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TODO Madeline Lambert Alex Dunn

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: Method of inputting AOI could be bounding box drawn on a Google Earth map, entering lat/long coordinates, or uploading a vector file.
    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.

Requirements

Running ARIA data through MintPy (the below section is in progress)

This section gives an overview of the necessary steps to run S1-GUNW products from a complete AOI track through the MintPy displacement time-series calculations.

Nominally, for a user outside of HySDS, the first step would be to download the data products of interest using ariaDownload.py from ARIA-tools (https://nbviewer.jupyter.org/github/aria-tools/ARIA-tools-docs/blob/master/JupyterDocs/ariaDownload/ariaDownload_tutorial.ipynb). This command allows the user to specify a data range either spatially (with a specified bounding box or link to a shapefile) or temporally (with start/stop dates or a temporal baseline). However, for the purposes of automation, it would be far more efficient to not have to download S1-GUNW files.

Step 1:

When an S1-GUNW product is created and within a specified AOI for the purposes of monitoring volcanic activity, it should trigger the AOI track evaluator. The AOI track evaluator should pass along a JSON file containing, among other metadata, a list of GUNW products when the full track for the AOI has been completed.

Step 2:

Rather than download the products, a text file containing the urls of each of the products can be passed to the ariaTSsetup.py step. Currently, the output of the AOI track evaluator is a JSON file that contains, among other metadata, a list of the urls of the GUNW products. A text file containing only the urls of each S1-GUNW within the complete AOI track should be created from the JSON file (or a separate text file should be created by the AOI track evaluator, whichever would be simpler). The text file should contain one url per line. (this step needs to be refined)

Step 3:

The ariaTSsetup.py code can take a text file containing the urls of each data product as input. The text file contains one url per line.

The bounding box of the AOI can be input using (-b ‘coordinates in SNWE’). To extract meta-data layers from the input data, the user needs to download a DEM (--dem Download). There is also a functionality to download a mask (--mask Download) to remove any water bodies from the data.

Calling ariaTSsetup.py should look like:

ariaTSsetup.py -f 'nameOfTextFile.txt' -b '37.25 38.1 -122.6 -121.75' --mask Download --dem Download

There is also an option to specify a working directory (-w) in which the intermediate products and final outputs are saved. If not otherwise specified, the default working directory is the current directory.

Processing Pipeline

  • create new diagram of the steps of (a) processing steps and (b) data

  • what are the key steps needed from S1-GUNW -> aria-tools -> MintPy -> L3 displacement time series

  • for each step, find out what input and outputs are, and what condition(s) is used to trigger that step.

  • what are the key datasets

    • type

    • dataset naming convention

  • identify source code of each step.

Implementation Notes (Alex Dunn )

User Guide

(for operator to run time series processing)

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 example of how to manually run ARIA products through MintPy: https://nbviewer.jupyter.org/github/insarlab/MintPy-tutorial/blob/master/smallbaselineApp_aria.ipynb

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