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See detailTowards assimilating SAR data into an anisotropic model of an underground aquifer
Salehian Ghamsari, Sona UL; van Dam, Tonie UL; Hale, Jack UL

Scientific Conference (2023)

In this study, we aim to shed light on the feasibility of assimilating synthetic aperture radar (SAR) data into a partial differential equation-based model of a poroelastic homogeneous aquifer with ... [more ▼]

In this study, we aim to shed light on the feasibility of assimilating synthetic aperture radar (SAR) data into a partial differential equation-based model of a poroelastic homogeneous aquifer with anisotropic hydraulic conductivity (AHC). Although other authors [1] have considered the problem of assimilating SAR data into a poroelastic model that uses an inhomogeneous isotropic random field model for hydraulic conductivity, to the best of our knowledge, our study is the first to consider assimilating SAR data into a poroelastic model with AHC. Our study is inspired by the work of [2] where an aquifer test is performed on the Anderson Junction aquifer in southwestern Utah. Due to the inherent preferential direction of the fractured sandstone at the Anderson Junction site, the ratio of hydraulic conductivity along the principal axes can be on the order of 24 to 1. We build an anisotropically conductive poroelastic finite element model of the Anderson Junction site that can predict the coupled fluid flow and mechanical displacements. Our results show that the effective elastic response of the aquifer on the Earth’s surface has an anisotropic nature driven by the underlying anisotropy in the fluid problem, even when the elasticity problem is assumed to be isotropic. We interpret these results in the context of using SAR data to improve the characterization of aquifer systems, like the Anderson Junction site, with strongly anisotropic behavior. The Doctoral Training Unit Data-driven computational modelling and applications (DRIVEN) is funded by the Luxembourg National Research Fund under the PRIDE programme (PRIDE17/12252781). [1] Amal Alghamdi. Bayesian inverse problems for quasi-static poroelasticity with application to ground water aquifer characterization from geodetic data. PhD thesis, 2020. https://repositories.lib.utexas.edu/handle/2152/86231. [2] Victor M. Heilweil and Paul A. Hsieh. Determining Anisotropic Transmissivity Using a Simplified Papadopulos Method. Groundwater, 44(5):749–753, 2006. 10.1111/j.1745-6584.2006.00210.x [less ▲]

Detailed reference viewed: 105 (23 UL)
See detailInSAR for climate change
Salehian Ghamsari, Sona UL

Presentation (2022, March 22)

Did you know that 50% of our drinking and irrigation water comes from underneath the earth? Understanding and managing these water resources is critically important as we prepare to tackle challenges such ... [more ▼]

Did you know that 50% of our drinking and irrigation water comes from underneath the earth? Understanding and managing these water resources is critically important as we prepare to tackle challenges such as population growth and man-made climate change in the 21st century. The goal of my research is to combine data from satellites with computer models to help us understand more about the groundwater on our planet. The surface of the earth moves all of the time, for example, when two tectonic plates move during an earthquake. But did you know that the surface of the earth also moves up and down all of the time depending on how wet it is? You can think of the earth as being a bit like a sponge. It is elastic, meaning that when it is loaded it deforms, and when it is unloaded it returns to the same position. It is also porous, so water can flow and be stored in small spaces inside the rock. Elasticity and porosity are coupled, so when we squeeze the rock, water flows out of it. The mathematical theory of poroelasticity gives us a model that can help us predict this movement. By using this theory we can build a model of the flow of water in the Earth and underground deformation on a computer. But to make this model useful we need real data about the earth as input. Incredibly, modern satellite systems can give us data about how the surface of the earth moves to centimetre or even millimetre accuracy. Every six days a satellite from the Sentinel-1 mission passes over Belval, and every other place on the planet. The satellite sends down a burst of radar that reflects off the Earth’s surface, and the satellite receives this information back. By comparing two radar signals, we can precisely measure the distance by which the ground has risen, or sunk. So we can produce data describing the surface displacement everywhere and every time. By combining the data from the satellite with the computer model, we will be able to reveal the hidden world of water under our feet. [less ▲]

Detailed reference viewed: 130 (11 UL)