Comparison of InSAR time series generation techniques as part of the collaborative GeoSCIFramework research project
Journal Article
Overview
abstract
The GeoSciFramework project (GSF), funded by the NSF Office of Advanced; Cyberinfrastructure and NSF EarthCube programs, aims to improve; intermediate-to-short term forecasts of catastrophic natural hazard; events, allowing researchers to instantly detect when an event has; occurred and reveal more suppressed, long-term motions of Earth’s; surface at unprecedented spatial and temporal scales. These goals will; be accomplished by training machine learning algorithms to recognize; patterns across various data signals during geophysical events and; deliver scalable, real-time data processing proficiencies for time; series generation. The algorithm will employ an advanced convolutional; neural network method wherein spatio-temporal analyses are informed both; by physics-based models and continuous datasets, including; Interferometric Synthetic Aperture Radar (InSAR), seismic, GNSS, tide; gauge, and gas-emission data. The project architecture accommodates; increasingly large datasets by implementing similar software packages; already proven to support internet searches and intelligence gathering.; This talk will focus primarily on the Differential InSAR (DInSAR); time-series analysis component, which quantifies line-of-sight (LOS); ground deformation at mm-cm spatial resolution. Here, we compare time; series products generated under three different processing techniques.; The first, an automated version of InSAR processing using the small; baseline subset (SBAS) method performed in parallel on systems such as; Generic Mapping Tool SAR (GMT5SAR) and the Generic InSAR Analysis; Toolbox (GIAnT). The second method will resemble the first but will; implement different processing systems for performance comparison using; the InSAR Scientific Computing Environment (ISCE) and the Miami InSAR; Time Series Software in Python (MintPy). The final strategy, developed; by Drs. Zheng and Zebker from Stanford University, concentrates on the; topographic phase component of the SAR signal so that simple cross; multiplication returns an observation sequence of interferograms in; geographic coordinates [Zebker, 2017]. Our results provide; high-resolution views of ground motions and measure LOS deformation over; both short and long periods of time.
