GROUNDWATER MODELING & VISUALIZATION
Abstract
Keywords:
Strategy #1 - Groundwater Contamination Within Glacial Outwash
In 1985, a 1,4-dioxane groundwater contamination plume was discovered in parts of Scio Township and western Ann Arbor, Michigan. The State of Michigan and the Washtenaw County Health Department have been tracking this plume for over 20 years in what has become a highly visible project to the public.
blanket Paleozoic sedimentary rocks. Surface waters drain primarily into the Huron river.
In 2019, the Michigan Department of Environment, Great Lakes, and Energy (EGLE), the Mannik Smith Group, and RockWare embarked on a joint project to consolidate the available historical well data into a borehole database in order to create lithologic, hydraulic conductivity, and time- based geochemical models. These models were subsequently used to create maps, cross-sections, fence diagrams, time- sequence animations, and volumetric estimations. The ani- mations were used during litigation to visualize and quantify the migration of the plume from 1986 to 2020. The models were also used as input into an online, interactive Geographic Information System (GIS) database hosted by the State of Michigan.
The steps required to create each annual model (Figure 1A on the previous page) were formidable and convoluted. In ad- bad data, and remodeling the data increased the workload. Another consideration was the addition of new data in the future and extending the models and animations accordingly. For these reasons, an emphasis was placed on automating the
The automation was implemented by using a RockWorks “playlist” (Figure 1C) which provides a means of adding in- - cessed sequentially. As new data is added to the database, the playlist may be re-processed to automatically create all new models, diagrams, animations, and volumetric reports.
www.aipg.org The steps involved in this study are described as follows:
1. Creating the Relational Database: A Structured Que- ry Language (SQL) relational borehole database was cre- ated to store the location, lithology, time-base geochem- istry, and historical water level data for 814 water and monitoring wells within the project area.
2. Creating the Surface Model: A Digital Elevation Model (DEM) grid was created (Figure 2B) by extracting points from a Light Detection and Ranging (LiDAR) data set provided by the Southeast Michigan Council of Gov- ernments (SEMCOG).
3. Draping a Satellite Image Over the Surface Model: A satellite image was cropped to the project area, geore- ferenced to the project dimensions, and draped over the surface model (Figure 2A).
4. Creating a Bedrock Surface Model: Very few of the water wells or monitor wells extended to the bedrock (an impermeable shale). As a consequence, seismic data was interpreted by the client and entered into the database as pseudo-boreholes and interpolated into a grid surface using a Kriging algorithm. This grid served as a lower constraining surface during subsequent block modeling.
5. Creating a Maximum Water Level Elevation Mod- el: - ded into a Maximum Historical Water Level (MHWL) grid model (Figure 2F) to serve as an upper constrain- ing surface within the block modeling. A second-order this surface and subsequently truncated by the ground surface model elevations wherever the polynomial sur- face extended above the ground surface (e.g., dissected regions near the Huron River).
6. Creating a Lithology Model: The borehole lithology data was interpolated into a lithology block model (Fig- ure 2D) using a lateral extrusion algorithm that projects lithologies horizontally from the boreholes. This model was truncated by a convex polygon around the control points to conservatively limit the modeling to areas with nearby control points.
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