Two-dimensional probabilistic inversion of plane-wave electromagnetic data: methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraint

Volcano electrical tomography unveils edifice collapse hazard linked to hydrothermal system structure and dynamics

International audienceCatastrophic collapses of the flanks of stratovolcanoes constitute a major hazard threatening numerous lives in many countries. Although many such collapses occurred following the ascent of magma to the surface, many are not associated with magmatic reawakening but are triggered by a combination of forcing agents such as pore-fluid pressurization and/or mechanical weakening of the volcanic edifice often located above a low-strength detachment plane. The volume of altered rock available for collapse, the dynamics of the hydrothermal fluid reservoir and the geometry of incipient collapse failure planes are key parameters for edifice stability analysis and modelling that remain essentially hidden to current volcano monitoring techniques. Here we derive a high-resolution, three-dimensional electrical conductivity model of the La Soufrière de Guadeloupe volcano from extensive electrical tomography data. We identify several highly conductive regions in the lava dome that are associated to fluid saturated host-rock and preferential flow of highly acid hot fluids within the dome. We interpret this model together with the existing wealth of geological and geochemical data on the volcano to demonstrate the influence of the hydrothermal system dynamics on the hazards associated to collapse-prone altered volcanic edifices

Middle-Atmosphere Dynamics Observed With a Portable Muon Detector

In the past years, large particle physics experiments have shown that muon rate variations detected in underground laboratories are sensitive to regional, middle-atmosphere temperature variations. Potential applications include tracking short-term atmosphere dynamics, such as Sudden Stratospheric Warmings. We report here that such sensitivity is not only limited to large surface detectors under high-opacity conditions. We use a portable muon detector conceived for muon tomography for geophysical applications, and we study muon rate variations observed over 1 year of measurements at the Mont Terri Underground Rock Laboratory, Switzerland (opacity of ~700 meter water equivalent). We observe a direct correlation between middle-atmosphere seasonal temperature variations and muon rate. Muon rate variations are also sensitive to the abnormal atmosphere heating in January–February 2017, associated to a Sudden Stratospheric Warming. Estimates of the effective temperature coefficient for our particular case agree with theoretical models and with those calculated from large neutrino experiments under comparable conditions. Thus, portable muon detectors may be useful to (1) study seasonal and short-term middle-atmosphere dynamics, especially in locations where data are lacking such as midlatitudes, and (2) improve the calibration of the effective temperature coefficient for different opacity conditions. Furthermore, we highlight the importance of assessing the impact of temperature on muon rate variations when considering geophysical applications. Depending on latitude and opacity conditions, this effect may be large enough to hide subsurface density variations due to changes in groundwater content and should therefore be removed from the time series.Fil: Tramontini, Matías Leandro. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Departamento de Geofísica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad de Lyon 3; FranciaFil: Rosas Carbajal, Marina Andrea. Institut de Physique Du Globe de Paris; FranciaFil: Nussbaum, C.. Swiss Geological Survey At Swisstopo; SuizaFil: Gibert, D.. Universite de Rennes I; FranciaFil: Marteau, Jacques Emmanuel. Universidad de Lyon 3; Franci

An analytical study of seismoelectric signals produced by 1-D mesoscopic heterogeneities

The presence of mesoscopic heterogeneities in fluid-saturated porous rocks can produce measurable seismoelectric signals due to wave-induced fluid flow between regions of differing compressibility. The dependence of these signals on the petrophysical and structural characteristics of the probed rock mass remains largely unexplored. In this work, we derive an analytical solution to describe the seismoelectric response of a rock sample, containing a horizontal layer at its centre, that is subjected to an oscillatory compressibility test. We then adapt this general solution to compute the seismoelectric signature of a particular case related to a sample that is permeated by a horizontal fracture located at its centre. Analyses of the general and particular solutions are performed to study the impact of different petrophysical and structural parameters on the seismoelectric response. We find that the amplitude of the seismoelectric signal is directly proportional to the applied stress, to the Skempton coefficient contrast between the host rock and the layer, and to a weighted average of the effective excess charge of the two materials. Our results also demonstrate that the frequency at which the maximum electrical potential amplitude prevails does not depend on the applied stress or the Skempton coefficient contrast. In presence of strong permeability variations, this frequency is rather controlled by the permeability and thickness of the less permeable material. The results of this study thus indicate that seismoelectric measurements can potentially be used to estimate key mechanical and hydraulic rock properties of mesoscopic heterogeneities, such as compressibility, permeability and fracture complianc

An analytical study of seismoelectric signals produced by 1-D mesoscopic heterogeneities

The presence of mesoscopic heterogeneities in fluid-saturated porous rocks can produce measurable seismoelectric signals due to wave-induced fluid flow between regions of differing compressibility. The dependence of these signals on the petrophysical and structural characteristics of the probed rock mass remains largely unexplored. In this work, we derive an analytical solution to describe the seismoelectric response of a rock sample, containing a horizontal layer at its centre, that is subjected to an oscillatory compressibility test. We then adapt this general solution to compute the seismoelectric signature of a particular case related to a sample that is permeated by a horizontal fracture located at its centre. Analyses of the general and particular solutions are performed to study the impact of different petrophysical and structural parameters on the seismoelectric response. We find that the amplitude of the seismoelectric signal is directly proportional to the applied stress, to the Skempton coefficient contrast between the host rock and the layer, and to a weighted average of the effective excess charge of the two materials. Our results also demonstrate that the frequency at which the maximum electrical potential amplitude prevails does not depend on the applied stress or the Skempton coefficient contrast. In presence of strong permeability variations, this frequency is rather controlled by the permeability and thickness of the less permeable material. The results of this study thus indicate that seismoelectric measurements can potentially be used to estimate key mechanical and hydraulic rock properties of mesoscopic heterogeneities, such as compressibility, permeability and fracture compliance.Facultad de Ciencias Astronómicas y Geofísica

An analytical study of seismoelectric signals produced by 1-D mesoscopic heterogeneities

The presence of mesoscopic heterogeneities in fluid-saturated porous rocks can produce measurable seismoelectric signals due to wave-induced fluid flow between regions of differing compressibility. The dependence of these signals on the petrophysical and structural characteristics of the probed rock mass remains largely unexplored. In this work, we derive an analytical solution to describe the seismoelectric response of a rock sample, containing a horizontal layer at its centre, that is subjected to an oscillatory compressibility test. We then adapt this general solution to compute the seismoelectric signature of a particular case related to a sample that is permeated by a horizontal fracture located at its centre. Analyses of the general and particular solutions are performed to study the impact of different petrophysical and structural parameters on the seismoelectric response. We find that the amplitude of the seismoelectric signal is directly proportional to the applied stress, to the Skempton coefficient contrast between the host rock and the layer, and to a weighted average of the effective excess charge of the two materials. Our results also demonstrate that the frequency at which the maximum electrical potential amplitude prevails does not depend on the applied stress or the Skempton coefficient contrast. In presence of strong permeability variations, this frequency is rather controlled by the permeability and thickness of the less permeable material. The results of this study thus indicate that seismoelectric measurements can potentially be used to estimate key mechanical and hydraulic rock properties of mesoscopic heterogeneities, such as compressibility, permeability and fracture compliance.Facultad de Ciencias Astronómicas y Geofísica

Stratégies d'inversion probabilistes et time-lapse pour des données issues de méthodes électromagnétiques à ondes planes

The efficient use of geothermal systems, the sequestration of CO_2 to mitigate climate change, and the preventionof seawater intrusion in coastal aquifers are only some examples that demonstrate the need for novel technologies to monitor subsurface processes from the surface. A main challenge is to assure optimal performance of such technologies at different temporal and spatial scales. Plane-wave electromagnetic (EM) methods are sensitive to subsurface electrical conductivity and consequently to fluid conductivity, fracture connectivity, temperature, and rock mineralogy. These methods have governing equations that are the same over a large range of frequencies, thus allowing to study in an analogous manner processes on scales ranging from few meters close to the surface down to several hundreds of kilometers depth. Unfortunately, they suffer from a significant resolution loss with depth due to the diffusive nature of the electromagnetic fields. Therefore, estimations of subsurface models that use these methods should incorporate a priori information to better constrain the models, and provide appropriate measures of model uncertainty. During my thesis, I have developed approaches to improve the static and dynamic characterization of the subsurface with plane-wave EM methods. In the first part of this thesis, I present a two-dimensional deterministic approach to perform time-lapse inversion of plane-wave EM data. The strategy is based on the incorporation of prior information into the inversion algorithm regarding the expected temporal changes in electrical conductivity. This is done by incorporating a flexible stochastic regularization and constraints regarding the expected ranges of the changes by using Lagrange multipliers. I use non-l_2 norms to penalize the model update in order to obtain sharp transitions between regions that experience temporal changes and regions that do not. I also incorporate a time-lapse differencing strategyto remove systematic errors in the time-lapse inversion. This work presents improvements in the characterization of temporal changes with respect to the classical approach of performing separate inversions and computing differences between the models. In the second part of this thesis, I adopt a Bayesian framework and use Markov chain Monte Carlo (MCMC) simulationsto quantify model parameter uncertainty in plane-wave EM inversion. For this purpose, I present a two-dimensionalpixel-based probabilistic inversion strategy for separate and joint inversions of plane-wave EM and electrical resistivity tomography (ERT) data. I compare the uncertainties of the model parameters when considering different types of prior information on the model structure and different likelihood functions to describe the data errors. The results indicate that model regularization is necessary when dealing with a large number of model parameters because it helpsto accelerate the convergence of the chains and leads to more realistic models. These constraints also lead to smaller uncertainty estimates, which imply posterior distributions that do not include the true underlying model in regions where the method has limited sensitivity. This situation can be improved by combining plane-wave EM methods with complimentary geophysical methods such as ERT. In addition, I show that an appropriate regularization weight and the standard deviation of the data errors can be retrieved by the MCMC inversion.Finally, I evaluate the possibility of characterizing the three-dimensional distribution of an injected water plume by performing three-dimensional time-lapse MCMC inversion of plane-wave EM data. Since MCMC inversion involves a significant computational burden in high parameter dimensions, I propose a model reduction strategy where the coefficients of a Legendre moment decomposition of the injected water plume and its location are estimated. For this purpose, a base resistivity model is needed which is obtained prior to the time-lapse experiment.A synthetic test shows that the methodology works well when the base resistivity model is correctly characterized. The methodology is also applied to an injection experiment performed in a geothermal system in Australia, and compared to a three-dimensional time-lapse inversion performed within a deterministic framework. The MCMC inversion better constrains the water plumes due to the larger amount of prior information that is included in the algorithm. The conductivity changes needed to explain the time-lapse data are much larger than what is physically possible based on present day understandings. This issue may be related to the base resistivity model used, therefore indicating that more efforts should be given to obtain high-quality base models prior to dynamic experiments.The studies described herein give clear evidence that plane-wave EM methods are useful to characterize and monitor the subsurface at a wide range of scales. The presented approaches contribute to an improved appraisal of the obtained models, both in terms of the incorporation of prior information in the algorithms and the posterior uncertainty quantification.In addition, the developed strategies can be applied to other geophysical methods, and offer great flexibility to incorporate additional information when available.De nouvelles technologies sont nécessaires pour passer d’une production d’énergie basée sur le nucléaire et les combustibles fossiles à des énergies renouvelables. L’utilisation de systèmes géothermaux stimulés est une des possibilités qui pourraient répondre partiellement à la demande en énergie. Pour atteindre cet objectif, il est nécessaire de suivre les chemins d'écoulements des fluides qui sont injectés en profondeur afin de les récupérer une fois qu’ils sont suffisamment chauds pour produire de l'énergie. La séquestration du CO2 pour limiter le changement climatique et la prévention de l’intrusion d’eau salée dans les aquifères costaux sont d’autres exemples qui démontrent notre besoin en technologies pour le suivi des processus dans le sous-sol à partir de la surface à différentes échelles d’espace et de temps. Les méthodes électromagnétiques (EM) d’ondes planes sont sensibles à la conductivité électrique du sous-sol et, par conséquent, à la conductivité électrique des fluides saturant la roche, à la présence de fractures connectées et à la température. Ces méthodes permettent d’étudier de manières analogues des processus allant de quelques mètres sous la surface jusqu’à plusieurs kilomètres de profondeur. Néanmoins, ces techniques sont soumises à une perte de résolution avec la profondeur. Pour cette raison, l’estimation des modèles permettant de représenter le sous-sol à partir de ces méthodes doit prendre en compte l’information a priori disponible afin de contraindre les modèles autant que possible. De plus, il est nécessaire de mesurer les incertitudes sur ces modèles de façon appropriées.Durant le déroulement de cette thèse, j’ai développé des approches permettant la caractérisation statique et dynamique du sous-sol à l’aide d’ondes EM planes. Dans une première partie, je présente une approche déterministe permettant de réaliser des inversions répétées dans le temps (time-lapse) de données d’ondes EM planes en deux dimensions. Cette stratégie est basée sur l’incorporation, dans l’algorithme, d’informations sur les changements de conductivité électrique attendus en fonction du temps. J’utilise aussi une stratégie permettant d’éliminer les erreurs systématiques dans l’inversion de données time-lapse. Dans une seconde partie, j’adopte un formalisme bayésien pour quantifier les incertitudes sur les paramètres du modèle. Pour cet objectif, je présente une stratégie d’inversion basée sur des simulations Markov chain Monte Carlo (MCMC) appliquée à des données d’ondes EM planes et de tomographies de résistivité électrique, séparées et jointes. Je compare les incertitudes des paramètres du modèle en considérant différentes contraintes sur la structure du modèle, et différentes fonctions pour décrire les erreurs sur les données. De plus, je montre que l’écart-type des erreurs sur les données peut être retrouvé par une inversion probabiliste. Dans la dernière partie de cette thèse, j’étudie la distribution d’un panache de traceur salin injecté dans le sous-sol en réalisant une inversion MCMC time-lapse tridimensionnelle d’ondes EM planes. Étant donné que les inversions probabilistes sont très coûteuses en temps de calcul lorsque l’espace des paramètres présente une grande dimension, je propose une stratégie qui permet de réduire le nombre de paramètres pour représenter le panache. Cette méthodologie est appliquée à une expérience d’injection d’un traceur salin et d’acides, réalisée dans un système géothermal stimulé en Australie, et comparée à une inversion time-lapse tridimensionnelle réalisée selon une approche déterministe. Les études décrites démontrent que les méthodes d’ondes EM planes sont très utiles pour caractériser et suivre les variations temporelles du sous-sol sur de larges échelles. Les présentes approches améliorent l’évaluation des modèles obtenus, autant en termes d’incorporation d’informations dans les algorithmes, qu’en termes de quantification d’incertitudes. De plus, les stratégies développées peuvent être appliquées à d’autres méthodes géophysiques, et offrent une grande flexibilité pour l’incorporation d’informations additionnelles lorsqu’elles sont disponibles

Stratégies d'inversion probabilistes et time-lapse pour des données issues de méthodes électromagnétiques à ondes planes

The efficient use of geothermal systems, the sequestration of CO_2 to mitigate climate change, and the preventionof seawater intrusion in coastal aquifers are only some examples that demonstrate the need for novel technologies to monitor subsurface processes from the surface. A main challenge is to assure optimal performance of such technologies at different temporal and spatial scales. Plane-wave electromagnetic (EM) methods are sensitive to subsurface electrical conductivity and consequently to fluid conductivity, fracture connectivity, temperature, and rock mineralogy. These methods have governing equations that are the same over a large range of frequencies, thus allowing to study in an analogous manner processes on scales ranging from few meters close to the surface down to several hundreds of kilometers depth. Unfortunately, they suffer from a significant resolution loss with depth due to the diffusive nature of the electromagnetic fields. Therefore, estimations of subsurface models that use these methods should incorporate a priori information to better constrain the models, and provide appropriate measures of model uncertainty. During my thesis, I have developed approaches to improve the static and dynamic characterization of the subsurface with plane-wave EM methods. In the first part of this thesis, I present a two-dimensional deterministic approach to perform time-lapse inversion of plane-wave EM data. The strategy is based on the incorporation of prior information into the inversion algorithm regarding the expected temporal changes in electrical conductivity. This is done by incorporating a flexible stochastic regularization and constraints regarding the expected ranges of the changes by using Lagrange multipliers. I use non-l_2 norms to penalize the model update in order to obtain sharp transitions between regions that experience temporal changes and regions that do not. I also incorporate a time-lapse differencing strategyto remove systematic errors in the time-lapse inversion. This work presents improvements in the characterization of temporal changes with respect to the classical approach of performing separate inversions and computing differences between the models. In the second part of this thesis, I adopt a Bayesian framework and use Markov chain Monte Carlo (MCMC) simulationsto quantify model parameter uncertainty in plane-wave EM inversion. For this purpose, I present a two-dimensionalpixel-based probabilistic inversion strategy for separate and joint inversions of plane-wave EM and electrical resistivity tomography (ERT) data. I compare the uncertainties of the model parameters when considering different types of prior information on the model structure and different likelihood functions to describe the data errors. The results indicate that model regularization is necessary when dealing with a large number of model parameters because it helpsto accelerate the convergence of the chains and leads to more realistic models. These constraints also lead to smaller uncertainty estimates, which imply posterior distributions that do not include the true underlying model in regions where the method has limited sensitivity. This situation can be improved by combining plane-wave EM methods with complimentary geophysical methods such as ERT. In addition, I show that an appropriate regularization weight and the standard deviation of the data errors can be retrieved by the MCMC inversion.Finally, I evaluate the possibility of characterizing the three-dimensional distribution of an injected water plume by performing three-dimensional time-lapse MCMC inversion of plane-wave EM data. Since MCMC inversion involves a significant computational burden in high parameter dimensions, I propose a model reduction strategy where the coefficients of a Legendre moment decomposition of the injected water plume and its location are estimated. For this purpose, a base resistivity model is needed which is obtained prior to the time-lapse experiment.A synthetic test shows that the methodology works well when the base resistivity model is correctly characterized. The methodology is also applied to an injection experiment performed in a geothermal system in Australia, and compared to a three-dimensional time-lapse inversion performed within a deterministic framework. The MCMC inversion better constrains the water plumes due to the larger amount of prior information that is included in the algorithm. The conductivity changes needed to explain the time-lapse data are much larger than what is physically possible based on present day understandings. This issue may be related to the base resistivity model used, therefore indicating that more efforts should be given to obtain high-quality base models prior to dynamic experiments.The studies described herein give clear evidence that plane-wave EM methods are useful to characterize and monitor the subsurface at a wide range of scales. The presented approaches contribute to an improved appraisal of the obtained models, both in terms of the incorporation of prior information in the algorithms and the posterior uncertainty quantification.In addition, the developed strategies can be applied to other geophysical methods, and offer great flexibility to incorporate additional information when available.De nouvelles technologies sont nécessaires pour passer d’une production d’énergie basée sur le nucléaire et les combustibles fossiles à des énergies renouvelables. L’utilisation de systèmes géothermaux stimulés est une des possibilités qui pourraient répondre partiellement à la demande en énergie. Pour atteindre cet objectif, il est nécessaire de suivre les chemins d'écoulements des fluides qui sont injectés en profondeur afin de les récupérer une fois qu’ils sont suffisamment chauds pour produire de l'énergie. La séquestration du CO2 pour limiter le changement climatique et la prévention de l’intrusion d’eau salée dans les aquifères costaux sont d’autres exemples qui démontrent notre besoin en technologies pour le suivi des processus dans le sous-sol à partir de la surface à différentes échelles d’espace et de temps. Les méthodes électromagnétiques (EM) d’ondes planes sont sensibles à la conductivité électrique du sous-sol et, par conséquent, à la conductivité électrique des fluides saturant la roche, à la présence de fractures connectées et à la température. Ces méthodes permettent d’étudier de manières analogues des processus allant de quelques mètres sous la surface jusqu’à plusieurs kilomètres de profondeur. Néanmoins, ces techniques sont soumises à une perte de résolution avec la profondeur. Pour cette raison, l’estimation des modèles permettant de représenter le sous-sol à partir de ces méthodes doit prendre en compte l’information a priori disponible afin de contraindre les modèles autant que possible. De plus, il est nécessaire de mesurer les incertitudes sur ces modèles de façon appropriées.Durant le déroulement de cette thèse, j’ai développé des approches permettant la caractérisation statique et dynamique du sous-sol à l’aide d’ondes EM planes. Dans une première partie, je présente une approche déterministe permettant de réaliser des inversions répétées dans le temps (time-lapse) de données d’ondes EM planes en deux dimensions. Cette stratégie est basée sur l’incorporation, dans l’algorithme, d’informations sur les changements de conductivité électrique attendus en fonction du temps. J’utilise aussi une stratégie permettant d’éliminer les erreurs systématiques dans l’inversion de données time-lapse. Dans une seconde partie, j’adopte un formalisme bayésien pour quantifier les incertitudes sur les paramètres du modèle. Pour cet objectif, je présente une stratégie d’inversion basée sur des simulations Markov chain Monte Carlo (MCMC) appliquée à des données d’ondes EM planes et de tomographies de résistivité électrique, séparées et jointes. Je compare les incertitudes des paramètres du modèle en considérant différentes contraintes sur la structure du modèle, et différentes fonctions pour décrire les erreurs sur les données. De plus, je montre que l’écart-type des erreurs sur les données peut être retrouvé par une inversion probabiliste. Dans la dernière partie de cette thèse, j’étudie la distribution d’un panache de traceur salin injecté dans le sous-sol en réalisant une inversion MCMC time-lapse tridimensionnelle d’ondes EM planes. Étant donné que les inversions probabilistes sont très coûteuses en temps de calcul lorsque l’espace des paramètres présente une grande dimension, je propose une stratégie qui permet de réduire le nombre de paramètres pour représenter le panache. Cette méthodologie est appliquée à une expérience d’injection d’un traceur salin et d’acides, réalisée dans un système géothermal stimulé en Australie, et comparée à une inversion time-lapse tridimensionnelle réalisée selon une approche déterministe. Les études décrites démontrent que les méthodes d’ondes EM planes sont très utiles pour caractériser et suivre les variations temporelles du sous-sol sur de larges échelles. Les présentes approches améliorent l’évaluation des modèles obtenus, autant en termes d’incorporation d’informations dans les algorithmes, qu’en termes de quantification d’incertitudes. De plus, les stratégies développées peuvent être appliquées à d’autres méthodes géophysiques, et offrent une grande flexibilité pour l’incorporation d’informations additionnelles lorsqu’elles sont disponibles

A new versatile method for the reconstruction of scintillator-based muon telescope events

International audienceThis paper presents a new method to process the data recorded with muon telescopes. We have developed this processing method for the plastic scintillator-based hodoscopes located around the volcano La Soufrière de Guadeloupe, in the French Lesser Antilles, in order to perform muon radiographies of the lava dome region, strongly impacted by the volcanic hydrothermal activity. Our method relies on particle trajectory reconstruction, performing a fit of the recorded hits in the impacted scintillator bars using a Random Sample Consensus algorithm. This algorithm is specifically built to discriminate outlier points, usually due to noise hits, in the data. Thus, it is expected to significantly improve the signal/noise separation in muon track hits and to obtain higher quality estimates of the particles' incident trajectories in our detectors. The first analysis of the RANSAC-reconstructed events offers promising results in terms of average density maps. To illustrate the performances of this algorithm, we provide angular resolution and reconstruction efficiency estimates using a GEANT4 simulation of a telescope equipped with four detection matrices. In addition, we also show preliminary results from open-sky data recorded with such telescope at La Soufrière de Guadeloupe volcano

Three-dimensional modelling of controlled source electro-magnetic surveys using non-conforming finite element methods

International audienceThe controlled source electro-magnetic (CSEM) method is increasingly used for in-land and off-shore subsurface characterization. Given its complex dependence between data and the parameters of interest, there is a crucial need for performant numerical algorithms that can simulate the CSEM response of 3-D geological structures. Here, we present two finite element (FE) algorithms for simulating the CSEM response in 3-D media with isotropic conductivity. A primary/secondary field approach is used to avoid the singularity introduced by the source. The primary field is computed semi-analytically for a horizontally layered model and different sources. The secondary field is obtained by discretizing the diffusive frequency-domain Maxwell's equations with non-conforming FE. The two numerical algorithms are specifically designed to work on distributed-memory computers: (1) an iterative procedure with domain decomposition and (2) a direct and global algorithm. We evaluate their performance by computing their speed up on parallel processors, and solving problems with realistic conductivity structures. We also compare the accuracy of the solutions with published results on canonical models. The results shown here demonstrate the functionality of the two methodologies presented for specific cases when computing 3-D CSEM solutions