MODELING THE BEDROCK SURFACE IN INDIANA WITH CONTOURING SOFTWARE

Most contouring algorithms can quickly generate numerous surfaces that honor bedrock surface (BRS) data, but automated BRS models are often poor geologic interpretations because a single combination of contouring algorithm and gridding parameters may not work best throughout a map area. Modeling that works well where deeply incised paleovalleys are present beneath thick glacial cover break down where thin sheets of unconsolidated sediment are draped over the BRS, and vice versa. One way to overcome this problem is to subdivide a map area and apply different BRS modeling techniques (independent, dependent, or coincident) based on inferred relationships between the BRS and a digital elevation model (DEM) of the topographic surface.

Independent BRS models are based on the assumption that the BRS and DEM are unrelated. These models focus on buried BRS features such as paleovalleys. Independent BRS models are made by first developing a computer-generated BRS model that honors the data and roughly outlines BRS features. Breaklines and phantom data points are added to mold the computer-generated surface into a geologic interpretation that fits the data and shows an interpretation of the shape, continuity, and connectivity of buried paleovalleys.

Dependent BRS models are based on the assumption that the BRS is sub-parallel to the topographic surface. These models focus on the thickness of unconsolidated deposits that is draped over the BRS. Dependent BRS models are generated by subtracting a model of unconsolidated deposit thickness from a DEM trend surface that filters out minor DEM relief not related to the shape of the BRS. These models work best where changes in the thickness of unconsolidated deposits is gradual.

The coincident BRS model is based on the assumption that the BRS and DEM are essentially equivalent. These models use DEM data as the BRS model and are employed where bedrock outcrops or where soil maps show thin soils derived from underlying bedrock.

Combining areas where these models are applied yields a digital BRS that fits the data and blends BRS interpretations appropriate for various Quaternary terrains. The digital BRS is used to automate the computation of unconsolidated deposit thickness and to compute the distribution of bedrock units on a geologic map.