[en] The flow of water in many aquifers is driven by strong anisotropy created by preferential flow features such as cracks and faults. This anisotropy can be modeled by introducing the anisotropic hydraulic conductivity (AHC) tensor into the equations of poroelasticity. Our overall goal is to assimilate Interferometric Synthetic Aperture Radar (InSAR) remote sensing data into a model of an aquifer system in order to infer information about AHC.
In this work we develop a flexible stochastic prior model of the AHC tensor that respects its underlying symmetry and positive definiteness. Our method for calibrating and constructing a random AHC tensor involves three steps: 1) fitting a Bayesian model with circular von Mises distributions to fracture outcrop data, 2) fitting a Bayesian model of two independent log-normal distributions to hydraulic conductivity estimates, and 3) feeding these stochastic models into an extended version of [1] model to construct random symmetric positive definite tensors using spectral decomposition to encode size and orientation separately.
Disciplines :
Earth sciences & physical geography
Author, co-author :
SALEHIAN GHAMSARI, Sona ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
VAN DAM, Tonie ; University of Utah > Department of Geology and Geophysics > College of Mines and Earth Sciences
HALE, Jack ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
External co-authors :
yes
Language :
English
Title :
Towards assimilating InSAR data into a model of a highly anisotropic aquifer system
Original title :
[en] Towards assimilating InSAR data into a model of a highly anisotropic aquifer system
Publication date :
06 June 2025
Number of pages :
2
Event name :
International Symposium on Computational Sensing
Event place :
Clervaux, Luxembourg
Event date :
4-6 June 2025
Audience :
International
References of the abstract :
[1] S. K. Shivanand, B. Rosi´c, and H. G. Matthies, “Stochastic modelling
of symmetric positive definite material tensors,” Journal of
Computational Physics, vol. 505, p. 112883, 2024.
Focus Area :
Computational Sciences
Development Goals :
15. Life on land
FnR Project :
FNR12252781 - DRIVEN - Data-driven Computational Modelling And Applications, 2017 (01/09/2018-28/02/2025) - Andreas Zilian
Funders :
FNR - Fonds National de la Recherche
Funding text :
This work was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant reference PRIDE/17/12252781. For the purposes of open access, and in fulfilment of the obligations arising from the grant agreement, the authors have applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission.
