Use Cases
Data Pipeline
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The pipeline in the figure above stems from the portion of the larger product pipeline outlined with a red dashed line in the figure on the Anomaly Detection via Machine Learning page.
The AOI-Track Evaluator PGE is triggered by the production of an S1-GUNW within a pre-defined volcanic AOI. The evaluator then publishes a JSON file upon receiving all GUNWs within the specified AOI track.
The production of the JSON file triggers the Displacement Time-Series using MintPy PGE, which calculates the displacement time-series for the defined AOI, and publishes an HDF5 file to S3.
(walk through each step here)The time-series h5 file then triggers the Volcano Anomaly Detection ML PGE, which outputs a Volcano Anomaly map. (format/contents TBD)
The Volcano Anomaly Map is then run through the TBD “Production Rules for Detected Anomalies” PGE. If the map contains (TBD) value over (XX) threshold, then (something happens; probably notify the user who submitted the job).
AOI track Evaluator
MintPy PGE
Volcano Anomaly Detection ML PGE:
Trigger product:
Inputs:
Outputs:
Use Cases
Use case 1: Maintaining forward keep-up production of S1-GUNW products for a volcanic AOI.
Use case 2: Monitoring a volcanic AOI via utilization of ML for potential anomaly detection