We characterize key features of subsurface flow paths relying on an energetic and probabilistic perspective. We consider subsurface flow in a free aquifer system as mainly ruled by gravity, the latter acting as the key driving force. Therefore, we study groundwater circulation relying upon stochastic simulations of aquifer bottom topography inferred from stratigraphic observations. Upon resting on the concept of optimal channel networks, we identify Preferential Groundwater Networks (PGNs) as spatially organized structures carved by locally following the steepest gradient associated with the aquifer bottom topography. A probabilistic description of PGNs is obtained by reconstructing the aquifer bottom topography as a spatial random field conditional on the available information, and using diverse area threshold values for PGNs delineation. We find that PGNs inferred from the (ensemble) averaged bottom topography with the highest area threshold considered are strikingly consistent with main flow directions and key subsurface flow patterns inferred from available piezometric data. The probabilistic distribution of PGNs is also consistent with geological and hydrogeological information at our disposal, such as geological data (and ensuing hydrogeological sections), and is coherent with the nature of the aquifers investigated.

Probabilistic identification of Preferential Groundwater Networks

Massimiliano Schiavo;Monica Riva;Laura Guadagnini;Alberto Guadagnini
2022-01-01

Abstract

We characterize key features of subsurface flow paths relying on an energetic and probabilistic perspective. We consider subsurface flow in a free aquifer system as mainly ruled by gravity, the latter acting as the key driving force. Therefore, we study groundwater circulation relying upon stochastic simulations of aquifer bottom topography inferred from stratigraphic observations. Upon resting on the concept of optimal channel networks, we identify Preferential Groundwater Networks (PGNs) as spatially organized structures carved by locally following the steepest gradient associated with the aquifer bottom topography. A probabilistic description of PGNs is obtained by reconstructing the aquifer bottom topography as a spatial random field conditional on the available information, and using diverse area threshold values for PGNs delineation. We find that PGNs inferred from the (ensemble) averaged bottom topography with the highest area threshold considered are strikingly consistent with main flow directions and key subsurface flow patterns inferred from available piezometric data. The probabilistic distribution of PGNs is also consistent with geological and hydrogeological information at our disposal, such as geological data (and ensuing hydrogeological sections), and is coherent with the nature of the aquifers investigated.
2022
Groundwater
Monte Carlo simulations
Uncertainty quantification
Porous media
Geostatistics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1228021
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