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Maalouf, Eliane

Nom
Maalouf, Eliane
Affiliation principale
Site web
Fonction
Assistante-Doctorante
Email
eliane.maalouf@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/1120
_
0000-0003-1549-7948

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  • Publication
    Accès libre
    Fast ABC with joint generative modelling and subset simulation
    (2022)
    Maalouf, Eliane 
    ;
    David Ginsbourger
    ;
    Niklas Linde
    We propose a novel approach for solving inverse-problems with high-dimensional inputs and an expensive forward mapping. It leverages joint deep generative modelling to transfer the original problem spaces to a lower dimensional latent space. By jointly modelling input and output variables and endowing the latent with a prior distribution, the fitted probabilistic model indirectly gives access to the approximate conditional distributions of interest. Since model error and observational noise with unknown distributions are common in practice, we resort to likelihood-free inference with Approximate Bayesian Computation (ABC). Our method calls on ABC by Subset Simulation to explore the regions of the latent space with dissimilarities between generated and observed outputs below prescribed thresholds. We diagnose the diversity of approximate posterior solutions by monitoring the probability content of these regions as a function of the threshold. We further analyze the curvature of the resulting diagnostic curve to propose an adequate ABC threshold. When applied to a cross-borehole geophysical example, our approach delivers promising performance without using prior knowledge of the forward nor of the noise distribution.
  • Publication
    Accès libre
    Approximate Bayesian Geophysical Inversion using Generative Modeling and Subset Simulation
    (2020)
    Maalouf, Eliane 
    ;
    David Ginsbourger
    ;
    Niklas Linde
    We present preliminary work on solving geophysical inverse problems by exploring the latent space of a joint Generative Neural Network (GNN) model by Approximate Bayesian Computation (ABC) based on Subset Simulation (SuS). Given pre-generated subsurface domains and their corresponding solver outputs, the GNN surrogates the forward solver during inversion to quickly explore the input space through SuS and locate regions of credible solutions. Akin to ABC methods, our methodology allows to tune the similarity threshold between observed and candidate outputs. We explore how tuning this threshold influences the uncertainty in the solutions, allowing to sample solutions with a selected diversity level. Our initial tests were carried out with data from straight-ray (linear) tomography with Gaussian priors on slowness fields and Gaussian versus Gumbel observation noise distributions. We are presently testing the methodology on non-linear physics to demonstrate its applicability in more general inversion settings.