Abstract
Inverse geochemical modeling of groundwater entails identifying a set of geochemical reactions which can explain observed changes in water chemistry between two samples that are spatially related in some sense, such as two points along a flow pathway. A common inversion approach is to solve a set of simultaneous mass and electron balance equations involving water-rock and oxidation-reduction reactions that are consistent with the changes in concentrations of various aqueous components. However, this mass-balance approach does not test the thermodynamic favorability of the resulting model and provides limited insight into the model uncertainties. In this context, a Monte Carlo-based forward-inverse modeling method is proposed that generates probability distributions for model parameters which best match the observed data using the Metro-polis-Hastings search strategy. The forward model is based on the well-vetted PHREEQC geochemical model. The proposed modeling approach is applied to two test applications, one involving an inverse modeling example supplied with the PHREEQC code that entails groundwater interactions with a granitic rock mineral assemblage, and the other concerning the impact of fuel hydrocarbon bioattenuation on groundwater chemistry. In both examples, the forward-inverse approach is able to approximately reproduce observed water quality changes invoking mass transfer reactions that are all thermodynamically favorable.
Highlights
Inverse process models attempt to estimate model parameter values based on changes in observed data between two or more data sets
The inverse modeling capability of the PHREEQC code itself suggests that the compositional differences between the two waters can be largely explained by additional dissolution of CO2, calcite, in the perennial spring water, coupled with the precipitation of kaolinite and silica
For the geochemical inverse modeling approach proposed in this study, the forward model PHREEQC has already been well vetted elsewhere in the literature
Summary
Inverse process models attempt to estimate model parameter values based on changes in observed data between two or more data sets (in contrast to forward process models, which predict the values of variables based on assumed model parameters) In this context, the compositional differences between two groundwater samples—an. W. McNab Jr. initial water and a final water, should contain the information necessary for an inverse model to unravel candidate causative reaction and mixing histories. Initial water and a final water, should contain the information necessary for an inverse model to unravel candidate causative reaction and mixing histories The results of such an inverse model may not necessarily be a unique problem exacerbated by uncertainties associated with the groundwater chemistry data itself (e.g., analytical uncertainty, representativeness of samples, incomplete analyses suites). 5.0E-03 4.5E-03 4.0E-03 3.5E-03 3.0E-03 2.5E-03 2.0E-03 1.5E-03 1.0E-03 5.0E-04 0.0E+00
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