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 AOI triggers the AOI track evaluator to run.

2. Once the AOI track evaluator detects that the full track of GUNWs has been produced, the evaluator creates a JSON file containing metadata about the data products, and stores it on S3.

3. The creation of the JSON file triggers the time-displacement PGE to run.

4. The PGE takes a start and end time and bbox as inputs, 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