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 Standard Product Pipeline figure.

The MintPy PGE (Displacement Time-Series using MintPy), is triggered by the production of an S1-GUNW within a pre-defined volcanic AOI. The MintPy PGE calculates the displacement time-series for the defined AOI, and publishes an HDF5 file to S3.

The time-series h5 file then triggers a Volcano Anomaly Detection ML PGE, which outputs a Volcano Anomaly map. Currently two separate ML codes are under development, indicated by the diagram. Any given MintPy PGE output will only go through one of the ML options. (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).

MintPy PGE

Trigger product: S1-GUNW within a mintpy polygon

Inputs: S1-GUNWs, polygon, track number, start and end dates

Outputs: time series data (standard time-series product structure TBD): includes timeseries_demErr.h5, velocity.h5, maskTempCoh.h5,demErr.h5, avgSpatialCoherehnce.h5, temporalCoherence.h5, and inputs/geometrygeo.h5

Volcano Anomaly Detection ML PGE:

Trigger product:

Inputs: Time series data products from MintPy PGE

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