References of "Nguyen, Frédéric"
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See detailSite-selection criteria for the Einstein Telescope
Amann, Florian; Bonsignorio, Fabio; Bulik, Tomasz et al

in Review of Scientific Instruments (2020), 91

The Einstein Telescope (ET) is a proposed next-generation, underground gravitational-wave detector to be based in Europe. It will provide about an order of magnitude sensitivity increase with respect to ... [more ▼]

The Einstein Telescope (ET) is a proposed next-generation, underground gravitational-wave detector to be based in Europe. It will provide about an order of magnitude sensitivity increase with respect to the currently operating detectors and, also extend the observation band targeting frequencies as low as 3 Hz. One of the first decisions that needs to be made is about the future ET site following an in-depth site characterization. Site evaluation and selection is a complicated process, which takes into account science, financial, political, and socio economic criteria. In this paper, we provide an overview of the site-selection criteria for ET, provide a formalism to evaluate the direct impact of environmental noise on ET sensitivity, and outline the necessary elements of a site-characterization campaign. [less ▲]

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See detailUsing UAV Collected RGB and Multispectral Images to Evaluate Winter Wheat Performance Across a Site Characterized by Century-Old Biochar Patches in Belgium
Heidarian Dehkordi, Ramin ULiege; Burgeon, Victor ULiege; Fouche, Julien et al

in Remote sensing (2020), 12

Remote sensing data play a crucial role in monitoring crop dynamics in the context of precision agriculture by characterizing the spatial and temporal variability of crop traits. At present there is ... [more ▼]

Remote sensing data play a crucial role in monitoring crop dynamics in the context of precision agriculture by characterizing the spatial and temporal variability of crop traits. At present there is special interest in assessing the long-term impacts of biochar in agro-ecosystems. Despite the growing body of literature on monitoring the potential biochar effects on harvested crop yield and aboveground productivity, studies focusing on the detailed crop performance as a consequence of long-term biochar enrichment are still lacking. The primary objective of this research was to evaluate crop performance based on high-resolution unmanned aerial vehicle (UAV) imagery considering both crop growth and health through RGB and multispectral analysis, respectively. More specifically, this approach allowed monitoring of century-old biochar impacts on winter wheat crop performance. Seven Red-Green-Blue (RGB) and six multispectral flights were executed over 11 century-old biochar patches of a cultivated field. UAV-based RGB imagery exhibited a significant positive impact of century-old biochar on the evolution of winter wheat canopy cover (p-value = 0.00007). Multispectral optimized soil adjusted vegetation index indicated a better crop development over the century-old biochar plots at the beginning of the season (p-values < 0.01), while there was no impact towards the end of the season. Plant height, derived from the RGB imagery, was slightly higher for century-old biochar plots. Crop health maps were computed based on principal component analysis and k-means clustering. To our knowledge, this is the first attempt to quantify century-old biochar effects on crop performance during the entire growing period using remotely sensed data. Ground-based measurements illustrated a significant positive impact of century-old biochar on crop growth stages (p-value of 0.01265), whereas the harvested crop yield was not affected. Multispectral simplified canopy chlorophyll content index and normalized difference red edge index were found to be good linear estimators of harvested crop yield (p-value(Kendall) of 0.001 and 0.0008, respectively). The present research highlights that other factors (e.g., inherent pedological variations) are of higher importance than the presence of century-old biochar in determining crop health and yield variability. [less ▲]

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See detailImproving the accuracy of 1D surface nuclear magnetic resonance surveys using the multi-central-loop configuration
Kremer, Thomas; Müller-Petke, Mike; Michel, Hadrien ULiege et al

in Journal of Applied Geophysics (2020)

Surface nuclear magnetic resonance is a near-surface geophysical method for characterizing the spatial distribution of liquid water in the top 100 m of the subsurface. The recovered water content models ... [more ▼]

Surface nuclear magnetic resonance is a near-surface geophysical method for characterizing the spatial distribution of liquid water in the top 100 m of the subsurface. The recovered water content models are obtained through the solution of an ill-posed inverse problem that is a function of acquisition parameters, including location and shape of the transmitter and receiver coils. In this paper, we introduce the multi-central-loop acquisition and inversion strategy where one or several smaller receivers coils are placed in the center of the larger transmitter loop and where all the data sets synchronously recorded through each loop are inverted simultaneously. We investigate the attributes of this acquisition and inversion strategy including the ability to provide improved resolution, accuracy and reduced uncertainty on the estimated subsurface models compared to single channel acquisition methods. Using widely-adopted inversion methods and introducing a new data interpretation technique called Bayesian Evidential Learning 1D imaging, we show that the multi-central-loop configuration provides improved recovery of synthetic models and reduced levels of inverted parameter uncertainty. A field case is also presented where the multi-central-loop results appear to better match the lithologic knowledge of the area compared with single channel configurations, again providing smaller uncertainties. [less ▲]

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See detailBEL1D: 1D imaging using geophysical data in the framework of Bayesian Evidential Learning
Michel, Hadrien ULiege; Nguyen, Frédéric ULiege; Hermans, Thomas

Conference (2020, May 06)

BEL1D has been newly introduced to the community as a viable algorithm for the stochastic interpretation of geophysical data in the form of 1D geological models. It relies on a simplified version of the ... [more ▼]

BEL1D has been newly introduced to the community as a viable algorithm for the stochastic interpretation of geophysical data in the form of 1D geological models. It relies on a simplified version of the Bayesian problem in reduced space called Bayesian Evidential Learning. However, the method is closer to machine learning than classical McMC approaches since it can be separated into a learning process followed by a prediction part. The learning phase consists in constituting statistical relationships between models parameters and geophysical data from a training set of numerical models. The prediction phase then samples the previous relationships according to field data. Compared to other stochastic methods such as McMC, BEL1D as key advantages: 1) it converges easily as long as the prior is consistent with the unique input parameter being the size of the training set, 2) every model in the posterior is drawn independently, making it easy to trace back their origin, 3) the CPU times are similar to McMC, but the method can be fully parallelized and the learning process can be done before data acquisition, leading to quasi instantaneous prediction of the posterior. BEL1D already has led to successful applications on surface nuclear magnetic resonance data as well as dispersion curves from surface waves analysis. Nonetheless, the method is not limited to those two examples and can be implemented for any 1D geophysical method as long as a forward model is provided. Currently, the method is implemented for blocky imaging but will be extended to non-blocky models in the future. The open source codes are readily available. [less ▲]

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See detailEvaluating the resource recovery potential of fly ash deposits using electrical and electromagnetic methods
Caterina, David ULiege; Isunza Manrique, Itzel ULiege; Michel, Hadrien ULiege et al

Poster (2020, May 06)

Burning coal, or municipal solid waste, in thermal power plants and in metallurgical industries is responsible for the production of large amounts of combustion residues, which depending on their particle ... [more ▼]

Burning coal, or municipal solid waste, in thermal power plants and in metallurgical industries is responsible for the production of large amounts of combustion residues, which depending on their particle size and density, are usually referred to as fly or bottom ash. Nowadays, they represent one of the main types of industrial waste generated. Although their composition is strongly dependent on the material burned, they typically contain ferro-aluminosilicate minerals with potentially toxic trace elements and inorganic compounds that can cause environmental problems when stored in non-sanitary landfills. At the same time, they also represent an economically interesting secondary resource as they can be valorised by replacing aggregates/additives in cement or ceramics production. Surprisingly, despite the environmental and economic considerations for these materials, their geophysical properties remain largely unknown. A better understanding of their geophysical identity could enable using geophysical methods to, for example, improve the estimation of the volume and quality of recoverable resources from ash deposition sites. In this contribution, we show the results of geophysical investigations carried out in three of these sites located in Belgium. The main geophysical techniques involved are electrical resistivity tomography, time-domain induced polarization and frequency-domain electromagnetic induction. The deposits studied generally exhibit high electrical conductivity presumably due to the high hygroscopy of fly ash, the high chlorides content and the presence of ferro-aluminosilicate minerals, each of which enhancing electrical conduction mechanisms, although the effect of the first two is conditioned by the level of water saturation present. The presence of magnetite, or other ferri- or ferromagnetic materials, may explain the high magnetic susceptibility observed. Yet, while representing a relatively homogeneous type of waste, strong variations in geophysical properties were observed between and within different sites. This suggests a great influence of the ash production process, but also of the site-specific conditions. These first results argue for further field and laboratory experiments to validate the exploratory geophysical survey results and to identify the decisive influencing factors explaining the observed electrical and magnetic response. Improved insight in the geophysical signature of fly ash deposits will allow for more accurate interpretations of geophysical measurements, in its turn providing a more sound basis for guiding conventional sampling design and thereby contributing to a more reliable assessment of the value of these industrial waste landfills in terms of the secondary resources they can deliver. [less ▲]

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See detail1D geological imaging of the subsurface from geophysical data with Bayesian Evidential Learning
Michel, Hadrien ULiege; Nguyen, Frédéric ULiege; Kremer, Thomas et al

in Computers and Geosciences (2020), 138

Imaging the subsurface of the Earth is of prime concern in geosciences. In this scope, geophysics offers a wide range of methods that are able to produce models of the subsurface, classically through ... [more ▼]

Imaging the subsurface of the Earth is of prime concern in geosciences. In this scope, geophysics offers a wide range of methods that are able to produce models of the subsurface, classically through inversion processes. Deterministic inversions lack the ability to produce satisfactory quantifications of uncertainty, whereas stochastic inversions are often computationally demanding. In this paper, a new method to interpret geophysical data is proposed in order to produce 1D imaging of the subsurface along with the uncertainty on the associated parameters. This new approach called Bayesian Evidential Learning 1D imaging (BEL1D) relies on the constitution of statistics-based relationships between simulated data and associated model parameters. The method is applied to surface nuclear magnetic resonance for both a numerical example and field data. The obtained results are compared to the solutions provided by other approaches for the interpretation of these datasets, to demonstrate the robustness of BEL1D. Although this contribution demonstrates the framework for surface nuclear magnetic resonance geophysical data, it is not restricted to this type of data but can be applied to any 1D inverse problem. [less ▲]

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See detailLandfill characterization by multi-method geophysical investigation: the case study of Leppe (Germany)
Debouny, Tom ULiege; Caterina, David ULiege; Isunza Manrique, Itzel ULiege et al

Poster (2020, May)

Whether environmental or economic interests are at stake, characterization of landfills is becoming a key operation. Characterization not only concerns old landfills, but also modern engineered landfills ... [more ▼]

Whether environmental or economic interests are at stake, characterization of landfills is becoming a key operation. Characterization not only concerns old landfills, but also modern engineered landfills where the assessment and monitoring of internal processes such as leachate and biogas generation is of a primary importance. Nowadays, characterization is mostly carried out by conventional invasive methods based on drilling/trenching, sampling and laboratory analyses. Although they provide direct and analytical information, their spatial coverage, or representability, remains a major drawback. In addition, they can be expensive and increase the risk of damaging contamination barriers. Therefore, non- to minimally- invasive characterization geophysical techniques emerge as a complementary option. They allow to better capture the spatial heterogeneity across a site and are more cost-effective than punctual measurements alone. Furthermore, when compared with limited ground truth data, they may provide insights into waste composition, water content or temperature. The present study highlights the added value of a multiple geophysical approach to characterize a landfill located in Engelskirchen in Germany. Leppe landfill was used as a municipal solid waste (MSW) deposit site from 1982 until the end of 2004. Since then, only ash coming from the MSW incineration is discarded, mostly on top of the previous MSW deposit. The combination of geophysical methods used in this study included electrical resistivity tomography (ERT), induced polarization (IP), multichannel analysis of surface waves (MASW) and horizontal to vertical noise spectral ratio (HVSNR). The 3D ERT and IP model allowed to identify dry zones within the waste (which may have a direct impact on biogas production) and to roughly discriminate the layer of ash from the MSW layer. Seismic velocity model provided by MASW permitted to significantly improve the delineation between the two layers. HVNSR results combined with the information provided by MASW were used to estimate the thickness of the top layer on a larger area using a bilayer hypothesis. These geophysical characterization results were validated with available ground truth data. Overall, in the present case seismic methods showed to be more suited than geoelectrical techniques for the distinction between the ash and MSW layers. [less ▲]

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See detailInduced Polarization as a Proxy for CO2-Rich Groundwater Detection—Evidences from the Ardennes, South-East of Belgium
Defourny, Agathe ULiege; Nguyen, Frédéric ULiege; Collignon, Arnaud et al

in Water (2020), 12(5),

CO2-rich mineral groundwaters are of great economic and touristic interest but their origin and circulation paths in the underground are often poorly understood. A deeper understanding of the system ... [more ▼]

CO2-rich mineral groundwaters are of great economic and touristic interest but their origin and circulation paths in the underground are often poorly understood. A deeper understanding of the system plumbery and the development of non—to minimally—invasive near-surface geophysical methods for the prospection of potential productive areas is therefore of great interest to manage future supply. The objective of this contribution is to assess the ability of the time-domain induced polarization (TDIP) method, combined with the electrical resistivity tomography (ERT) method, to make the distinction between CO2-rich groundwater from non-gaseous groundwater. Three combined ERT/TDIP tomographies were performed above known uplift zones in the south-east of Belgium where thousands of CO2-rich groundwater springs exist. On all profiles, important contrasts in both electrical resistivity and chargeability distributions were observed in the vicinity of the upflow zone, also reflected in the normalized chargeability sections computed from the measured data. Low resistivity vertical anomalies extending in depth were interpreted as a saturated fracture network enabling the upflow of deep groundwater to the surface. High chargeability anomalies appearing directly close to the CO2-rich groundwater springs were inferred to metallic oxides and hydroxides precipitation in the upper part of the aquifer, linked to pressure decrease and changing redox conditions in the up-flowing groundwater approaching the land surface. The combined interpretation of electrical resistivity and induced polarization datasets provides a very promising method for a robust prospection of CO2-rich groundwater. [less ▲]

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See detailA cross-validation framework to extract data features for reducing structural uncertainty in subsurface heterogeneity
Lopez Alvis, Jorge ULiege; Hermans, Thomas ULiege; Nguyen, Frédéric ULiege

in Advances in Water Resources (2019), 133

Spatial heterogeneity is a critical issue in the management of water resources. However, most studies do not consider uncertainty at different levels in the conceptualization of the subsurface patterns ... [more ▼]

Spatial heterogeneity is a critical issue in the management of water resources. However, most studies do not consider uncertainty at different levels in the conceptualization of the subsurface patterns, for example using one single geological scenario to generate an ensemble of realizations. In this paper, we represent the spatial uncertainty by the use of hierarchical models in which higher-level parameters control the structure. Reduction of uncertainty in such higher-level structural parameters with observation data may be done by updating the complete hierarchical model, but this is, in general, computationally challenging. To address this, methods have been proposed that directly update these structural parameters by means of extracting lower dimensional representations of data called data features that are informative and applying a statistical estimation technique using these features. The difficulty of such methods, however, lies in the choice and design of data features, i.e. their extraction function and their dimensionality, which have been shown to be case-dependent. Therefore, we propose a cross-validation framework to properly assess the robustness of each designed feature and make the choice of the best feature more objective. Such framework aids also in choosing the values for the parameters of the statistical estimation technique, such as the bandwidth for kernel density estimation. We demonstrate the approach on a synthetic case with cross-hole ground penetrating radar traveltime data and two higher-level structural parameters: discrete geological scenarios and the continuous preferential orientation of channels. With the best performing features selected according to the cross-validation score, we successfully reduce the uncertainty for these structural parameters in a computationally efficient way. While doing so, we also provide guidelines to design features accounting for the level of knowledge of the studied system. © 2019 [less ▲]

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See detailImpact of Maize Roots on Soil–Root Electrical Conductivity: A Simulation Study
Sathyanarayan, Rao; Meunier, Félicien; Ehosioke, Solomon ULiege et al

in Vadose Zone Journal (2019)

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See detailUse of precision farming practices and crop modelling for enhancing water and phosphorus efficiency
El-Mejjaouy, Yousra ULiege; Mamassi, Achraf ULiege; Chtouki, Mohamed ULiege et al

Poster (2019, October 07)

In a context of climate change, African agriculture aims at developing new approaches to face multiple constraints related to water scarcity, soil degradation or nutrients depletion. Nonrenewable ... [more ▼]

In a context of climate change, African agriculture aims at developing new approaches to face multiple constraints related to water scarcity, soil degradation or nutrients depletion. Nonrenewable resources such as phosphorus are of concern. Precision farming, as a new alternative to conventional agriculture, aims to improve crop productivity through the optimization of water and nutrients use efficiency. It considers the spatiotemporal variability of fields related to soil heterogeneity, plant nutrient needs and meteorological conditions through the growing season. For an effective management of soil and crop system, several new technologies have emerged, including soil-plant sensing, innovative crop management practices, and crop growth simulation and yield forecasting models. Regarding phosphorus management, use efficiency can be improved through the accurate assessment of phosphorus status in soil and plant. Proximal sensing based on visible near-infrared spectroscopy seems to be a promising alternative to manage soil fertility, understand phosphorus dynamics and enhance crop productivity. These aims can be also achieved by adopting hyper-frequent drip fertigation as an efficient agricultural practice, combined to hydrogeophysics to monitor water and nutrient fluxes in the soil-plant continuum. In addition, based on the interactions between meteorological conditions, soil properties and crop management, the use of agrometeorological models in simulation of crop growth parameters and forecasting crop production levels may allow assessing soil fertility and potential, ensuring an optimal future exploitation of farmland through the improvement of fertilization practices in an integrated management cropping system. [less ▲]

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See detail10 years of temperature monitoring experiments using electrical resistivity tomography: What have we learned?
Hermans, Thomas ULiege; Lesparre, Nolwenn; De Schepper, Guillaume et al

in EarthDoc - Proceedings of the 1st Conference on Geophysics for Geothermal and Renewable Energy Storage (2019, September 11)

The electrical resistivity of the subsurface is dependent on the temperature. This makes electrical resistivity tomography a good candidate for monitoring temperature variations within the context of ... [more ▼]

The electrical resistivity of the subsurface is dependent on the temperature. This makes electrical resistivity tomography a good candidate for monitoring temperature variations within the context of aquifer thermal energy storage or thermal tracer test. In this contribution, we review the advances made in the development of ERT for monitoring heat storage and heat tracing experiments during the last ten years. We highlight the common limitations related to ERT such as the need for a petrophysical relationship for a proper survey design, as well as the concerns related to noise and inversion. We also point towards the solutions available to overcome those limitations and guidelines for successful monitoring experiments. We think this contribution will help practitioners and scientists to make the appropriate choice when designing or exploiting shallow geothermal systems. [less ▲]

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See detailIntegration of proxy data for understanding CO2-rich mineral groundwater in the Ardennes region (southeast of Belgium)
Defourny, Agathe ULiege; Kremer, Thomas; Collignon, Arnaud et al

Poster (2019, September 11)

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See detail1D geological modeling of the subsurface from geophysical data with Bayesian Evidential Learning
Michel, Hadrien ULiege; Hermans, Thomas; Nguyen, Frédéric ULiege

Poster (2019, April 11)

Uncertainty appraisal is a key concern to geophysicists when imaging the subsurface. This issue is classically handled by stochastic inversion (costly CPU) or by error propagation (unrealistic uncertainty ... [more ▼]

Uncertainty appraisal is a key concern to geophysicists when imaging the subsurface. This issue is classically handled by stochastic inversion (costly CPU) or by error propagation (unrealistic uncertainty). However, those methods suffer from an important CPU cost, due to the need for many runs of inversions. Bayesian Evidential Learning (BEL) offers a real shift towards a fully stochastic framework for the optimization of acquisition and the interpretation of data in geophysics. Contrary to inversion methods, interpretation of geophysical data through BEL relies on the constitution of statistical relationships between model parameters (in the prior model space) and the corresponding data, in order to produce statistical distributions of model parameters constrained to the knowledge of field acquired data (the posterior model space). Hence, it does not require any inversion of the data but rather multiple, independent (and thus fully parallelizable) runs of the much more CPU efficient forward model. This new framework has been adapted to static 1D modelling of the subsurface constrained to geophysical data. The developed process has then been applied to both synthetic and field-acquired data, demonstrating the ability of the process to create consistent sets of probable posterior models, provided that the prior model space is defined wisely, even for noisy data sets. The method was tested for surface nuclear magnetic resonance and multi-channel analysis of surface wave. However, the framework and associated software package were developed such that it can be applied to any 1D problem as long as the forward code is available. [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 detailImproving Bayesian Evidential Learning 1D imaging (BEL1D) accuracy through iterative prior resampling
Michel, Hadrien ULiege; Nguyen, Frédéric ULiege; Hermans, Thomas

Poster (2019)

Bayesian Evidential Learning 1D Imaging (BEL1D) has been recently introduced as a new computationally efficient tool for the interpretation of 1D geophysical datasets in a Bayesian framework. Applications ... [more ▼]

Bayesian Evidential Learning 1D Imaging (BEL1D) has been recently introduced as a new computationally efficient tool for the interpretation of 1D geophysical datasets in a Bayesian framework. Applications have already been demonstrated for Surface Nuclear Magnetic Resonance (SNMR) data and surface waves dispersion curves. The case of SNMR is particularly relevant in hydrogeophysics, as it directly sounds the water content of the subsurface. BEL1D relies on the constitution of statistical relationships in a reduced dimension space between model parameters and simulated data using prior model samples that replicate the field experiment. In BEL1D, this relationship is deduced through Canonical Correlation Analysis (CCA). When using large prior distributions, CCA may lead to numerous poorly correlated distributions for higher dimensions. Those poorly correlated distributions are resulting in a low reduction of uncertainty on some parameters, even if the experiment is supposed to be sensitive to them. This phenomenon is related to the aggregation of multiple parameters in the same dimension, hence the possible aggregation of sensitive and insensitive parameters. However, arbitrarily reducing the extent of the prior will lead to biased estimations. To overcome this impediment, we introduce an iterative procedure, using the posterior model space of the previous iteration as prior model of the current iteration. This approach frequently reveals higher correlations between the datasets and the model parameters, while still using large unbiased priors. It enables BEL1D to produce better estimations of the posterior probability density functions of the model parameters. Nonetheless, iterating on BEL1D presents several challenges related to the presence of insensitive parameters, that will always mitigate the capacity to reduce at once the uncertainty on the whole set of parameters describing the models. On noise-free synthetic datasets, this method leads to near-exact estimation of the sensitive parameters after few (two to three) iterations. On noisy datasets, the resulting distributions bear some uncertainty, arising directly from the presence of noise, but to a lesser extent than the non-iterative approach.. The procedure remains more computationally efficient than McMC. [less ▲]

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See detailInduced polarization applied to biogeophysics: recent advances and future prospects
Kessouri, P.; Furman, A.; Huisman, J.A. et al

in Near Surface Geophysics (2019)

This paper provides an update on the fast-evolving field of the induced polarization (IP) method applied to biogeophysics. It emphasizes recent advances in the understanding of IP signals stemming from ... [more ▼]

This paper provides an update on the fast-evolving field of the induced polarization (IP) method applied to biogeophysics. It emphasizes recent advances in the understanding of IP signals stemming from biological activity, points out new developments and applications, and identifies existing knowledge gaps. The focus of this review is on the application of IP to study living organisms: soil microorganisms and plants (both roots and stems). We first discuss observed links between the IP signal and microbial cell structure, activity and biofilm formation. We provide an up-to-date conceptual model of the electrical behavior of the microbial cell and biofilms under the influence of an external electrical field, and examine the role of extracellular electron transfer mechanisms on the functionality and development of biofilms. We review the latest biogeophysical studies, including work on hydrocarbon biodegradation, contaminant sequestration, soil strengthening and peatland characterization. We then elaborate on the IP signature of the plant root zone, relying on a conceptual model for the generation of biogeophysical signals from a plant root cell. The first laboratory experiments show that single roots and root system are highly polarizable. They also present encouraging results for imaging the root system, embedded in a medium, and gaining information on the mass density, the structure or the physiological characteristics of root system. In addition we highlight the application of IP to characterize wood and tree structures in the lab and at the field scale, through tomography of the stem. Finally, we discuss up- and down-scaling between lab and field studies as well as joint interpretation. We emphasize the need for intermediate scale studies and the benefits of using IP as a time-lapse monitoring method. We conclude with the promising integration of IP in mechanistic models to better understand and quantify subsurface biogeochemical processes. [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 detailUpdating structural uncertainty through dimension reduction of geophysical data
Nguyen, Frédéric ULiege; Lopez Alvis, Jorge ULiege; Hermans, Thomas

Conference (2018, December)

Detailed reference viewed: 34 (3 ULiège)