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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.

System Requirements

Dependencies for ARIA-tools:

Installation instructions for ARIA-tools are outlined here: https://github.com/aria-tools/ARIA-tools#installation

Packages:

Python >= 3.5
[PROJ 4](https://github.com/OSGeo/proj) github >= 6.0
[GDAL](https://www.dgal.org/) and its Python bindings >=3.0

Python dependencies:

[SciPy](https://scipy.org/)
[netcdf4](http://unidata.github.io/netcdf4-python/netCDF4/index.html)
[requests](https://2.python-requests.org/en/master/)

Python Jupyter dependencies

py3X-jupyter
py3X-jupyter_client
py3X-jupyet_contrib_nbextensions
py3X-jupyter_nbextensions_configurator
py3X-hide_code
py3X-RISE

Additionally, you need to download the ARIA-tools documentation repository: 

https://github.com/aria-tools/ARIA-tools-docs.git

Dependencies and/or requirements for MintPy:

Installation instructions for various OS (as well as notes for Docker users) are encompassed here: https://github.com/insarlab/MintPy/blob/master/docs/installation.md

The following requirements are in the requirements.txt file in the MintPy github repository:

cvxopt
dask>=1.0
dask-jobqueue>=0.3
defusedxml
h5py
lxml
matplotlib
numpy
pyproj
pykml
pyresample
scikit-image
scikit-learn
scipy

MintPy and PyAPS repositories:

https://github.com/insarlab/MintPy.git
https://github.com/yunjunz/pyaps3.git

The configuration file necessary to run ARIA data through MintPy is attached below (can I attach it to this page?), and can also be found on the MintPy git: https://github.com/insarlab/MintPy/blob/master/docs/examples/input_files/SanFranSenDT42.txt

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 triggers the AOI track evaluator. The AOI track evaluator creates a JSON file containing, among other metadata, a list of GUNW products when the full track for the AOI has been completed.

Alternatively, S1-GUNW production for a specified AOI track could also be triggered upon the user first defining the AOI. The system will pull relevant SLCs and other data to create the S1-GUNWs for the AOI, if the GUNWs for that area were not already extant.

Questions to answer:

  • Is the JSON file creation hard-coded into the current AOI-track-evaluator? – yes

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.

Questions to answer:

  • Should we create the text file from the metadata in the JSON file (contains list of urls for GUNWs), or should we use Elasticsearch?

    • Depends on how much info we want to pass into the MintPy; are we re-processing past data as well? How are we handling the forward keep-up processing, and how will we update the time-series?

    • Could potentially edit AOI-track-evaluator directly, but may be more complex than other options and could potentially lead to issues with other current capabilities of the system.

  • Need to clarify which products we want to do the time-series calculation on - just the most recent pair, or annual/seasonal pairs and nominal nearest 2-neighbors? Would Elasticsearch work better in this case, to compile all relevant data, rather than just using data currently in JSON output of AOI-track-evaluator?

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, and no other information (no headers, footers, labels, etc).

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.

Questions to answer:

  • Where do we want the data to be saved? Just in the working directory (PGE)? Or do we want the stack of interferograms output from ariaTSsetup to be saved in S3 for potential future use?

  • ariaTSsetup.py also takes minimum overlap (-mo) as an input, defined in units of km^2. Do we want to include this? If so, do we want a single defined value, or should this change depending on the AOI? Who should decide this value?

Step 4:

Once the data has been prepared, it can be run through the main MintPy application, smallbaselineApp.py. The input to this command is the custom configuration file (here named SanFranSenDT42.txt). The custom configuration file is attached to this page (can I do this? Any concerns attaching the config file to this page?), and further information about the format is on the MintPy website: https://mintpy.readthedocs.io/en/latest/examples/input_files/ The figure below shows the flow of smallbaselineApp.py.

Diagram of the flow of smallbaselineApp.py. Figure from Yunjun et al., 2019

(Figure from Yunjen et al., 2019)

An example call to smallbaselineApp is shown below:

smallbaselineApp.py -t SanFranSenDT42.txt

The output product is an HDF-EOS5 format file containing the displacement time-series with geometry information.

Questions to answer:

  • The scientists have indicated a preference for raw displacement time-series, without atmopsheric correction - is there a way to accomplish this using the various capabilities of smallbaselineApp? Currently can define a starting (--start STEP) and ending (--end STEP, --stop STEP) processing step, as well as just one step to perform (--dostep STEP). The individual processing steps are all defined on https://nbviewer.jupyter.org/github/insarlab/MintPy-tutorial/blob/master/smallbaselineApp_aria.ipynb

    • Does the functionality of smallbaselineApp allow for stopping and starting again while skipping steps, without needing to reformat data structure?

  • After initial calculation of displacement time-series, we want to update the time-series at a defined cadence. This brings up a few decisions:

    • Do we want to have the time-series update at a user input cadence (individual for each AOI), or just have the cadence pre-designated for all AOI, then design the system to update at that cadence?

    • How do we update the time-series? Re-process all old data? Or combine time-series HDF-EOS5 files after they’re created by MintPy?

      • If combining, how to accomplish this?

      • If re-processing, how/where to store or call to old data products for re-processing?

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.

Draft diagram of Processing Pipeline - to be refined as more information is learned:

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

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 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

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