TODO Madeline Lambert Alex Dunn

This page provides a user-centric overview of the displacement time-series using MintPy processing pipeline. Developer-centric information is on Developer Information.

Use cases

Use Case 1: System continues forward “keep-up” production of S1-GUNW for volcano.

• User adds an AOI for the system to keep up processing on, and sets the type of AOI as “volcano”.
  Note to Developer: AOIs should be input by developers, not users, at this time.
  Science story: AOIs will encompass volcanic areas to be monitored for anomalies.

• System automatically pulls ancillary data for AOI (L1 SLCs, orbit data, calibration data, etc.).

• System automatically processes from the L1 SLC to L2 S1-GUNW.
  Note to Developer: System will perform automatic forward “keep-up” production of S1-GUNW for each defined AOI. The system will produce the standard product of the nearest 2-neighbors, and will also produce annual and seasonal pairs.
  Science story: Annual and seasonal pairs will account for/work around low coherence data from winter seasons (due to snow, etc).

Use Case 2: System updates MintPy time-series for volcano.

• User defines frequency of displacement time-series processing for defined AOI (automatically upon new data acquisition, monthly, every N months, etc.).

• System updates previous time-series for defined AOI with new acquisition.
  Note to developer: Science team has specified a preference for output of raw displacement time-series (i.e. without atmospheric correction), rather than filtered data, rolling means, or velocity maps.

• User logs in to system to download time-series.
  Note to Developer: Will need to add facet to allow for user search of displacement time-series.
  Science story: Displacement time-series generation will allow scientists to quickly check on behavior and status of volcanoes of interest.

Use Case 3: Machine learning applied to output of MintPy for potential anomaly detection.

• System applies ML to detect potential anomalies in displacement time-series, and publishes detected results back to GRQ catalog.

• User logs into system to browse potential anomalies.
  Science story: Anomaly detection will inform scientists of possible volcanoes to monitor closely.

Processing Pipeline

This diagram branches off from the larger system diagram on Standard Product S1-GUNW Processing Pipeline.

From a high-level, the main steps of this pipeline are:

1. The production of a S1-GUNW from within a defined volcanic polygon triggers the MintPy PGE to run. Note: a mintpy volcano polygon will be separate from the larger defined AOI. The enumeration strategy for the encompassed volcano(es) will be entered upon creation of the larger volcanic AOI, which should follow track boundaries.

2. The PGE takes a start and end time, track number and polygon as inputs (this information is all stored within the trigger rule), and outputs an HDF5 format file containing the displacement time-series for the AOI.

Implementation Notes (Alex Dunn )

References

Video walkthrough of ARIA-Tools and Time Series InSAR (Discussion of how to prepare ARIA data products for use in MintPy begins at around 3:00:23, and all following material relates to MintPy): https://www.youtube.com/playlist?list=PLzmugeDoplFP-Ju8LwWfALyIKLrPWDfbY

GitHub repo for ARIA-tools: https://github.com/aria-tools/ARIA-tools

GitHub repo for MintPy: https://github.com/insarlab/mintpy

Useful documentation of MintPy: https://mintpy.readthedocs.io/en/latest/

Jupyter notebook examples of how to manually run ARIA products through MintPy:

1. https://nbviewer.jupyter.org/github/insarlab/MintPy-tutorial/blob/master/smallbaselineApp_aria.ipynb

2. https://github.com/aria-tools/ARIA-tools-docs/blob/master/JupyterDocs/NISAR/L2_interseismic/mintpySF/smallbaselineApp_aria.ipynb