2005 Salt Lake City Annual Meeting (October 16–19, 2005)

Paper No. 3
Presentation Time: 1:30 PM-5:30 PM


MACCORMACK, Kelsey E.1, EYLES, Carolyn H.1 and MACLACHLAN, John C.2, (1)School of Geography and Earth Sciences, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada, (2)School of Geography and Earth Sciences, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, maccorke@mcmaster.ca

A number of recent advances in 3-D subsurface geologic modeling techniques now allow relatively large data sets to be modeled in regions underlain by complex geology. However, the accuracy and reliability of model outputs are still constrained by the quality of input data and the spatial analytical techniques used to visualize and analyze these data. The selection of a modeling technique to be used for a particular application is dependent largely on the purpose of the project, the nature of available input data, expertise of personnel and time available. This presentation will discuss three types of 3-D subsurface modeling technique that use different types of input data and filtering algorithms. These techniques will be illustrated with 3-D subsurface models created for the Hamilton region of southern Ontario. The first technique is a basic model application using digitized data from readily available sources such as regional water well databases. The most common problem with relying on water well data as the only data source is the variable quality of input data which requires that erroneous points or ‘outliers' are carefully identified and removed, either manually or with algorithms available in the modeling program. The advantage of a basic approach to 3-D subsurface modeling is that models can be produced relatively quickly. The second technique involves creation of an integrated database by the addition of high quality data from sources such as geotechnical and construction reports, or outcrop descriptions. This integrated database can be used to create multiple 3-D models using a variety of program algorithms, such as triangulation, inverse distance, and kriging. Different models can be compared with known geological conditions to determine which is the most realistic. This technique is best used in areas underlain by complex stratigraphies. Finally, the quality of model output can be greatly improved by the implementation of various filtering techniques in areas where significant geological barriers such as faults, escarpments or large surface water bodies exist. In these situations, simple spatial interpolation of data points produces erroneous results and the application of filters significantly increases the reliability of model output.