Relevant bibliographies by topics / Probabilistic inversions

Academic literature on the topic 'Probabilistic inversions'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Probabilistic inversions"

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Bobe, Christin, Daan Hanssens, Thomas Hermans, and Ellen Van De Vijver. "Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic Data." Algorithms 13, no. 6 (June 18, 2020): 144. http://dx.doi.org/10.3390/a13060144.

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Often, multiple geophysical measurements are sensitive to the same subsurface parameters. In this case, joint inversions are mostly preferred over two (or more) separate inversions of the geophysical data sets due to the expected reduction of the non-uniqueness in the joint inverse solution. This reduction can be quantified using Bayesian inversions. However, standard Markov chain Monte Carlo (MCMC) approaches are computationally expensive for most geophysical inverse problems. We present the Kalman ensemble generator (KEG) method as an efficient alternative to the standard MCMC inversion approaches. As proof of concept, we provide two synthetic studies of joint inversion of frequency domain electromagnetic (FDEM) and direct current (DC) resistivity data for a parameter model with vertical variation in electrical conductivity. For both studies, joint results show a considerable improvement for the joint framework over the separate inversions. This improvement consists of (1) an uncertainty reduction in the posterior probability density function and (2) an ensemble mean that is closer to the synthetic true electrical conductivities. Finally, we apply the KEG joint inversion to FDEM and DC resistivity field data. Joint field data inversions improve in the same way seen for the synthetic studies.
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Manassero, M. C., J. C. Afonso, F. Zyserman, S. Zlotnik, and I. Fomin. "A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation." Geophysical Journal International 223, no. 3 (September 1, 2020): 1837–63. http://dx.doi.org/10.1093/gji/ggaa415.

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SUMMARY Simulation-based probabilistic inversions of 3-D magnetotelluric (MT) data are arguably the best option to deal with the nonlinearity and non-uniqueness of the MT problem. However, the computational cost associated with the modelling of 3-D MT data has so far precluded the community from adopting and/or pursuing full probabilistic inversions of large MT data sets. In this contribution, we present a novel and general inversion framework, driven by Markov Chain Monte Carlo (MCMC) algorithms, which combines (i) an efficient parallel-in-parallel structure to solve the 3-D forward problem, (ii) a reduced order technique to create fast and accurate surrogate models of the forward problem and (iii) adaptive strategies for both the MCMC algorithm and the surrogate model. In particular, and contrary to traditional implementations, the adaptation of the surrogate is integrated into the MCMC inversion. This circumvents the need of costly offline stages to build the surrogate and further increases the overall efficiency of the method. We demonstrate the feasibility and performance of our approach to invert for large-scale conductivity structures with two numerical examples using different parametrizations and dimensionalities. In both cases, we report staggering gains in computational efficiency compared to traditional MCMC implementations. Our method finally removes the main bottleneck of probabilistic inversions of 3-D MT data and opens up new opportunities for both stand-alone MT inversions and multi-observable joint inversions for the physical state of the Earth’s interior.
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Bredesen, Kenneth, Ian Herbert, Florian Smit, Ask Frode Jakobsen, Peter Frykman, and Anders Bruun. "Characterizing a Wedged Chalk Prospect in the Danish Central Graben Using Direct Probabilistic Inversion." Geosciences 12, no. 5 (April 29, 2022): 194. http://dx.doi.org/10.3390/geosciences12050194.

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A novel direct probabilistic inversion using seismic pre-stack data as input to characterize a wedged chalk reservoir prospect was demonstrated from the Upper Cretaceous unit, Danish North Sea. The objective was to better resolve the lateral extent and pinch-out of the chalk prospect in a frontier exploration setting and compare the results with a more traditional deterministic inversion and geostatistical reservoir modeling. The direct probabilistic inversion results provided additional reservoir insights that were challenging to obtain from the more traditional workflows and are also more flexible for associated uncertainty assessments. Hence, this study demonstrates the usefulness of such direct probabilistic inversions even with suboptimal data availability.
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Geng, Meixia, Xiangyun Hu, Henglei Zhang, and Shuang Liu. "3D inversion of potential field data using a marginalizing probabilistic method." GEOPHYSICS 83, no. 5 (September 1, 2018): G93—G106. http://dx.doi.org/10.1190/geo2016-0683.1.

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Probabilistic inversion methods have proven effective in solving many geophysical inverse problems. Structural orientation and spatial extent information can be efficiently incorporated the probabilistic inversion by the use of parameter covariances to produce a geologically realistic model. However, the use of a single model covariance matrix, with the underlying assumption of the presence of only one type of feature (e.g., similar size, shape, and orientation) in the subsurface, limits the ability of probabilistic inversions to recover geologically sound models. An approach based on marginalizing the probabilistic inversion is presented, which makes it possible to partition the inverse domain into various zones, each of which can have its own covariance matrix depending upon the features and/or depths of the sources. Moreover, a spatial gradient weighting function is introduced to enhance or attenuate the structural complexity in different zones. Thus, sources with different shapes, sizes, depths, and densities (or magnetic susceptibilities) can be simultaneously reconstructed. The sensitivity of the solutions to uncertainties in the a priori information, including the orientation, depth, and horizontal position as well as subdivision of the inversion domain, is analyzed. We found through synthetic examples and field data that the developed inversion method was a valid tool for exploration geophysics in presence of a priori geologic information.
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Rosa, Daiane R., Juliana M. C. Santos, Rafael M. Souza, Dario Grana, Denis J. Schiozer, Alessandra Davolio, and Yanghua Wang. "Comparing different approaches of time-lapse seismic inversion." Journal of Geophysics and Engineering 17, no. 6 (November 4, 2020): 929–39. http://dx.doi.org/10.1093/jge/gxaa053.

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Abstract Time-lapse (4D) seismic inversion aims to predict changes in elastic rock properties, such as acoustic impedance, from measured seismic amplitude variations due to hydrocarbon production. Possible approaches for 4D seismic inversion include two classes of method: sequential independent 3D inversions and joint inversion of 4D seismic differences. We compare the standard deterministic methods, such as coloured and model-based inversions, and the probabilistic inversion techniques based on a Bayesian approach. The goal is to compare the sequential independent 3D seismic inversions and the joint 4D inversion using the same type of algorithm (Bayesian method) and to benchmark the results to commonly applied algorithms in time-lapse studies. The model property of interest is the ratio of the acoustic impedances, estimated for the monitor, and base surveys at each location in the model. We apply the methods to a synthetic dataset generated based on the Namorado field (offshore southeast Brazil). Using this controlled dataset, we can evaluate properly the results as the true solution is known. The results show that the Bayesian 4D joint inversion, based on the amplitude difference between seismic surveys, provides more accurate results than sequential independent 3D inversion approaches, and these results are consistent with deterministic methods. The Bayesian 4D joint inversion is relatively easy to apply and provides a confidence interval of the predictions.
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Giraud, Jeremie, Mark Lindsay, Vitaliy Ogarko, Mark Jessell, Roland Martin, and Evren Pakyuz-Charrier. "Integration of geoscientific uncertainty into geophysical inversion by means of local gradient regularization." Solid Earth 10, no. 1 (January 25, 2019): 193–210. http://dx.doi.org/10.5194/se-10-193-2019.

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Abstract. We introduce a workflow integrating geological modelling uncertainty information to constrain gravity inversions. We test and apply this approach to the Yerrida Basin (Western Australia), where we focus on prospective greenstone belts beneath sedimentary cover. Geological uncertainty information is extracted from the results of a probabilistic geological modelling process using geological field data and their inferred accuracy as inputs. The uncertainty information is utilized to locally adjust the weights of a minimum-structure gradient-based regularization function constraining geophysical inversion. Our results demonstrate that this technique allows geophysical inversion to update the model preferentially in geologically less certain areas. It also indicates that inverted models are consistent with both the probabilistic geological model and geophysical data of the area, reducing interpretation uncertainty. The interpretation of inverted models reveals that the recovered greenstone belts may be shallower and thinner than previously thought.
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Pendrel, John, and Henk Schouten. "Facies — The drivers for modern inversions." Leading Edge 39, no. 2 (February 2020): 102–9. http://dx.doi.org/10.1190/tle39020102.1.

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It is common practice to make facies estimations from the outcomes of seismic inversions and their derivatives. Bayesian analysis methods are a popular approach to this. Facies are important indicators of hydrocarbon deposition and geologic processes. They are critical to geoscientists and engineers. The application of Bayes’ rule maps prior probabilities to posterior probabilities when given new evidence from observations. Per-facies elastic probability density functions (ePDFs) are constructed from elastic-log and rock-physics model crossplots, over which inversion results are superimposed. The ePDFs are templates for Bayesian analysis. In the context of reservoir characterization, the new information comes from seismic inversions. The results are volumes of the probabilities of occurrences of each of the facies at all points in 3D space. The concepts of Bayesian inference have been applied to the task of building low-frequency models for seismic inversions without well-log interpolation. Both a constant structurally compliant elastic trend approach and a facies-driven method, where models are constructed from per-facies trends and initial facies estimates, have been tested. The workflows make use of complete 3D prior information and measure and account for biases and uncertainties in the inversions and prior information. Proper accounting for these types of effects ensures that rock-physics models and inversion data prepared for reservoir property analysis are consistent. The effectiveness of these workflows has been demonstrated by using a Gulf of Mexico data set. We have shown how facies estimates can be effectively used to build reasonable low-frequency models for inversion, which obviate the need for well-log interpolation and provide full 3D variability. The results are more accurate probability-based net-pay estimates that correspond better to geology. We evaluate the workflows by using several measures including precision, confidence, and probabilistic net pay.
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Stähler, S. C., and K. Sigloch. "Fully probabilistic seismic source inversion – Part 1: Efficient parameterisation." Solid Earth Discussions 5, no. 2 (July 23, 2013): 1125–62. http://dx.doi.org/10.5194/sed-5-1125-2013.

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Abstract. Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters themselves, but also estimates of their uncertainties are of great practical importance. Probabilistic source inversion (Bayesian inference) is very adapted to this challenge, provided that the parameter space can be chosen small enough to make Bayesian sampling computationally feasible. We propose a framework for PRobabilistic Inference of Source Mechanisms (PRISM) that parameterises and samples earthquake depth, moment tensor, and source time function efficiently by using information from previous non-Bayesian inversions. The source time function is expressed as a weighted sum of a small number of empirical orthogonal functions, which were derived from a catalogue of >1000 STFs by a principal component analysis. We use a likelihood model based on the cross-correlation misfit between observed and predicted waveforms. The resulting ensemble of solutions provides full uncertainty and covariance information for the source parameters, and permits to propagate these source uncertainties into travel time estimates used for seismic tomography. The computational effort is such that routine, global estimation of earthquake mechanisms and source time functions from teleseismic broadband waveforms is feasible.
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Stähler, S. C., and K. Sigloch. "Fully probabilistic seismic source inversion – Part 1: Efficient parameterisation." Solid Earth 5, no. 2 (November 17, 2014): 1055–69. http://dx.doi.org/10.5194/se-5-1055-2014.

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Abstract. Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters themselves but also estimates of their uncertainties are of great practical importance. Probabilistic source inversion (Bayesian inference) is very adapted to this challenge, provided that the parameter space can be chosen small enough to make Bayesian sampling computationally feasible. We propose a framework for PRobabilistic Inference of Seismic source Mechanisms (PRISM) that parameterises and samples earthquake depth, moment tensor, and source time function efficiently by using information from previous non-Bayesian inversions. The source time function is expressed as a weighted sum of a small number of empirical orthogonal functions, which were derived from a catalogue of >1000 source time functions (STFs) by a principal component analysis. We use a likelihood model based on the cross-correlation misfit between observed and predicted waveforms. The resulting ensemble of solutions provides full uncertainty and covariance information for the source parameters, and permits propagating these source uncertainties into travel time estimates used for seismic tomography. The computational effort is such that routine, global estimation of earthquake mechanisms and source time functions from teleseismic broadband waveforms is feasible.
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Hauser, Juerg, James Gunning, and David Annetts. "Probabilistic inversion of airborne electromagnetic data under spatial constraints." GEOPHYSICS 80, no. 2 (March 1, 2015): E135—E146. http://dx.doi.org/10.1190/geo2014-0389.1.

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Probabilistic 1D inversions of airborne electromagnetic (AEM) surveys allow an exhaustive search of model space for each station, but they often assume that there is no spatial correlation between neighboring stations. This can result in abrupt transverse model discontinuities when attempting to construct a 3D model. In contrast to this, fully spatially regularized deterministic inversions can take spatial correlation between 1D models into account, but they do not explore the model space sufficiently to be able to evaluate model robustness. The Bayesian parametric bootstrap (BPB) approach that we developed is a practical compromise between computationally expensive exhaustive search techniques and computationally efficient deterministic inversions. Using a 1D kernel, we inverted for the interfaces, layer properties, and related uncertainties, taking lateral spatial correlations and additional prior information into account. Numerical examples revealed that a BPB technique was likely to explore the model space sufficiently for nonpathological situations. Using a subset of a large AEM survey collected in northwest Australia for aquifer mapping, we show how the BPB approach can be used to produce a spatially coherent map of the base of the Broome sandstone aquifer. The recovered uncertainties, which are likely to be one of the main sources of uncertainty in any groundwater model, exhibited the well-known increase in uncertainty of a depth to interface with increasing depth to the interface.
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Dissertations / Theses on the topic "Probabilistic inversions"

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Fu, Shuai. "Inversion probabiliste bayésienne en analyse d'incertitude." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00766341.

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Ce travail de recherche propose une solution aux problèmes inverses probabilistes avec des outils de la statistique bayésienne. Le problème inverse considéré est d'estimer la distribution d'une variable aléatoire non observée X a partir d'observations bruitées Y suivant un modèle physique coûteux H. En général, de tels problèmes inverses sont rencontrés dans le traitement des incertitudes. Le cadre bayésien nous permet de prendre en compte les connaissances préalables d'experts surtout avec peu de données disponibles. Un algorithme de Metropolis-Hastings-within-Gibbs est proposé pour approcher la distribution a posteriori des paramètres de X avec un processus d'augmentation des données. A cause d'un nombre élevé d'appels, la fonction coûteuse H est remplacée par un émulateur de krigeage (méta-modèle) H chapeau. Cette approche implique plusieurs erreurs de nature différente et, dans ce travail, nous nous attachons a estimer et réduire l'impact de ces erreurs. Le critère DAC a été proposé pour évaluer la pertinence du plan d'expérience (design) et le choix de la loi a priori, en tenant compte des observations. Une autre contribution est la construction du design adaptatif adapté a notre objectif particulier dans le cadre bayésien. La principale méthodologie présentée dans ce travail a été appliquée a un cas d' étude d'ingénierie hydraulique.
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Arnst, Maarten. "Inversion of probabilistic models of structures using measured transfer functions." Châtenay-Malabry, Ecole centrale de Paris, 2007. http://www.theses.fr/2007ECAP1037.

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L'objectif de la thèse est de développer une méthodologie d’identification expérimentale de modèles probabilistes qui prédisent le comportement dynamique de structures. Nous focalisons en particulier sur l’inversion de modèles probabilistes à paramétrage minimal, introduits par Soize, à partir de fonctions de transfert expérimentales. Nous montrons d’abord que les méthodes classiques d’estimation de la théorie des statistiques mathématiques, telle que la méthode du maximum de vraisemblance, ne sont pas bien adaptées pour aborder ce problème. En particulier, nous montrons que des difficultés numériques, ainsi que des problèmes conceptuels dus au risque d’une mauvaise spécification des modèles, peuvent entraver l’application des méthodes classiques. Ces difficultés nous motivent à formuler l’inversion de modèles probabilistes alternativement comme la minimisation, par rapport aux paramètres recherchés, d’une fonction objectif, mesurant une distance entre les données expérimentales et le modèle probabiliste. Nous proposons deux principes de construction pour la définition de telles distances, basé soit sur la fonction de logvraisemblance, soit l’entropie relative. Nous montrons comment la limitation de ces distances aux lois marginales d’ordre bas permet de surmonter les difficultés mentionnées plus haut. La méthodologie est appliquée à des exemples avec des données simulées et à un problème en ingénierie civile et environnementale avec des mesures réelles
The aim of this thesis is to develop a methodology for the experimental identification of probabilistic models for the dynamical behaviour of structures. The inversion of probabilistic structural models with minimal parameterization, introduced by Soize, from measured transfer functions is in particular considered. It is first shown that the classical methods of estimation from the theory of mathematical statistics, such as the method of maximum likelihood, are not well-adapted to formulate and solve this inverse problem. In particular, numerical difficulties and conceptual problems due to model misspecification are shown to prohibit the application of the classical methods. The inversion of probabilistic structural models is then formulated alternatively as the minimization, with respect to the parameters to be identified, of an objective function measuring a distance between the experimental data and the probabilistic model. Two principles of construction for the definition of this distance are proposed, based on either the loglikelihood function, or the relative entropy. The limitation of the distance to low-order marginal laws is demonstrated to allow to circumvent the aforementioned difficulties. The methodology is applied to examples featuring simulated data and to a civil and environmental engineering case history featuring real experimental data
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Diebel, James Richard. "Bayesian image vectorization : the probabilistic inversion of vector image rasterization /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Spikes, Kyle Thomas. "Probabilistic seismic inversion based on rock-physics models for reservoir characterization /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Shigapov, Renat [Verfasser], and T. [Akademischer Betreuer] Bohlen. "Probabilistic waveform inversion: Quest for the law / Renat Shigapov ; Betreuer: T. Bohlen." Karlsruhe : KIT-Bibliothek, 2019. http://d-nb.info/1179963989/34.

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Arnst, Maarten. "Inversion de modèles probabilistes de structures à partir de fonctionsde transfert expérimentales." Phd thesis, Ecole Centrale Paris, 2007. http://tel.archives-ouvertes.fr/tel-00238573.

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L'objectif de la thèse est de développer une méthodologie d'identification expérimentale de modèles probabilistes qui prédisent le comportement dynamique de structures. Nous focalisons en particulier sur l'inversion de modèles probabilistes à paramétrage minimal, introduits par Soize, à partir de fonctions de transfert expérimentales. Nous montrons d'abord que les méthodes classiques d'estimation de la théorie des statistiques mathématiques, telle que la méthode du maximum de vraisemblance, ne sont pas bien adaptées pour aborder ce problème. En particulier, nous montrons que des difficultés numériques, ainsi que des problèmes conceptuels dus au risque d'une mauvaise spécification des modèles, peuvent entraver l'application des méthodes classiques. Ces difficultés nous motivent à formuler l'inversion de modèles probabilistes alternativement comme la minimisation, par rapport aux paramètres recherchés, d'une fonction objectif, mesurant une distance entre les données expérimentales et le modèle probabiliste. Nous proposons deux principes de construction pour la définition de telles distances, basé soit sur la fonction de logvraisemblance, soit l'entropie relative. Nous montrons comment la limitation de ces distances aux lois marginales d'ordre bas permet de surmonter les difficultés mentionnées plus haut. La méthodologie est appliquée à des exemples avec des données simulées et à un problème en ingénierie civile et environnementale avec des mesures réelles.
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Kozlovskaya, E. (Elena). "Theory and application of joint interpretation of multimethod geophysical data." Doctoral thesis, University of Oulu, 2001. http://urn.fi/urn:isbn:9514259602.

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Abstract This work is devoted to the theory of joint interpretation of multimethod geophysical data and its application to the solution of real geophysical inverse problems. The targets of such joint interpretation can be geological bodies with an established dependence between various physical properties that cause anomalies in several geophysical fields (geophysical multiresponse). The establishing of the relationship connecting the various physical properties is therefore a necessary first step in any joint interpretation procedure. Bodies for which the established relationship between physical properties is violated (single-response bodies) can be targets of separate interpretations. The probabilistic (Bayesian) approach provides the necessary formalism for addressing the problem of the joint inversion of multimethod geophysical data, which can be non-linear and have a non-unique solution. Analysis of the lower limit of resolution of the non-linear problem of joint inversion using the definition of e-entropy demonstrates that joint inversion of multimethod geophysical data can reduce non-uniqueness in real geophysical inverse problems. The question can be formulated as a multiobjective optimisation problem (MOP), enabling the numerical methods of this theory to be employed for the purpose of geophysical data inversion and for developing computer algorithms capable of solving highly non-linear problems. An example of such a problem is magnetotelluric impedance tensor inversion with the aim of obtaining a 3-D resistivity distribution. An additional area of application for multiobjective optimisation can be the combination of various types of uncertain information (probabilistic and non-probabilistic) in a common inversion scheme applicable to geophysical inverse problems. It is demonstrated how the relationship between seismic velocity and density can be used to construct an algorithm for the joint interpretation of gravity and seismic wide-angle reflection and refraction data. The relationship between the elastic and electrical properties of rocks, which is a necessary condition for the joint inversion of data obtained by seismic and electromagnetic methods, can be established for solid- liquid rock mixtures using theoretical modelling of the elastic and electrical properties of rocks with a fractal microstructure and from analyses of petrophysical data and borehole log data.
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Ben, Hmida Sahar. "Inversion des formes d'ondes LiDAR pour l'estimation des caractéristiques des cultures et des forêts par des techniques probabilistes et variationnelles." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30303/document.

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L'utilisation du LiDAR en télédétection permet une description précise de l'architecture du couvert végétal. L'objet de cette thèse est le développement des approches d'inversion de mesures LiDAR à l'aide d'une modélisation physique et statistique du signal dans le but d'estimer des propriétés biophysiques de cultures dominantes (blé, maïs) du Sud-Ouest de la France et d'un couvert forestier en Chine. Le travail a tout d'abord porté sur l'estimation du LAI et la hauteur des cultures par inversion de formes d'onde LiDAR à faible empreinte. Une base de données de simulations de formes d'onde réalistes des cultures est réalisée à l'aide du modèle de transfert radiatif (MTR) DART. L'inversion consiste à utiliser la technique de table de correspondance qui consiste à chercher la simulation la plus proche de l'observation réelle. Le travail a ensuite porté sur l'estimation du profil de LAI des arbres de la forêt. Une approche variationnelle d'estimation du profil de LAI par inversion de formes d'ondes est proposée. Elle repose sur un MTR simplifié et une technique de lissage du profil de LAI s'appuyant sur les chaines de Markov. La formulation bayésienne du problème, nous amène à une fonction de coût non-linéaire. Elle est minimisée à l'aide d'une nouvelle technique de gradient multi-échelle. Les approches développées montrent bien leurs performances en les appliquant sur des données réelles de cultures (maïs et blé) et de milieu forestier
The use of LiDAR in remote sensing allows a precise description of the vegetation cover architecture. The aim of this thesis is the development of LiDAR data inversion approaches using physical and statistical signal modeling in order to estimate the biophysical properties of dominant crops (wheat, maize) of the South-West of France and a forest cover in China. The work firstly focused on estimating LAI and crop height by small footprint LiDAR waveforms inversion. A realistic crop waveform simulations database is performed using the Radiative Transfer Model (MTR) DART. The inversion consists in using the Look up Table technique which consists of looking for the closest simulation to the actual observation. The second inversion approach focused on LAI profile estimation of the forest trees. A variational approach to estimate LAI by waveform inversion is proposed. It relies on a simplified MTR and LAI profile smoothing technique based on Markov chains. The Bayesian formulation of the problem leads us to a non-linear cost function. It is minimized using a new multi-scale gradient technique. The developed approaches show clearly their performance by applying them to real crop data (corn and wheat) and forest
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Berrada, Mohamed. "Une approche variationnelle de l'inversion : de la recherche locale à la recherche globale par carte topologique : application en inversion géoacoustique." Paris 6, 2008. http://www.theses.fr/2008PA066016.

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La propagation acoustique dans la colonne d'eau peu profond et à basse fréquence dépend fortement des caractéristiques géoacoustiques du sous sol marin et de la colonne d'eau. Il s'agit dans cette thèse d'élaborer un modèle d'inversion variationnelle des données géoacoustiques permettant de retrouver ces caractéristiques d'une manière précise. Les méthodes d'inversion variationnelles consistent à introduire des fonctions coûts qui mesure le degré d'adaptation entre les observations et leurs équivalentes modèle. Notre modèle d'inversion est basé sur deux approches: une approche modulaire du calcul de l'adjoint du modèle de propagation direct, nécessaire pour calculer le gradient des fonctions coûts, et une approche neuronale, ou plus particulièrement les cartes topologiques, permettant une recherche aussi globale que locale. Nous étudions d'abord l'approche modulaire que nous allons appliquer en inversion géoacoustique sur des données synthétiques, puis une validation dans le cadre d'une expérience avec des données réelles sera présentée. Nous présentons ensuite une méthode d'inversion globale basée sur un modèle d'ACP probabiliste et les cartes topologiques. Les tests de validation de cette méthode, en inversion géoacoustique sur des données synthétiques bruitées, montrent son efficacité.
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Bletery, Quentin. "Analyse probabiliste et multi-données de la source de grands séismes." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4092/document.

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Les séismes sont le résultat de glissements rapides le long de failles actives chargées en contraintes par le mouvement des plaques tectoniques. Il est aujourd'hui établi, au moins pour les grands séismes, que la distribution de ce glissement rapide le long des failles pendant les séismes est hétérogène. Imager la complexité de ces distributions de glissement constitue un enjeu majeur de la sismologie en raison des implications potentielles dans la compréhension de la genèse des séismes et la possibilité associée de mieux anticiper le risque sismique et les tsunamis. Pour améliorer l'imagerie de ces distributions de glissement co-sismique, trois axes peuvent être suivis: augmenter les contraintes sur les modèles en incluant plus d'observations dans les inversions, améliorer la modélisation physique du problème direct et progresser dans le formalisme de résolution du problème inverse. Dans ce travail de thèse, nous explorons ces trois axes à travers l'étude de deux séismes majeurs: les séisme de Tohoku-Oki (Mw 9.0) et de Sumatra-Andaman (Mw 9.1-9.3) survenus en 2011 et 2004, respectivement
Earthquakes are the results of rapid slip on active faults loaded in stress by the tectonic plates motion. It is now establish - at least for large earthquakes - that the distribution of this rapid slip along the rupturing faults is heterogeneous. Imaging the complexity of such slip distributions is one the main challenges in seismology because of the potential implications on understanding earthquake genesis and the associated possibility to better anticipate devastating shaking and tsunami. To improve the imaging of such co-seismic slip distributions, three axes may be followed: increase the constraints on the source models by including more observations into the inversions, improve the physical modeling of the forward problem and improve the formalism to solve the inverse problem. In this PhD thesis, we explore these three axes by studying two recent major earthquakes: the Tohoku-Oki (Mw 9.0) and Sumatra-Andaman (Mw 9.1-9.3) earthquakes, which occured in 2011 and 2004 respectively
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Book chapters on the topic "Probabilistic inversions"

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Demoment, Guy, and Yves Goussard. "Inversion within the Probabilistic Framework." In Bayesian Approach to Inverse Problems, 59–78. London, UK: ISTE, 2010. http://dx.doi.org/10.1002/9780470611197.ch3.

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Hadidi, Rambod, and Nenad Gucunski. "Probabilistic Inversion: A New Approach to Inversion Problems in Pavement and Geomechanical Engineering." In Intelligent and Soft Computing in Infrastructure Systems Engineering, 21–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04586-8_2.

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Herkersdorf, Andreas, Michael Engel, Michael Glaß, Jörg Henkel, Veit B. Kleeberger, Johannes M. Kühn, Peter Marwedel, et al. "RAP Model—Enabling Cross-Layer Analysis and Optimization for System-on-Chip Resilience." In Dependable Embedded Systems, 1–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52017-5_1.

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AbstractThe Resilience Articulation Point (RAP) model aims to provision a probabilistic fault abstraction and error propagation concept for various forms of variability related faults in deep sub-micron CMOS technologies at the semiconductor material or device levels. RAP assumes that each of such physical faults will eventually manifest as a single- or multi-bit binary signal inversion or out-of-specification delay in a signal transition between bit values. When probabilistic error functions for specific fault origins are known at the bit or signal level, knowledge about the unit of design and its environment allow the transformation of the bit-related error functions into characteristic higher layer representations, such as error functions for data words, finite state machine (FSM) states, IP macro-interfaces, or software variables. Thus, design concerns can be investigated at higher abstraction layers without the necessity to further consider the full details of lower levels of design. This chapter introduces the ideas of RAP based on examples of particle strike, noise and voltage drop induced bit errors in SRAM cells. Furthermore, we show by different examples how probabilistic bit flips are systematically abstracted and propagated towards instruction and data vulnerability at MPSoC architecture level, and how RAP can be applied for dynamic testing and application-level optimizations in an autonomous robot scenario.
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McLaughlin, Dennis. "A probabilistic perspective on nonlinear model inversion and data assimilation." In Subsurface Hydrology: Data Integration for Properties and Processes, 243–53. Washington, D. C.: American Geophysical Union, 2007. http://dx.doi.org/10.1029/171gm17.

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"Probabilistic Inversion." In Uncertainty Analysis with High Dimensional Dependence Modelling, 239–68. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470863072.ch9.

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"Probabilistic inversion techniques for uncertainty analysis." In Probabilistic Risk Analysis, 316–25. Cambridge University Press, 2001. http://dx.doi.org/10.1017/cbo9780511813597.017.

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"Probabilistic derivation of inversion formulas." In Probability Theory and Applications, 797–802. De Gruyter, 1987. http://dx.doi.org/10.1515/9783112314227-109.

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"Smoothing and Inversion Inequalities." In Analytic and Probabilistic Methods in Number Theory, 111–20. De Gruyter, 1992. http://dx.doi.org/10.1515/9783112314234-015.

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"Stated Preference Analysis Using Probabilistic Inversion (PSAM-0354)." In Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM), 1503–11. ASME Press, 2006. http://dx.doi.org/10.1115/1.802442.paper187.

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Giacovazzo, Carmelo. "The probabilistic estimation of triplet and quartet invariants." In Phasing in Crystallography. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199686995.003.0010.

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This chapter describes how to estimate, by probabilistic approaches, triplet and quartet invariants from diffraction magnitudes. We will skip quintet (Fortier and Hauptman, 1977a,b,c; Hauptman and Fortier, 1977a,b; Van der Putten and Schenk, 1977; Giacovazzo, 1977b, 1980; Burla et al., 1977) and higher-order s.i.s, because their usefulness in modern phasing procedures is entirely marginal. For simplicity, we will also skip the mathematics necessary to obtain conclusive formulas (the general approach is described in Appendix 4.A), except for the triplet invariants, first representation, because of their prominent role. Triplet and quartet estimates will be discussed, particularly in relation to their impact on phasing procedures. For simplicity, some other specialized topics will also be skipped, even if theoretically relevant. For example: results obtained by Shmueli and Weiss (1986, 1992), who used Fourier series representations of joint probability density functions to estimate triplets; the effect of pseudotranslational symmetry on the triplet phase estimates, as described by Cascarano et al. (1985a,b, 1987b, 1988a,b); algebraic formulas obtained by Karle and Hauptman (1957), Vaughan (1958), Hauptman et al. (1969), Hauptman (1970), Fischer et al. (1970a,b), all related to (and encompassed by) the estimation of triplet phases via their second representation. Interested readers are referred to the original papers. Let us first consider the space group P1. According to Chapter 4, the simplest way to estimate the triplet s.i. . . . Φ = φh1 + φh2 + φh3 with h1 + h2 + h3 = 0 (5.1) . . . is to study the joint probability distribution . . . P(Eh1 , Eh2 , Eh3 ) ≡ P(Rh1 , Rh2 , Rh3 , φh1 , φh2 , φh3 ). (5.2) . . . According to Section 4.1 we must first calculate the characteristic function C and then, by Fourier inversion, recover the distribution (5.2). Because of the importance of the triplet invariant, we report the necessary calculations in Appendix 5.A.
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Conference papers on the topic "Probabilistic inversions"

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Harris, P., and O. Kolbjørnsen. "Comparing probabilistic inversions: understanding robustness and sensitivity." In Second EAGE Conference on Seismic Inversion. European Association of Geoscientists & Engineers, 2022. http://dx.doi.org/10.3997/2214-4609.202229023.

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Ndingwan, A. O., J. A. Haugen, K. R. Straith, A. K. Evensen, and O. Kolbjørnsen. "A complex simplicity of probabilistic 3.5D seismic inversion." In Second EAGE Conference on Seismic Inversion. European Association of Geoscientists & Engineers, 2022. http://dx.doi.org/10.3997/2214-4609.202229021.

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Jakobsen, A. F., and H. J. Hansen. "Direct Probabilistic Inversion for Facies Using Zoeppritz Reflectivity Model." In First EAGE Conference on Seismic Inversion. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202037034.

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Aker, E., J. Thiebaud, and P. Røe. "Probabilistic AVO Inversion of Transversely Isotropic Medium for Better Characterization of North Sea Oxfordian Turbidite Reservoir." In First EAGE Conference on Seismic Inversion. European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.202037010.

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Linde, N., M. Cardiff, G. Mariethoz, J. Bradford, and G. Pirot. "Towards 3D Probabilistic Inversion with Graphcuts." In 23rd European Meeting of Environmental and Engineering Geophysics. Netherlands: EAGE Publications BV, 2017. http://dx.doi.org/10.3997/2214-4609.201702084.

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Hadidi, Rambod, and Nenad Gucunski. "Probabilistic Approach to Seismic Waveform Inversion." In Symposium on the Application of Geophysics to Engineering and Environmental Problems 2007. Environment and Engineering Geophysical Society, 2007. http://dx.doi.org/10.4133/1.2924760.

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Connolly, Patrick, and Mark O'Brien. "Probabilistic seismic inversion using pseudo-wells." In SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists, 2017. http://dx.doi.org/10.1190/segam2017-17565685.1.

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Hadidi, Rambod, and Nenad Gucunski. "Probabilistic Approach To Seismic Waveform Inversion." In 20th EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems. European Association of Geoscientists & Engineers, 2007. http://dx.doi.org/10.3997/2214-4609-pdb.179.0959-968.

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Goodway, B., R. Cova, M. Perez, E. Mutual, W. Pardasie, A. JaKobsen, and H. Hansen. "Direct Probabilistic Inversion: Adding Back Geology into Geophysics Through Probabilistic Inversion Extended to Anisotropic Quantitative Interpretation." In First EAGE Workshop on East Canada Offshore Exploration. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202186014.

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Hansen, H. J., and A. F. Jakobsen. "Local probabilistic inversion of seismic AVO data." In 80th EAGE Conference & Exhibition 2018 Workshop Programme. Netherlands: EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201801888.

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Reports on the topic "Probabilistic inversions"

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Symington, N. J., A. M. Haiblen, A. Ray, S. J. Tickell, and L. J. Gow. Exploring for the Future —Remodelling the Oolloo–Jinduckin interface across the Daly Basin: Interpreting stratigraphic boundaries using probabilistic airborne electromagnetic inversions. Geoscience Australia, 2021. http://dx.doi.org/10.11636/record.2021.004.

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