Publications of Frédéric Nguyen
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See detailGeochemical and Mineralogical Characterisation of Historic Zn–Pb Mine Waste, Plombières, East Belgium
Bevandić, Srećko; Blannin, Rosie; Vander Auwera, Jacqueline ULiege et al

in Minerals (2021)

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See detailGeophysical surveys for unlocking landfill resources: from past applications to future developments
Van de Vijver, Ellen; Caterina, David ULiege; Isunza Manrique, Itzel ULiege et al

Poster (2020, December 16)

The earliest recognition of the potential of geophysical methods for characterizing landfill sites dates from over half a century ago and since then numerous case studies have been presented. In the vast ... [more ▼]

The earliest recognition of the potential of geophysical methods for characterizing landfill sites dates from over half a century ago and since then numerous case studies have been presented. In the vast majority of studies in the literature, the interest in landfill investigation relates to the assessment of associated environmental pollution problems, primarily the contamination of groundwater by landfill leachate. The landfill geometry and internal structure are recurrent targets in geophysical surveys performed in direct support of planning site remediation projects. While remediation usually involves the excavation of the disposed wastes, in which part of the excavated materials can be recycled, only a few studies have made an explicit link to landfill mining. The recent introduction of the concept of dynamic landfill management – aiming to provide an integrated framework for landfill pollution prevention, and land remediation and reclamation, including enhanced landfill mining focusing on the recovery of resources in terms of materials and/or energy – provided a new incentive to advance the use of geophysical methods as economic tools for landfill characterization. Yet, setting the waste composition and the quality of waste materials for recovery as main targets adds some new challenges to the more conventional survey practice tailored to environmental risk assessment. The extreme complexity, variability and heterogeneity that waste deposits can show, raise the ambiguity of possible interpretations of geophysical data to the next level and, hence, careful preparation is required for any investments made in geophysical investigations not to go to waste. In this contribution, we present an overview of recent applications of geophysical methods to the study of landfills of different types of waste, ages, and construction settings. We identify critical factors to successful site exploration to support landfill “mining” and formulate recommendations for the improvement of geophysical survey design and accompanying calibration and/or validation sampling in order to achieve maximal information retrieval and allow for uncertainty assessment of the obtained results. The instructions given are illustrated by examples of case studies conducted within the EU projects RAWFILL and NEW-MINE. [less ▲]

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See detailSensing the electrical properties of roots: A review
Ehosioke, Solomon ULiege; Nguyen, Frédéric ULiege; Rao, Sathanarayan et al

in Vadose Zone Journal (2020)

Thorough knowledge of root system functioning is essential to understand the feedback loops between plants, soil and climate. In-situ characterization of root systems is challenging due to the ... [more ▼]

Thorough knowledge of root system functioning is essential to understand the feedback loops between plants, soil and climate. In-situ characterization of root systems is challenging due to the inaccessibility of roots and the complexity of root zone processes. Electrical methods have been proposed to overcome these difficulties. Electrical conduction and polarization occur in and around roots, but the mechanisms are not yet fully understood. We review the potential and limitations of low-frequency electrical techniques for root zone investigation, discuss the mechanisms behind electrical conduction and polarization in the soil-root continuum and address knowledge gaps. A range of electrical methods for root investigation is available. Reported methods using current injection in the plant stem to assess the extension of the root system lack robustness. Multi-electrode measurements are increasingly used to quantify root zone processes through soil moisture changes. They often neglect the influence of root biomass on the electrical signal, probably because it is yet to be well understood. Recent research highlights the potential of frequency-dependent impedance measurements. These methods target both surface and volumetric properties by activating and quantifying polarization mechanisms occurring at the root segment and cell scale at specific frequencies. The spectroscopic approach opens up a range of applications. Nevertheless, understanding electrical signatures at the field scale requires significant understanding of small-scale polarization and conduction mechanisms. Improved mechanistic soil-root electrical models, validated with small-scale electrical measurements on root systems, are necessary to make further progress in ramping up the precision and accuracy of multi-electrode tomographic techniques for root zone investigation. [less ▲]

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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 detailBayseian Evidential Learning for 1D geological imaging from geophysical data
Michel, Hadrien ULiege; Nguyen, Frédéric ULiege; Hermans, Thomas

Conference (2020, September)

Geophysics is widely used to model the subsurface due to its combination of low-cost and large spatial coverage. However, proper uncertainty quantification based on geophysical data is rarely performed ... [more ▼]

Geophysics is widely used to model the subsurface due to its combination of low-cost and large spatial coverage. However, proper uncertainty quantification based on geophysical data is rarely performed due to the high computational cost of such operation (Bayesian approach) or the poor quality of the uncertainty estimation (error propagation). Bayesian Evidential Learning (BEL) approximates the Bayesian problem in a reduced space, leading to a reduced computational cost. When applied to 1D geological imaging, we demonstrate that BEL produces coherent posterior uncertainty under a reasonable CPU time. Moreover, our implementation of BEL for 1D geological imaging (BEL1D) is fully separated into two phases, similar to machine learning: a learning phase and a prediction phase. This means that prediction of posterior model space can be reduced to only the prediction phase since learning can be reused as much as wanted, leading to extremely rapid estimations of uncertainty. During the learning phase, we derive a statistical relationship from a training set of geophysical models and their associated geophysical response in reduced space. During the prediction phase, we simply extract the conditional probability of the geological models given the geophysical data. This latter process is extremely rapid numerically and results in the approximated posterior in reduced space. Then, sampling as many models from this posterior space as needed and transforming them back into the original space leads to a set of models from the posterior. The algorithm (implemented into a set of open-source Matlab toolboxes) is already applied with success on surface nuclear magnetic resonance and dispersion curves from seismic surface waves. In further version of the algorithm, we plan on extending the capabilities of BEL1D to other geophysical methods and to relax the constrains on the prior definition (currently 1D blocky models). [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 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 detailElectrical signature of Co2-rich groundwater systems
Defourny, Agathe ULiege; Kremer, Thomas; Dassargues, Alain ULiege et al

Poster (2020, May 06)

The combination of IP and ERT measurements provides a new proxy for detection of CO2 -rich groundwater.

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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 detailAssessment of magnetic data for landfill characterization by means of a probabilistic approach
Isunza Manrique, Itzel ULiege; Caterina, David ULiege; Inauen, Cornelia et al

Conference (2020, May 06)

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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 detailProbabilistic Joint Interpretation of Multiple Geophysical Methods for Landfill Characterization
Isunza Manrique, Itzel ULiege; Caterina, David ULiege; Hermans, Thomas et al

Poster (2020, February 20)

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See detailImproving BEL1D accuracy for geophysical imaging of the subsurface
Michel, Hadrien ULiege; HERMANS, Thomas; Nguyen, Frédéric ULiege

in Nedorub, O.; Swinfrod, B. (Eds.) SEG TEchnical Program Expanded Abstracts 2020 (2020)

BEL1D (Bayesian Evidential Learning 1D imaging) has recently been introduced as a viable option for the stochastic imaging of the subsurface geophysical properties (Michel et al., 2020). This methodology ... [more ▼]

BEL1D (Bayesian Evidential Learning 1D imaging) has recently been introduced as a viable option for the stochastic imaging of the subsurface geophysical properties (Michel et al., 2020). This methodology has been applied to surface nuclear magnetic resonance and surface wave data in order to produce sets of probable models of the subsurface. Here, we improve the accuracy of this algorithm by the introduction of iterative prior resampling. We further validate results against a state-of-the-art McMC method. [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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