| Abstract | Sinkhole hazards pose a major threat to key infrastructure and human lives in Taylor, Fisher, Haskell, Knox, Stonewall, Callahan, and Jones Counties of the West Central region of Texas. These counties are underlain by soluble evaporite and carbonate rocks. In this study, a data fusion approach was adopted in which multi-source datasets and techniques were combined to detect and map the spatial distribution of sinkholes, quantify their displacement rates, and identify the processes and factors controlling their occurrence. (a) Using Light Detection and Ranging (LIDAR) datasets, areas with depressions ranging from 600 m2 to 2600 m2 and a CI value exceeding 0.86 were identified as potential sinkhole zones based on their morphological and physical characteristics. (b) deformation rates over the mapped depressions derived using Persistent Scatterer Interferometry technique applied on 56 level-1 Sentinel-1 images (2016 – 2021) and calibrated using long-term (2006 – 2021) GNSS. The result indicate that average and peak subsidence rates of -1.5 mm/yr and -6.5 mm/yr, respectively; (c) clusters of high subsidence rates were noted over are as underlain by evaporites belonging to the Clear Fork Group, and Blain Formation of carbonated rocks; (d) In order to investigate the formation of sinkholes in the highly vulnerable area of north Abilene, an Electrical Resistivity survey was conducted to identify their characteristics and underlying processes. In addition, groundwater level and discharge time series and other relevant datasets were integrated to assess the processes and factors that induce the formation of these features. Results of this study could be used to develop an early warning system to implement mitigation strategies to curtail the impacts of the sinkhole hazards in Texas and other parts of the globe. |
