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\begin{document}

\title{Comparison of InSAR time series generation techniques as part of the
collaborative GeoSCIFramework research project}



\author[1]{Brianna Corsa}%
\author[1]{Kristy Tiampo}%
\author[2]{Krisztina Kelevitz}%
\author[3]{Scott Baker}%
\author[3]{Charles Meertens}%
\affil[1]{University of Colorado Boulder, CIRES}%
\affil[2]{University of Leeds, COMET}%
\affil[3]{UNAVCO, Inc.}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\begin{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.%
\end{abstract}%



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