EOMF-06. InSAR time series generation and data streaming into the GeoSciFramework

Abstract
The GeoSciFramework (GSF) project, 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 events 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. Here, we focus on the Differential InSAR (DInSAR) time series analysis component, which quantifies mm-to-cm level line-of-sight (LOS) ground deformation at ~25-meter spatial resolution. We begin by comparing processing techniques that produce initial interferograms such as the Generic Mapping Tool SAR (GMT5SAR), and the InSAR Scientific Computing Environment (ISCE). Using those results, we also compare time series generation programs like the Generic InSAR Analysis Toolbox (GIAnT) and the Miami InSAR Time Series Software in Python (MintPy). Finally, we attempt to integrate our decisive InSAR time series with GNSS data to reveal 3-D ground surface motions. Our discussion focuses on why these techniques are better suited for data ingestion and streaming through the GSF architecture and how we will begin to construct a synthetic InSAR data set in order to accurately model volcanic deformation over Kilauea volcano in Hawaii and Yellowstone National Park.