Since 2008, the rate of seismic events within the Central United States has dramatically increased, which is likely associated with wastewater injection from nearby oil and gas operations. Surface deformation measurements derived from spaceborne interferometric synthetic aperture radar (InSAR) data can be used to quantify the magnitude and spatial extent of the injection-related stress perturbation, which are critical for understanding the complex interaction between the injected fluid and the earth’s subsurface. In this study, we processed Sentinel-1 InSAR data over Central and West Texas using a recently developed processing framework that performs topography/geometry phase corrections prior to the interferogram formation (Zebker 2017). We streamlined the creation of upsampled digital elevation maps (DEMs) from NASA Shuttle Radar Topographic Mission (SRTM) data, as well as the collection of Sentinel-1 precise orbit data. We developed a tool for InSAR time-series analysis and data visualization. To detect unknown deformation signatures from large volumes of InSAR data, we employed computer vision ideas for feature detection independent of scale, well known through their success in the Scale Invariant Feature Transform (SIFT). We used multi-scale Laplacian-of-Gaussian (LoG) filters to find local maxima and minima in a coarse deformation solution, corresponding to “bowls” of uplift and subsidence, respectively. This allowed us to drastically cut down processing time of high-resolution InSAR products. As a validation, our method successfully detected all sinkhole locations, injection-related uplift signals and production-related subsidence signals as reported in Kim and Lu (2017) over a 100 km x 100km search area without the need for manual inspection. We then examined the Dallas Fort Worth Basin area for evidence of deformation near wastewater injection and oil/gas production sites. We begin to quantify the uncertainty from common noise sources to produce more confident time-series results.