Showing 3 results for Porosity
Volume 3, Issue 1 (11-2009)
Abstract
(Paper pages 543-562) When two solutions are mixed, concentrations in the mixture are volume-weighted averages of the two end-members, but the thermodynamic activities of the species controlling the water–mineral reactions are non-linear functions of the mixing ratio. Therefore, two end member solutions in equilibrium with carbonate phase could lead to a mixture undersaturated with respect to carbonate. A favorite place for this phenomenon is water table, where mixing of different waters is taking place. In this paper, Porosity change in freshwater lens of an island was calculated by coupling dissolution potential with a variable density flow and solute transport model. The effect of permeability enhancement on the rate of porosity change was evaluated. Dissolution due to this mixing takes place in water table and active edge of freshwater lens (40m from coastline). The results indicate an increase rate of 0.6×10-3 percent of porosity per year. Permeability enhancement increases the rate of dissolution and porosity change in fresh water lens.
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Volume 15, Issue 3 (12-2021)
Abstract
Tahereh Azari,
Volume 19, Issue 1 (6-2025)
Abstract
Accurately determining hydraulic parameter values is the first step in sustainably developing an aquifer. Since Theis (1935) introduced the type curve matching technique (TCMT), it has been used to estimate aquifer parameters from pumping test data. However, the TCMT is subject to graphical error. To eliminate this error, a multi-layer perceptron (MLP) artificial neural network (ANN) was developed as an alternative to the conventional TCMT. This MLP ANN models the Bourdet-Gringaten well function to determine fractured double porosity aquifer parameters. The MLP model was developed using a four-step protocol and trained using the backpropagation method and the Levenberg-Marquardt optimization algorithm for the well function of double-porosity aquifers. Through a trial-and-error procedure and by applying principal component analysis (PCA) to the training input data, the optimal network structure with the topology [3×6×3] is determined. We evaluated the validity of the developed network with synthetic and real field data. The network receives pumping test data and provides the user with aquifer parameter values. This network provides an automatic, fast procedure for determining double-porosity aquifer parameters, eliminating the graphical errors inherent in the conventional TCMT.
