GROUNDWATER MODELING & VISUALIZATION
draulic conductivity (K) data from a direct-push core sampler - pendicular to the hydraulic gradient. The host geology was K-model suggested otherwise. As with the previous example, the applied strategy (Figure 5 - previous page) is illustrated by a “pseudo case-study” meaning proprietary data 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, hy- draulic conductivity, TCE, and RDX data for 43 direct push core samples.
2. Creating the Surface Model: A Kriging algorithm was used to model the ground surface based on the borehole collar elevations. This model served as an upper con-
3. Borehole Logs: Separate borehole log diagrams were created for the hydraulic conductivity, TCE, and RDX data (Figure 5A) in which proportionally scaled cylinders are scaled and color-coded in proportion to the data.
4. Block Models: Block models were interpolated for the hydraulic conductivity, TCE, and RDX data using an anisotropic inverse-distance weighting algorithm as con- strained by the ground surface model (Figure 5B).
5. Mass Flux Models: RDX were created by multiplying the interpolated mod- els by the hydraulic conductivity model (Figure 5C).
6. Mass Flux Transects:- sects perpendicular to the hydraulic gradient were cre- (Figure 5D).
Conclusions
If should be noted that the aforementioned case-studies represent static models depicting the contamination at the time of the data sampling. No attempt was made to create predictive models or simulations representing the contamina- tion beyond the last sampling date.
Enumerating these strategies as playlists provides a num- ber of advantages, including:
1. All diagrams, animations, and analyses can be automati- cally regenerated if the data is changed or added versus the tedium of opening, editing, and closing dozens of menus.
2. The playlists can be used as templates for similar proj- ects. This is especially true for sub-sites within large projects where the geology is similar.
3. Given that all of the modeling parameters are saved within each playlist item, they can serve as an audit trail during litigation and infrequently-revisited projects to determine exactly how the data was modeled.
4. Strategies can be provided to other investigators in an organized, self-documenting fashion.
5. Strategies can be readily modified and re-processed thereby allowing for experimentation. For example, if a
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modeling algorithm that is used early on in the process is changed, the effects upon subsequent operations can be quickly determined.
Acknowledgements
The author sincerely thanks Kevin Lund (CPG-10052) and EGLE for providing data, guidance, and friendship during the modeling of the Gelman 1,4-dioxane plume. Additional grati- tude is extended to Kevin Brown and the Mannik Smith Group for performing the “heavy lifting” involved with gathering and error-checking the input data and consolidating the modeling results into an online GIS that is accessible to the public.
References
“Gelman Sciences, Inc.” Michigan Department of Environ- ment, Great Lakes, and Energy,
www.michigan.gov/egle/ about/organization/Remediation-and-Redevelopment/Gel- man-Sciences-Inc.
RockWorks2002 Geological Data Management, Analysis, and Visualization Software, RockWare Inc., Golden, CO.
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