THE CHALLENGES OF LARGE SCALE TERRAIN CONDUCTIVITY MAPPING AND INVERSION FOR SHALLOW BEDROCK DETERMINATION

With the recent increase in utility-scale solar projects, engineers are faced with the challenge of determining the appropriate foundation design in geographic areas that are dominated by varying shallow subsurface conditions. THG Geophysics has assisted in mapping shallow bedrock on several projects some exceeding 1,000 acres by deploying a UTV mounted terrain conductivity meter.

THG deployed a GF Instruments CMD Explorer, a 3-dipole tool, to collect datasets that could be inverted to obtain maps of varying apparent conductivity with depth. Using boring data, associations were derived between apparent conductivity and rock conditions.

In all cases, inverting datasets from records that cover large tracts with variable geologic conditions introduces challenges in formulating an apparent conductivity model that fits the site conditions; while fulfilling the project objectives. In karst-dominated regions, thick layers of conductive clays overlie deep, weathered bedrock and high quality limestone. It is not difficult to design cutoff models to remove the effect of high conductivity clays, but often weathered limestone bedrock exhibits higher conductivities than local sand deposits that generates a map that overestimates the presence of shallow bedrock. In an area of varying topography, the majority of bedrock can be exposed; very shallow; or overlain with a few feet of very conductive clay. The cutoff models designed to differentiate clay from limestone were not complicated to define; resulting in a large-scale bedrock map with low inversion residuals. However, a few high elevation regions exhibit very high residual values. Upon further review of boring data and geologic mapping, these areas contain shale bedrock rather than limestone, thus defying the assumptions of our model.

Although, this is a fast and efficient method to assess large areas of shallow rock, it requires intensive geologic assessment of the inversion residuals to quantify misinterpretations.