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The time-series h5 file then triggers the 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).
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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 maskTempCohinputs/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
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