Joint 55th Annual North-Central / 55th Annual South-Central Section Meeting - 2021

Paper No. 8-7
Presentation Time: 10:20 AM

ELECTRICAL RESISTIVITY INVESTIGATION OF KARST FEATURES, SOUTHWEST MISSOURI


WORTHINGTON, Donald, Geography and Geology, Missouri State University, Springfield, MO 65897, MICKUS, Kevin, Missouri State UnivGeology, 901 S National Ave, Springfield, MO 65897-0027 and GOUZIE, Douglas, Geography, Geology, and Planning, Missouri State University, 901 S. National Ave, Springfield, MO 65897

Nixa, Missouri is located on the southwestern edge of the Ozark Dome which mainly consists of Paleozoic carbonates and minor amounts of siliceous sediments that have been cut by mainly northwest trending faults. These Paleozoic formations have low structural dips (1-2 degrees) and are susceptible to the formation of karst features such as sinkholes and caves. Karst features are more common in the near-surface Mississippian carbonate units but also occur within the Ordovician carbonate units. Near surface geophysical methods can be used in determining the location and nature of karst features including caves and sinkholes, especially those that are not visible on the surface.

There are a variety of geophysical methods that can be used to investigate karst features including electrical resistivity, gravity, seismic refraction and very low frequency electromagnetics. While all of these methods are useful in imaging karst features, electrical resistivity methods have been shown to be the most useful in deciphering sinkholes and caves. To investigate a known cave and related sinkholes and faults within Mississippian carbonates south of Nixa, Missouri, a series of two-dimensional electrical resistivity profiles will be collected using the Schlumberger array. The data will be collected using 64 electrodes and a four meter electrode spacing. Terrain data will be collected to include in modeling. The data will be modeled using a robust two-dimensional inversion method where the inversion parameters will be varied to determine the statistically most reasonable model. To constrain the inverse models, seismic ambient noise data will be collected and analyzed which will provide thicknesses of the overlying soil layer.