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| Created: | Aug 16, 2022 at 3:13 p.m. | |
| Last updated: | Sep 13, 2023 at 9:34 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Discoverable (Accessible via direct link sharing) |
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Abstract
This project aims to develop a new methodology to integrate geostatistical methods with hydro- geophysical measurements in conjunction with terrain and conventional hydrologic observations to evaluate aquifer recharge dynamics in groundwater recharge areas.A key limitation of most hydrologic models is that they do not provide reliable quantification of the uncertainty in the model predictions associated to subsurface parameters that govern water movement. We propose a geostatistical methodology based on Bayes theory to predict the spatial distributions of the main variables that control the water supply, such as porosity and water saturation. The model variables generally vary in space and time and cannot be measured directly in the subsurface, except for at a limited number of locations.
The dataset focuses on two types of hydrogeophysical measurements: electrical resistivity and seismic velocities. Electrical resistivity is sensitive to the ability of soils to conduct electric current. Using a geophysical method called electrical resistivity tomography (ERT), we can image electrical properties to several meters in the subsurface including the unsaturated zone, water table, and the upper portion of the aquifer. When the measurement is made over time (e.g. daily), changes in water content are the primary driver of electrical property variations. During an ERT survey, an array of electrodes is measured in sets of four (quadrupoles). Different from resistivity, seismic velocity is sensitive to the elasticity of the rock that depend the pore volume of the saturated rocks as well as the volumetric fractions of the solid and fluid components of the rock. Using a geophysical method called seismic refraction imaging, we can image elastic properties in the subsurface to determine porosity, depth to fresh bedrock, and are an input into the rock physics inversion.
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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