GROUNDWATER MODELING & VISUALIZATION
Figure 3. Flowchart (left) and associated playlist (right) depicting strategy for creating time series models of groundwater contaminant migrating laterally through unconsolidated overburden, downwards through epikarst, and into fractured carbonates.
than 7.2 ppb (parts per billion), depths to contamina- tion greater than 1,900 ppb, and depths to the maximum 1,4-dioxane concentrations (Figure 1B). These grids were subsequently imported into the EGLE online GIS system which allows users to quickly determine the 1,4-dioxane contamination risk for any point within the project area.
10. Creating Time-Based Animations: The lithology model and annual geochemical models were combined the 1,4-dioxane plume as it migrated and diluted or in- creased from 1986 to 2020 (Figure 2H). This video can be viewed at
https://youtu.be/r9ITUVXhNMI.
The aforementioned models were used to create cross-sec- tions that were reviewed in an iterative fashion as new data became available and bad data was corrected. This meant that all models and animations had to be re-generated with each change of the data. Fortunately, the automation pro- vided by the playlist (Figure 1C) proved to be indispensable.
Strategy #2 - Groundwater Contamination Within an Epikarst Environment
In this example, trichloroethylene (TCE) contamination was sampled on a quarterly basis over a one-year time period as it percolated laterally through unconsolidated overbur- den, then downward through an epikarst zone, and into an
www.aipg.org
underlying fractured carbonate. Unlike the glacial outwash example, the applied strategy is illustrated by a “pseudo case- study” meaning proprietary data that has been obfuscated at the request of the client.
1. Creating the Relational Database: An SQL relational borehole database was created to store the location, li- thology, stratigraphy, fractures, time-base geochemistry, and historical water level data for 49 water and monitor- ing wells within the project area.
2. Creating the Surface Model: A ground surface model (Figure 4A) was interpolated from digitized contour lines using an inverse-distance weighting algorithm.
3. Creating the Stratigraphy Model: The underlying sedimentary bedrock layers were modeled as a series of stacked 2D grids (Figure 4C) using a Kriging algorithm and converted to a 3D block model. Note that the stra- tigraphy was modeled before the lithology because the lithology modeling used the top of the stratigraphy model as a lower constraining surface.
4. Creating the Lithology Model: A lithology block model (Figure 4B) was interpolated for the unconsolidated over- burden by using a three-dimensional lateral-extrusion algorithm. This method allows for discontinuous units
Oct.Nov.Dec 2022 • TPG 9
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