Automatic Detection of InSAR Deformation and Tropospheric Noise Features Using Computer Vision: A Case Study Over West Texas

Abstract

Automatic detection of surface deformation features from large volumes of Interferometric Synthetic Aperture Radar (InSAR) data is challenging because the magnitude of InSAR measurement noise varies substantially in both space and time. In this work, we present a computer vision algorithm based on Laplacian of Gaussian (LoG) filtering to detect the size and location of unknown surface deformation features. Because our algorithm targets spatially coherent features, tropospheric noise artifacts with similar spatial characteristics may also be detected. To quantify the likelihood that a detected feature is a real deformation signal, we estimate the tropospheric noise spectrum directly from data, and we characterize tropospheric noise using noise simulations that resemble the actual InSAR observations. We demonstrate our algorithm using Sentinel-1 data acquired between 2014 and 2019 over the $\sim$80,000 ${\text{km}}^2$ oil-producing Permian Basin in West Texas—one of the most productive oil fields in the world. We detect clusters of deformation features associated with oil production, wastewater injection, and fault activity. The number of detected deformation features increases substantially over the study period, which is consistent with the overall rise in oil production within the Permian Basin since 2014. Further, we show that our algorithm can detect subtle surface deformation from the 26 March 2020 $M_W$ 5.0 earthquake near Mentone, Texas, USA and quantify detection uncertainty. Our method is robust and flexible and can be integrated into various multi-temporal InSAR time series techniques for detecting a broad range of local deformation features.