264-1 Classification of Water Flow Paths in Heterogeneous Karst Formation Using Remote Sensing LiDAR

Session: Understanding Karst Hydrology and Karst Aquifers Using Innovative Tracers and Other Technologies (Posters)

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Abstract:

The Edwards Aquifer is a critical water resource for the state of Texas and the city of San Antonio. This aquifer is hosted in a complex network of fractured Cretaceous carbonate rock with significant areas of cavernous porosity. The complexity and importance of this aquifer make understanding and mapping the flow of this system crucial for preserving this resource. Caves provide unique access to aquifers, and one cave that allows this access for the Edwards Aquifer is Natural Bridge Caverns, north of San Antonio. 

LiDAR utilizes light to create a three-dimensional point cloud and has previously been used to study both groundwater and caves. This project utilized LiDAR point clouds of varying spatial resolutions to automatically identify cave ceiling features (stalactites). The technique then classifies water percolation in the vadose zone from the surface to the cave ceiling based on the morphology and spatial distribution of stalactites. The best point cloud resolution for accurate stalactite identification was found by comparing manual and automatic stalactite counts. The input parameters (stalactite threshold, flow threshold, and moving average window size), which are used to define stalactites and flow types, were optimized for the cave site. With these optimized parameters, the ceiling of the cavern was sectioned and analyzed for flow type classification. This project also sought to determine the relationship between the base scan resolution and the accuracy of stalactite counts. This was achieved by conducting a second LiDAR survey and comparing the output stalactite counts resulting from the two different point clouds. We found the optimal resolution to be a 10 mm point spacing and optimal parameters to be a threshold of the 97th percentile for stalactite identification, a threshold of the 93rd percentile for flow classification, and a moving average window of 20 cm x 20 cm. Through our analysis of the cave ceiling, we found that the sections of the cave with the highest amount of flow are located in the deeper sections of the cavern, and the shallower sections contain the flow types representing the lowest amount of flow. The accuracy of the method is controlled by the total number of LiDAR scans and the scan position, as these factors impact data voids in the point cloud.

Geological Society of America Abstracts with Program. Vol. 57, No. 6, 2025

doi: 10.1130/abs/2025AM-6455

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