of these indicators remains a difficult task depending mainly on the confidence level desired by the user. Further research is thus needed to develop new appraisal indicators more suited for a quantitative use and to improve the quality of inversion itself.
Many geophysical inverse problems are ill-posed and their solution non-unique. It is thus important to reduce the amount of mathematical solutions to more geologically plausible models by regularizing the inverse problem and incorporating all available prior information in the inversion process. We compare three different ways to incorporate prior information for electrical resistivity tomography (ERT): using a simple reference model or adding structural constraints to Occam's inversion and using geostatistical constraints. We made the comparison on four real cases representing different field applications in terms of scales of investigation and level of heterogeneities. In those cases, when electromagnetic logging data are available in boreholes to control the solution, it appears that incorporating prior information clearly improves the correspondence with logging data compared to the standard smoothness constraint. However, the way to incorporate it may have a major impact on the solution. A reference model can often be used to constrain the inversion; however, it can lead to misinterpretation if its weight is too strong or the resistivity values inappropriate. When the computation of the vertical and/or horizontal correlation length is possible, the geostatistical inversion gives reliable results everywhere in the section. However, adding geostatistical constraints can be difficult when there is not enough data to compute correlation lengths.When a known limit between two layers exists, the use of structural constraint seems to be more indicated particularly when the limit is located in zones of low sensitivity for ERT. This work should help interpreters to include their prior information directly into the inversion process through an appropriate way.
Adequate management of contaminated sites requires information with improved spatio-temporal resolution, in particular to assess bio-geochemical processes, such as the transformation and degradation of contaminants, precipitation of minerals or changes in groundwater geochemistry occurring during and after remediation procedures. Electrical Resistivity Tomography (ERT), a geophysical method sensitive to pore-fluid and pore-geometry properties, permits to gain quasi-continuous information about subsurface properties in real-time and has been consequently widely used for the characterization of hydrocarbon-impacted sediments. However, its application for the long-term monitoring of processes accompanying natural or engineered bioremediation is still difficult due to the poor understanding of the role that biogeochemical processes play in the electrical signatures. For in-situ studies, the task is further complicated by the variable signal-to-noise ratio and the variations of environmental parameters leading to resolution changes in the electrical images. In this work, we present ERT imaging results for data collected over a period of two years on a site affected by a diesel fuel contamination and undergoing bioremediation. We report low electrical resistivity anomalies in areas associated to the highest contaminant concentrations likely due transformations of the contaminant due to microbial activity and accompanying release of metabolic products. We also report large seasonal variations of the bulk electrical resistivity in the contaminated areas in correlation with temperature and groundwater level fluctuations. However, the amplitude of bulk electrical resistivity variations largely exceeds the amplitude expected given existing petrophysical models. Our results suggest that the variations in electrical properties are mainly controlled by microbial activity which in turn depends on soil temperature and hydrogeological conditions. Therefore, ERT can be suggested as a promising tool to track microbial activity during bioremediation even though further research is still needed to completely understand the bio-geochemical processes involved and their impact on electrical signatures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.