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.

The Permian Basin has become the United States’ largest producer of oil over the past decade. Along with the rise in production, there has been an increase in the rate of low magnitude earthquakes, some of which have been associated with hydrocarbon extraction and wastewater injection. A detailed knowledge of changes to the subsurface can aid in understanding the causes of seismicity, and these changes can be inferred from InSAR surface deformation measurements. In this study, we show that both cm-level cumulative deformation, as well as mm-level coseismic deformation signals, are detectable in West Texas. In a region west of Mentone, TX, we reconstructed the subtle coseismic deformation signal on the order of ~5 mm associated with the recent M4.9 earthquake. Over ~100,000 km2 of the Permian Basin, we created annual cumulative LOS deformation maps, decomposing into vertical and eastward components where overlapping data are available. These maps contain numerous subsidence and uplift features near active production and disposal wells. The most important deformation signatures are linear streaks that extend tens of kilometers near Pecos, TX, where a cluster of increased seismic events was cataloged by TexNet. As validated by independent GPS data, our InSAR processing strategy achieved millimeter-level accuracy. A careful treatment of the InSAR tropospheric noise, which can be as large as 15 cm in West Texas, is required to detect surface deformation signals with such low signal-to-noise ratio. We developed an outlier removal technique based on robust statistics to detect the presence of strong, non-Gaussian noise. We compared the surface deformation solutions of multiple InSAR time series methods, and all of them produced more accurate and consistent deformation trends after removing outlier InSAR measurements. We are exploring a Bayesian generalization of SBAS velocity estimation by including probabilistic data rejection to determine which pixels should be excluded from the model fitting. This technique provides a full posterior distribution of the model parameters along with the best-fit surface velocity.