Volcanic Anomaly Detection

Data Pipeline

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 MintPy PGE (Displacement Time-Series using MintPy), which calculates the displacement time-series for the defined AOI, and publishes an HDF5 file to S3.

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:

Notes: Need to incorporate reference point selection code again before running through the ML code

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