Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection

Managing the remediation strategy of contaminated megasites using field-scale calibration of geo-electrical imaging with chemical monitoring

Groundwater contamination is a threat to drinking water resources and ecosystems. Remediation by injection of chemical reagents into the aquifer may be preferred to excavation to reduce cost and environmental footprint. Yet, successful remediation requires complete contact between contamination and reagents. Subsurface heterogeneities are often responsible for diffusion into low-permeable zones, which may inhibit this contact. Monitoring the spatial distribution of injected reagents over time is crucial to achieve complete interaction. Source zone contamination at megasites is particularly challenging to remediate and monitor due to the massive scale and mixture of contaminants. Source zone remediation at Kærgård Plantation megasite (Denmark) is monitored here, with a new methodology, using high-resolution cross-borehole electrical resistivity tomography (XB-ERT) imaging calibrated by chemical analyses on groundwater samples. At this site, high levels of toxic non-aqueous phase liquids (NAPL) are targeted by in-situ chemical oxidation using activated persulfate. It may take numerous injection points with extensive injection campaigns to distribute reagents, which requires an understanding of how reagent may transport within the aquifer. A geophysical (XB-ERT) monitoring network of unprecedented size was installed to identify untreated zones and help manage the remediation strategy. The combination of spatially continuous geophysical information with discrete but precise chemical information, allowed detailed monitoring of sulfate distribution, produced during persulfate activation. Untreated zones identified in the first remediation campaign were resolved in the second campaign. The monitoring allowed adjusting the number of injection screens and the injection strategy from one campaign to the next, which resulted in better persulfate distribution and contaminant degradation in the second campaign. Furthermore, geophysical transects repeated over the timespan of a remediation campaign allowed high-resolution time-lapse imaging of reagent transport, which could in the future improve the predictability of transport models, compared to only using on a-priori assumptions of the hydraulic conductivity field.

Detecting soil water redistribution in subsurface drip irrigated processing tomatoes using electrical resistivity tomography, proximal sensing and hydrological modelling

In this study, multiple soil-plant-atmosphere continuum (SPAC) monitoring methodologies, including electrical resistivity tomography (ERT), proximal thermal sensing techniques, and micrometeorological data, were combined with two-dimensional (2-D) soil hydrological modelling using HYDRUS 2-D to explore the soil water redistribution, and infer the relative crop water status in a subsurface drip irrigated (SDI) processing tomato field located in California (Yolo County, USA). Specifically, time-lapse ERT surveys were performed at two transects distributed parallel and perpendicular, respectively, to the SDI line, during an irrigation event. The ERT results were compared to HYDRUS 2-D outputs and the relative differences were explained in the form of local heterogeneities in electrical resistivity (ER) changes, as a proxy for soil water content (SWC) variations. Concurrent simultaneous soil wetting and root water uptake during the last irrigation event of the season caused negligible changes in ER in the active root zone. Slight differences in ER were observed in the top 20 cm along the dripline, confirming that the emitter spacing is small enough to create a wetted strip along the processing tomato bed. These changes were also compared to SWC values measured with time domain reflectometry soil moisture sensors. A comparison between HYDRUS 2-D and ERT confirmed negligible changes in ER during irrigation due to simultaneous wetting and root water uptake processes. In addition, a good correlation was observed between the proximal sensed and the ERT results. Finally, the findings of this study underscore the necessity of using multiple methods for improving our knowledge of the SPAC system under real field conditions.

An integrated modelling framework for multiple pollution source identification in surface water

Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed to achieve rapid, accurate and reliable pollution source identification. In this study, the hydrodynamics and water quality processes in a straight lab-based flume were simulated to test pollution source identification under steady flow conditions. Additionally, the pollution source identification in the unsteady flow conditions was examined using a real-life estuary, specifically the Yangtze River estuary. First, we developed two process-based models to simulate hydrodynamics and water quality in the flume and estuary. Then, the data generated from the process-based models were used to develop surrogate models. Three typical artificial neural networks (ANNs) algorithms: backpropagation (BP), radial basis function (RBF) and general regression neural networks (GRNN) were selected to develop surrogates for process-based models (PBMs), and they were coupled with PSO algorithm to achieve the hybrid modelling framework for pollution source identification. Our results showed that hybrid PBM-ANNs-PSO models could be applied to identify the pollution source and quantify release intensity in spatial distribution when the discharge type was assumed as the point source with a continuous release. Multiple-performance criteria metrics, in terms of the coefficient of determination, root-mean-square error, mean absolute error, evaluated the model performance as "Excellent prediction". The BP-PSO models consistently appear to be the top-performing source identification model within the developed models, with most cases of relative error (RE) values lower than 5%. The new insights from the hybrid modelling framework would provide useful information for the local government agency to make reasonable decisions regarding pollution source identification issues.

LDI-MVFNet: A Multi-view fusion deep network for leachate distribution imaging

The accurate monitoring and early warning of groundwater pollution caused by the concealed leakage of landfills is a major challenge globally in the field of solid waste management and groundwater protection. Electrical resistivity tomography (ERT) represents a potential solution with advantages, owing to its fast and nondestructive characteristics. However, traditional ERT based on a single array cannot reveal the distribution and dynamics of pollution in complex underground media owing to the limited information it carries. We designed a novel deep network for multi-view fusion to invert the real resistivity distribution of the medium caused by leachate (named LDI-MVFNet) so as to infer the distribution of leachate. To support model establishment and validation, ERT instances collected from synthetic models and a salt tracer experiment were inverted. We compared the inversion results of the LDI-MVFNet with those of single arrays and found that the LDI-MVFNet performed the best overall. The average root mean square error (RMSE) of synthetic models reached 0.98, performing better than Dipole-Dipole (3.86), Wenner-Schlumberger (3.37), and Pole-Pole (6.61), which were inverted separately. The resultant inverted subsurface true resistivity data were presented in the form of two-dimensional (2D) cross sections. The imaging results of 2D cross sections showed that LDI-MVFNet was superior to others in data noise suppression and inversion accuracy. The results of this study indicate that the data fusion of multiple views can more accurately reflect the real resistivity than the inversion of a single array can.

Time-Lapse Electrical Resistivity Tomography (TL-ERT) for Landslide Monitoring: Recent Advances and Future Directions

To date, there is a growing interest for challenging applications of time-lapse electrical resistivity tomography (TL-ERT) in Earth sciences. Tomographic algorithms for resistivity data inversion and innovative technologies for sensor networks have rapidly transformed the TL-ERT method in a powerful tool for the geophysical time-lapse imaging. In this paper, we focus our attention on the application of this method in landslide monitoring. Firstly, an overview of recent methodological advances in TL-ERT data processing and inversion is presented. In a second step, a critical analysis of the main results obtained in different field experiments and lab-scale simulations are discussed. The TL-ERT appears to be a robust and cost-effective method for mapping the water-saturated zones, and for the identification of the groundwater preferential pathways in landslide bodies. Furthermore, it can make a valuable contribution to following time-dependent changes in top-soil moisture, and the spatio-temporal dynamics of wetting fronts during extreme rainfall events. The critical review emphasizes the limits and the advantages of this geophysical method and discloses a way to identify future research activities to improve the use of the TL-ERT method in landslide monitoring.