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See detailAssessment of short-term aquifer thermal energy storage for demand-side management perspectives: Experimental and numerical developments
De Schepper, Guillaume; Paulus, Claire; Bolly, Pierre-Yves et al

in Applied Energy (2019), 242

In the context of demand-side management and geothermal energy production, our proposal is to store thermal energy in shallow alluvial aquifers at shorter frequencies than classical seasonal aquifer ... [more ▼]

In the context of demand-side management and geothermal energy production, our proposal is to store thermal energy in shallow alluvial aquifers at shorter frequencies than classical seasonal aquifer thermal energy storage. We first conducted a one-week experiment in a shallow alluvial aquifer, which is characterized by a slow ambient groundwater flow, to assess its potential for thermal energy storage and recovery. This experiment has shown that up to 90% of the stored thermal energy can be recovered and would therefore suggest that aquifer thermal energy storage could be considered for demand-side management applications. We then conceptualized, developed, and calibrated a deterministic 3D groundwater flow and heat transport numerical model representing our study site, and we simulated 77 different scenarios to further assess this potential. This has allowed us to demonstrate that low-temperature aquifer thermal energy storage (temperature differences of −4 K for precooling and 3, 6, and 11 K for preheating) is efficient with energy recovery rates ranging from 78 to 87%, in a single aquifer thermal energy storage cycle. High-temperature aquifer thermal energy storage (temperature differences between 35 and 65 K) presents lower energy recovery rates, from 53 to 71%, with all other parameters remaining equals. Energy recovery rates decrease with increasing storage duration and this decrease is faster for higher temperatures. Retrieving directly useful heat (without upgrading with a groundwater heat pump) using only a single storage and recovery cycle appears to be complicated. Nevertheless, there is room for aquifer thermal energy storage optimization in space and time with regard to improving both the energy recovery rates and the recovered absolute temperatures. © 2019 Elsevier Ltd [less ▲]

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See detail4D electrical resistivity tomography (ERT) for aquifer thermal energy storage monitoring
Lesparre, Nolwenn; Robert, Tanguy ULiege; Nguyen, Frédéric ULiege et al

in Geothermics (2019), 77

In the context of aquifer thermal energy storage, we conducted a hydrogeophysical experiment emulating the functioning of a groundwater heat pump for heat storage into an aquifer. This experiment allowed ... [more ▼]

In the context of aquifer thermal energy storage, we conducted a hydrogeophysical experiment emulating the functioning of a groundwater heat pump for heat storage into an aquifer. This experiment allowed the assessment of surface electrical resistivity tomography (ERT) ability to monitor the 3D development over time of the aquifer thermally affected zone. The resistivity images were converted into temperature. The images reliability was evaluated using synthetic tests and the temperature estimates were compared to direct temperature measurements. Results showed the capacity of surface ERT to characterize the thermal plume and to reveal the spatial variability of the aquifer hydraulic properties, not captured from borehole measurements. A simulation of the experiment was also performed using a groundwater flow and heat transport model calibrated with a larger set-up. Comparisons of the simulation with measurements highlighted the presence of smaller heterogeneities that strongly influenced the groundwater flow and heat transport. [less ▲]

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See detailTowards a subsurface predictive-model environment to simulate aquifer thermal energy storage for demand-side management applications
Robert, Tanguy ULiege; Hermans, Thomas ULiege; Lesparre, Nolwenn et al

in Proceedings of SSB 2018, 10th International Conference on System Simulation in Buildings (2018, December 12)

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See detailCan short-term hydrogeological experiments predict the long-term behavior of subsurface reservoirs? An example from shallow geothermy
Hermans, Thomas ULiege; Nguyen, Frédéric ULiege; Robert, Tanguy ULiege et al

Conference (2018, June 01)

Aquifer thermal energy storage (ATES) systems can potentially store and recover thermal energy seasonally. In practice, the increase of energy efficiency is often lower than expected from simulations due ... [more ▼]

Aquifer thermal energy storage (ATES) systems can potentially store and recover thermal energy seasonally. In practice, the increase of energy efficiency is often lower than expected from simulations due to spatial heterogeneity or non-favorable conditions. In many cases, the lack of available data leads the modeler to consider homogeneous layered conceptual models to forecast the long-term behavior of geothermal systems. Ignoring spatial heterogeneity bears the risk of misleading decisions based on the prediction of those models. The proper design of ATES systems should always consider the uncertainty about subsurface parameters. In practice, classical hydrogeological tests including geophysical surveys are performed to gain knowledge on subsurface parameters. One question remains: are these tests sufficiently informative to predict with realistic uncertainty the long-term behavior of reservoirs? We investigate how short-term heat tracing and storage experiments can predict the long-term behavior of ATES system. We combine field experiments with a probabilistic modeling approach called Bayesian Evidential Learning (BEL) to assess the information content of our data set(s). BEL relies on a set of surrogate models of the subsurface representing prior uncertainty. It uses a global sensitivity analysis to identify sensitive parameters for long-term heat storage and short-term experimental data and can validate the use of short-term experiments to generate informative data sets. In addition, this approach allows a direct estimate of the uncertainty range of the prediction from the observed experimental data, without explicit inverse modeling. The methodology therefore allows us to tests experimental hypothesis that can be further validated with field data. Here, we use the approach to compare the information content of different data acquisition schemes: tracing vs. storage/push-pull experiments, standard vs. multi-cycle experiments, very-short vs. long experiments, single-hole vs. multi-borehole and geophysical measurements. Finally, we illustrate and validate the proposed framework with field data. [less ▲]

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See detailUse and challenges of geophysics to study processes in agro-ecosystems (invited)
Garré, Sarah ULiege; Javaux, Mathieu; Dumont, Gaël ULiege et al

Conference (2018, April 05)

Electrical resistivity tomography (ERT) is increasingly used in the context of agriculture since the measured resistivity distribution can be linked to soil moisture, soil structure or pore water salinity ... [more ▼]

Electrical resistivity tomography (ERT) is increasingly used in the context of agriculture since the measured resistivity distribution can be linked to soil moisture, soil structure or pore water salinity. Due to its minimally invasive character, its spatial coverage and its monitoring abilities, ERT can be used to study field heterogeneity and competition between plants, quantify water fluxes throughout a growing season or distinguish preferential flow pathways in soils. Nevertheless, a lot of challenges still remain. From a mathematical point of view, the inverse problem linked to ERT is ill-posed. To solve it, the inverse problem is often regularized with a Tikhonov-type approach resulting in a smoothed resistivity distribution. However, in reality strong contrasts can exist due to e.g. compacted soil layers due to ploughing, water infiltration fronts, etc. and in that case other operators have been proposed to regularize the inversion. Taking into account spatial heterogeneity of petrophysical characteristics and providing a realistic uncertainty estimation are additional challenges, which can be addressed using stochastic approaches. Monitoring data provides further elements to constrain the inverse problem: data can be replaced by data difference and regularization may incorporate the temporal dimension for instance. However, such constraints require their compatibility with the studied temporal process, which is not always straightforward. Several alternative strategies are being developed, such as coupled hydrogeophysical inversion, or stochastic approaches using a prior falsification/validation method following a Popper-Bayes philosophy. In this presentation, we will illustrate the mentioned challenges and some recent developments in the context of agrogeophysics. [less ▲]

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See detailUncertainty quantification of medium‐term heat storage from short‐term geophysical experiments using bayesian evidential learning
Hermans, Thomas ULiege; Nguyen, Frédéric ULiege; Klepikova, Maria et al

in Water Resources Research (2018), 54

In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy ... [more ▼]

In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy efficiency is often lower than expected from simulations due to spatial heterogeneity of hydraulic properties or non‐favorable hydrogeological conditions. A proper design of ATES systems should therefore consider the uncertainty of the prediction related to those parameters. We use a novel framework called Bayesian Evidential Learning (BEL) to estimate the heat storage capacity of an alluvial aquifer using a heat tracing experiment. BEL is based on two main stages: pre‐ and postfield data acquisition. Before data acquisition, Monte Carlo simulations and global sensitivity analysis are used to assess the information content of the data to reduce the uncertainty of the prediction. After data acquisition, prior falsification and machine learning based on the same Monte Carlo are used to directly assess uncertainty on key prediction variables from observations. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data, without any explicit full model inversion. We demonstrate the methodology in field conditions and validate the framework using independent measurements. [less ▲]

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See detailThe mathematical challenges in agrogeophysics: examples and ways ahead
Garré, Sarah ULiege; Nguyen, Frédéric ULiege; Lesparre, Nolwenn ULiege et al

Conference (2017, October 05)

Electrical resistivity tomography (ERT) is increasingly used in the context of agriculture since the measured resistivity distribution can be linked to soil moisture, soil structure or pore water salinity ... [more ▼]

Electrical resistivity tomography (ERT) is increasingly used in the context of agriculture since the measured resistivity distribution can be linked to soil moisture, soil structure or pore water salinity. Due to its minimally invasive character, its spatial coverage and its monitoring abilities, ERT can be used to study field heterogeneity and competition between plants, quantify water fluxes throughout a growing season or distinguish preferential flow pathways in soils. Nevertheless, a lot of challenges still remain. From a mathematical point of view, the inverse problem linked to ERT is ill-posed. To solve it, the inverse problem is often regularized with a Tikhonov-type approach. The latter is typically done using a gradient operator, resulting in smoothed resistivity distribution. However, strong contrasts can exist due to e.g. compacted soil layers due to ploughing, water infiltration fronts, etc. In such a case, other operators such as the total variation or the minimum gradient support may be used. In such approaches, the selection of the regularization parameter with respect to the data quality and the definition of image appraisal indicators still remains a challenge. Uncertainty quantification of ERT-derived results often relies on data-error propagation around the inverse solution. Given the inherent non-uniqueness of the problem, both mathematically but also from a pedological point of view, challenges for stochastic approaches lie in providing realistic uncertainty estimation, encompassing all uncertainties (e.g. prior, pedophysics or data error). Monitoring data allows further elements to constrain the inverse problems, data can be replaced by data difference and regularization may incorporate the temporal dimension for instance. However, such constraints require their compatibility with the studied temporal process. Whereas the above challenges stay true for monitoring data, several alternative strategies are being developed more specifically, such as coupled hydrogeophysical inversion, with the challenge of addressing the non-stationarity of pedophysical relationships and the accuracy of the conceptual flow and transport model using deterministic approaches. Stochastic approaches allow to a certain extent to tackle those challenges in particular using a prior falsification/validation approach following a Popper-Bayes philosophy. In this presentation, we will illustrate the challenges and some of the recent developments with numerical and field examples. [less ▲]

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See detailUsing Geophysical Hard Data to Enhance the Reliability of Hydrological Models
De Schepper, Guillaume; Paulus, Claire; Molron, Justine et al

in EarthDoc (2017, September 06)

Appropriate design of geophysical experiments combined with common hydrological measurements offer opportunities to use geophysical data as hard data in hydrological models, regarding their ... [more ▼]

Appropriate design of geophysical experiments combined with common hydrological measurements offer opportunities to use geophysical data as hard data in hydrological models, regarding their conceptualisation or their calibration. Two study sites located in Wallonia, Belgium, were investigated. In the first case (fractured limestone aquifer), streaming potentials were linked to piezometric measurements, allowing us to better conceptualise the local groundwater flow model and calibrate it. In the second example (alluvial sandy aquifer), the use of 4D electrical resistivity tomography and temperature measurements appeared to be a reliable methodology to predict heat storage and recovery cycles in hydrological models with a better constrained calibration process. [less ▲]

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See detailSpatio-temporal Monitoring of Heat Storage in a Shallow Aquifer Using Electrical Resistivity 4D Imagery and DTS
Lesparre, Nolwenn ULiege; Hermans, Thomas ULiege; Nguyen, Frédéric ULiege et al

in EarthDoc (2017, September 06)

The design of groundwater heat pumps requires a good understanding of the aquifer and heat flow conditions. Issues of short-circuit or recycling between cold and hot wells have to be carefully considered ... [more ▼]

The design of groundwater heat pumps requires a good understanding of the aquifer and heat flow conditions. Issues of short-circuit or recycling between cold and hot wells have to be carefully considered. Surface geophysical methods allow monitoring subsurface processes without additional perturbations of the medium. Within available methods, the electrical resistivity imagery (ERI) applied in time-lapse (TL) is appropriate. Here, we monitored with ERI and distributed temperature sensors (DTS) a heat plume propagation during an experiment of hot water injection in a shallow aquifer. DTS and TL ERI measurements acquired from two boreholes provide a local estimate of the heat propagation through the medium. TL ERI were also performed from a grid at surface to follow the 3D plume shape formation and evolution through time. The different complementary data validate the potential of surface TL ERI for monitoring in 3D the behavior of shallow heat plumes. ERI highlight the heterogeneity of the aquifer by distinguishing regions with higher or lower hydraulic conductivity. In the higher hydraulic conductivity zone, the heat might be evacuated through water flow, while in the lower hydraulic conductivity area heat storage is achievable. Thus, in that last region the plume temperature decreases progressively with time. [less ▲]

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See detailQuantification of Temperature with Time-lapse electrical resistivity using the prediction-focused approach-A field case
Hermans, Thomas ULiege; Nguyen, Frédéric ULiege; Caers, Jef

in EarthDoc (2017, September 06)

Standard inversion of time-lapse geophysical suffers from spatially and temporally varying resolution due to the regularization procedure used during the inversion process. In this study, we apply the ... [more ▼]

Standard inversion of time-lapse geophysical suffers from spatially and temporally varying resolution due to the regularization procedure used during the inversion process. In this study, we apply the recently developed prediction-focused approach (PFA) to directly estimate temperature with electrical resistance data, without classic tomographic inversions. PFA is based on a set of prior subsurface models coherent with our prior knowledge of the site. From this set of models, we generate a prior set of temperature distribution and resistance data mimicking the field experiment. Then, we use dimension-reduction techniques to derive a direct relationship between the data and the desired prediction. The use of canonical correlation analysis linearize the relationship and allows using Gaussian regression to sample the posterior. In this paper, we demonstrate the ability of PFA to process time-lapse ERT data during a field experiment. We propose an analysis of time-lapse reciprocals to derive an error model and generate the posterior distribution of temperature. We validate the results using direct measurements in the aquifer. This successful application opens new ways to process and integrate geophysical data in hydrogeological model. [less ▲]

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See detailThe Prediction-Focused Approach: An opportunity for hydrogeophysical data integration and interpretation in the critical zone
Hermans, Thomas ULiege; Nguyen, Frédéric ULiege; Klepikova, Maria et al

Poster (2017, July 27)

Two important challenges remain in hydrogeophysics: the inversion of geophysical data and their integration in quantitative subsurface models. Classical regularized inversion approaches suffer from ... [more ▼]

Two important challenges remain in hydrogeophysics: the inversion of geophysical data and their integration in quantitative subsurface models. Classical regularized inversion approaches suffer from spatially varying resolution and yield geologically unrealistic solutions, making their utilization for model calibration less consistent. Advanced techniques such as coupled inversion allow for a direct integration of geophysical data; but, they are difficult to apply in complex cases and remain computationally demanding to estimate uncertainty. We investigated a prediction-focused approach (PFA) to directly estimate subsurface physical properties relevant in the critical zone from geophysical data, circumventing the need for classic inversions. In PFA, we seek a direct relationship between the data and the subsurface variables we want to predict (the forecast). This relationship is obtained through a prior set of subsurface models for which both data and forecast are computed. A direct relationship can often be derived through dimension reduction techniques (Figure 1). For hydrogeophysical inversion, the considered forecast variable is the subsurface variable, such as the salinity or saturation for example. An ensemble of possible solutions is generated, allowing uncertainty quantification. For data integration, the forecast variable is the prediction we want to make with our subsurface models, such as the concentration of contaminant in a drinking water production well. Geophysical and hydrological data are combined to derive a direct relationship between data and forecast. We illustrate the methodology to predict the energy recovered in an ATES system considering the uncertainty related to spatial heterogeneity. With a global sensitivity analysis, we identify sensitive parameters for heat storage prediction and validate the use of a short term heat tracing experiment to generate informative data. We illustrate how PFA can be used to successfully derive the distribution of temperature in the aquifer from ERT during the heat tracing experiment. Then, we successfully integrate the geophysical data to predict heat storage in the aquifer using PFA. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data in a relatively limited time budget. [less ▲]

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See detailBuilding flow and transport models with electrical resistivity tomography data
Gottschalk, Ian; Hermans, Thomas ULiege; Knight, Rosemary et al

Poster (2017, July 26)

Aquifer recharge and recovery (ARR) is the process of enhancing natural groundwater resources and recovering water for later use by constructing engineered conveyances. Insufficient understanding of ... [more ▼]

Aquifer recharge and recovery (ARR) is the process of enhancing natural groundwater resources and recovering water for later use by constructing engineered conveyances. Insufficient understanding of lithological heterogeneity at ARR sites often hinders attempts to predict where and how quickly infiltrating water will flow in the subsurface, which can adversely affect the quality and quantity of available water in the ARR site. In this study, we explored the use of electrical resistivity tomography (ERT) to assist in characterizing lithological heterogeneity at an ARR site, so as to incorporate it into a flow and contaminant transport model. In this case, we had non-collocated well core log data and ERT data from a full-scale ARR basin. We compared three independent methods for producing conditional lithology-resistivity probability distributions: 1) a search template to relate the nearest logged well lithologies with ERT resistivity panels, given search criteria; 2) a maximum likelihood estimation (MLE) to match bimodal normal distributions to the histogram of each ERT line; and 3) variogram-based lithology indicator simulations constrained to well data. Each approach leverages Bayes’ Rule to estimate lithology probability given electrical resistivity. The simplest approach (method 1) yields an erroneous conditional probability function where sand dominates the conditional probability at nearly all resistivities, due in part to the strong presence of sand in the wells nearest the ERT lines. The approaches using MLE and lithology simulations (methods 2 and 3) produce similar, more realistic lithofacies probability functions. The range of resistivities where clay and sand overlap differs between methods 2 and 3: ranging between 100 and 200 ohm-m for method 2, and between 30 and 50 ohm-m for the method 3. These differences affect the posterior lithology distributions in multiple point geostatistical (MPS) simulations, and in turn, predictions of flow from models which integrate these results. To test the models, we can compare measured breakthrough times of recharged water at the site to groundwater flow simulation results using the lithofacies models created by each method. The methods described here can inform the integration of non-collocated geophysical data into a variety of applications. [less ▲]

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See detailThe effect of initial water distribution and spatial resolution on the interpretation of ERT monitoring of water infiltration
Dumont, Gaël ULiege; Pilawski, Tamara ULiege; Robert, Tanguy ULiege et al

Poster (2017, July 25)

A better understanding of the water balance of a landfill is crucial for its management, as the waste water content is the main factor influencing the biodegradation process of organic waste. In order to ... [more ▼]

A better understanding of the water balance of a landfill is crucial for its management, as the waste water content is the main factor influencing the biodegradation process of organic waste. In order to investigate the ability of long electrical resistivity tomography (ERT) profiles to detect zones of high infiltration in a landfill cover layer, low resolution time lapse data were acquired during a rainfall event. Working at low resolution allows to cover large field areas but with the drawback of limiting quantitative interpretation. In this contribution, we use synthetic modeling to quantify the effect of the following issues commonly encountered when dealing with field scale ERT data: (i) the effect of low resolution on electrical resistivity changes interpretation, (ii) the effect of the original heterogeneous resistivity distribution on the observed relative resistivity changes, (iii) the need for temperature and pore fluid conductivity data in order to compute water content and absolute changes of water content, and (iv) the interpretation error commonly made while neglecting the dilution effect during fresh water infiltration. Firstly, due to the lack of spatial resolution, the regularized inversion process yields a smoothed distribution of resistivity changes that fail to detect small infiltration zones and yields an overestimation of the infiltration depth and an underestimation of the infiltrated volume in large infiltration areas. Secondly, the analysis of relative changes, as commonly used in literature, is not adequate when the background water content is highly heterogeneous. In such a case, relative changes reflect both the initial water content distribution and the observed changes. Thirdly, the computation of absolute water content changes better reflects the infiltration pattern, but requires spatially distributed temperature and pore fluid conductivity input data. Lastly, the dilution effect, if not considered, leads to an underestimation of the infiltrated volume. Taking into account these elements, we extracted the maximum amount of information from our field data without over-interpreting the results. This allowed the detection of larger infiltration areas possibly responsible for a large part of the annual water infiltration and landfill gas loss. [less ▲]

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See detailThe Prediction-Focused Approach: An opportunity for hydrogeophysical data integration and interpretation
Hermans, Thomas ULiege; Nguyen, Frédéric ULiege; Klepikova, Maria et al

Conference (2017, April 28)

Hydrogeophysics is an interdisciplinary field of sciences aiming at a better understanding of subsurface hydrological processes. If geophysical surveys have been successfully used to qualitatively ... [more ▼]

Hydrogeophysics is an interdisciplinary field of sciences aiming at a better understanding of subsurface hydrological processes. If geophysical surveys have been successfully used to qualitatively characterize the subsurface, two important challenges remain for a better quantification of hydrological processes: (1) the inversion of geophysical data and (2) their integration in hydrological subsurface models. The classical inversion approach using regularization suffers from spatially and temporally varying resolution and yields geologically unrealistic solutions without uncertainty quantification, making their utilization for hydrogeological calibration less consistent. More advanced techniques such as coupled inversion allow for a direct use of geophysical data for conditioning groundwater and solute transport model calibration. However, the technique is difficult to apply in complex cases and remains computationally demanding to estimate uncertainty. In a recent study, we investigate a prediction-focused approach (PFA) to directly estimate subsurface physical properties from geophysical data, circumventing the need for classic inversions. In PFA, we seek a direct relationship between the data and the subsurface variables we want to predict (the forecast). This relationship is obtained through a prior set of subsurface models for which both data and forecast are computed. A direct relationship can often be derived through dimension reduction techniques. PFA offers a framework for both hydrogeophysical “inversion” and hydrogeophysical data integration. For hydrogeophysical “inversion”, the considered forecast variable is the subsurface variable, such as the salinity. An ensemble of possible solutions is generated, allowing uncertainty quantification. For hydrogeophysical data integration, the forecast variable becomes the prediction we want to make with our subsurface models, such as the concentration of contaminant in a drinking water production well. Geophysical and hydrological data are combined to derive a direct relationship between data and forecast. We illustrate the process for the design of an aquifer thermal energy storage (ATES) system. An ATES system can theoretically recover in winter the heat stored in the aquifer during summer. In practice, the energy efficiency is often lower than expected due to spatial heterogeneity of hydraulic properties combined to a non-favorable hydrogeological gradient. A proper design of ATES systems should consider the uncertainty of the prediction related to those parameters. With a global sensitivity analysis, we identify sensitive parameters for heat storage prediction and validate the use of a short term heat tracing experiment monitored with geophysics to generate informative data. First, we illustrate how PFA can be used to successfully derive the distribution of temperature in the aquifer from ERT during the heat tracing experiment. Then, we successfully integrate the geophysical data to predict medium-term heat storage in the aquifer using PFA. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data in a relatively limited time budget. [less ▲]

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See detailA new approach for time-lapse data weighting in ERT
Lesparre, Nolwenn ULiege; Nguyen, Frédéric ULiege; Kemna, Andreas et al

in Geophysics (2017), 82(6), 325-333

Applications of timelapse inversion of electrical resistivity tomography (ERT) allows monitoring variations in the subsurface that play a key role in a variety of contexts. The inversion of timelapse data ... [more ▼]

Applications of timelapse inversion of electrical resistivity tomography (ERT) allows monitoring variations in the subsurface that play a key role in a variety of contexts. The inversion of timelapse data provides successive images of the subsurface properties showing the medium evolution. Images quality is highly dependent on the data weighting determined from the data error estimates. However, the quantification of errors in the inversion of timelapse data has not yet been addressed. We propose a methodology for the quantification of timelapse data error based on the analysis of the discrepancy between normal and reciprocal readings acquired at different times. We apply the method to field monitoring data sets collected during the injection of heated water in a shallow aquifer. We tested different error models to show that the use of an appropriate time-lapse data error estimate yields significant improvements in terms of imaging. An adapted inversion weighting for time-lapse data implies that the procedure does not allow an over-fitting of the data, so the presence of artifacts in the resulting images is greatly reduced. Our results demonstrate that a proper estimate of time-lapse data error is mandatory for weighting optimally the inversion in order to obtain images that best reflect the medium properties evolution through time. [less ▲]

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See detailPrediction-focused approaches: an opportunity for hydrology
Hermans, Thomas ULiege

in Groundwater (2017), 55(5), 683-687

Our ability to predict the evolution of complex hydrological system is fundamental. For decades, such problems have been solved by calibrating a conceptual model of the subsurface to fit data ... [more ▼]

Our ability to predict the evolution of complex hydrological system is fundamental. For decades, such problems have been solved by calibrating a conceptual model of the subsurface to fit data. Unfortunately, model calibration does not allow a realistic uncertainty quantification, whereas stochastic inversion is often computationally prohibitive. In this contribution, prediction-focused approaches (PFAs) are introduced to overcome those main shortcomings. This new paradigm focused on generating predictions directly from the data instead of generating models. A group of prior models is used to generate the data and the prediction in order to derive a direct relationship between both types of variables. The advantages, limitations and research perspectives are discussed. [less ▲]

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See detailPROPOSITION D'UN SUPPORT D'AIDE À LA DÉCISION POUR L'AMÉLIORATION DE L'ACCÈS À UNE EAU SOUTERRAINE DE MEILLEURE QUALITÉ DANS UN CONTEXTE DE CONTAMINATION GÉOGÉNIQUE AU FLUORURE AU BENIN (AFRIQUE DE L'OUEST)
Tossou, Joël; Hermans, Thomas ULiege; Orban, Philippe ULiege et al

in Geo-Eco-Trop: Revue Internationale de Géologie, de Géographie et d'Écologie Tropicales (2017)

Les eaux souterraines des aquifères de socle cristallin de la partie centrale du Bénin (Département des Collines) présentent des concentrations élevées en fluorure, allant jusqu'à 7 mg/L alors que la ... [more ▼]

Les eaux souterraines des aquifères de socle cristallin de la partie centrale du Bénin (Département des Collines) présentent des concentrations élevées en fluorure, allant jusqu'à 7 mg/L alors que la norme recommandée par l'OMS est de 1.5 mg/L. La consommation de ces eaux à fortes teneurs en fluorure impacte la santé humaine. La population de la région est effectivement largement affectée par la fluorose dentaire. Les investigations hydrogéochimiques révèlent que l’origine de ces teneurs anormales est géogénique avec une forte contribution des minéraux ferromagnésiens, principalement la biotite. Ce travail se propose de réaliser une double cartographie à l'échelle du département des Collines: (i) une carte de l'estimation des teneurs en fluorure dans les eaux souterraines par krigeage ordinaire et (ii) une carte de la probabilité d'excéder la valeur guide de l’OMS (1.5 mg/L) en fluorure dans les eaux par krigeage d'indicatrices. Outre la cartographie en elle-même, l'analyse de la structure spatiale des données (teneurs en fluorure des eaux souterraines) à travers le calcul des variogrammes montre qu'il existe un lien fort entre celles-ci et les structures géologiques dominantes, confirmant l'origine géogénique du fluorure. Ces informations cartographiques serviront de support à la décision pour les décideurs et les gestionnaires de la ressource quant au choix judicieux des zones de captage d'eau potable pour minimiser/éviter les risques de fortes teneurs en fluorure. [less ▲]

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See detailFacies discrimination with electrical resistivity tomography using a probabilistic methodology: Effect of sensitivity and regularization
Hermans, Thomas ULiege; Irving, James

in Near Surface Geophysics (2017), 15

Electrical resistivity tomography (ERT) has become a standard geophysical method in the field of hydrogeology, as it has the potential to provide important information regarding the spatial distribution ... [more ▼]

Electrical resistivity tomography (ERT) has become a standard geophysical method in the field of hydrogeology, as it has the potential to provide important information regarding the spatial distribution of facies. However, inverted ERT images tend to be grossly smoothed versions of reality because of the regularization of the inverse problem. In this study, we use a probabilistic methodology based upon co-located measurements to assess the utility of ERT to identify hydrofacies in alluvial aquifers. With this methodology, ERT images are interpreted in terms of the probability of belonging to pre-defined hydrofacies. We first analyze through a synthetic study the ability of ERT to discriminate between different facies. As ERT data suffer from a loss of sensitivity with depth, we find that low sensitivity regions are more affected by misclassification. To counteract this effect, we adapt the probabilistic framework to include the spatially varying data sensitivity. We then apply our learning to a field case. For the latter, we consider two different regularization procedures. In contrast to the data sensitivity which affects the facies probability to a limited amount, the regularization can affect the probability maps more considerably because it has a strong influence on the spatial distribution of inverted resistivity. We find that a regularization strategy based on the most realistic prior information tends to offer the most reliable discrimination of facies. Our results confirm the ability of ERT surveys, when properly designed, to detect facies variations in alluvial aquifers. The method can be easily extended to other contexts. [less ▲]

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See detail3D electrical resistivity tomography of karstified formations using cross-line measurements
Van Hoorde, Maurits; Hermans, Thomas ULiege; Dumont, Gaël ULiege et al

in Engineering Geology (2017), 220

The acquisition of a full 3D survey on a large area of investigation is difficult, and from a practitioner’s point of view, very costly. In high-resolution 3D surveys, the number of electrodes increases ... [more ▼]

The acquisition of a full 3D survey on a large area of investigation is difficult, and from a practitioner’s point of view, very costly. In high-resolution 3D surveys, the number of electrodes increases rapidly and the total number of electrode combinations becomes very large. In this paper, we propose an innovative 3D acquisition procedure based on the roll-along technique. It makes use of 2D parallel lines with additional cross-line measurements. However, in order to increase the number of directions represented in the data, we propose to use cross-line measurements in several directions. Those cross-line measurements are based on dipole-dipole configurations as commonly used in cross-borehole surveys. We illustrate the method by investigating the subsurface geometry in a karstic environment for a future wind turbine project. We first test our methodology with a numerical benchmark using a synthetic model. Then, we validate it through a field case application to image the 3D geometry of karst features and the top of unaltered bedrock in limestone formations. We analyze the importance of cross-line measuring and analyze their capability for accurate subsurface imaging. The comparison with standard parallel 2D surveys clearly highlighted the added value of the cross-lines measurements to detect those structures. It provides crucial insight in subsurface geometry for the positioning of the future wind turbine foundation. The developed method can provide a useful tool in the design of 3D ERT survey to optimize the amount of information collected within a limited time frame. [less ▲]

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