Four AI systems using different strategies to identify, classify and locate both volcano-tectonic and non-traditional volcanic earthquakes
Description
Identifying and classifying volcanic earthquakes, which can be very different from traditional tectonic earthquakes. These are addressed with CNN's, hidden Markov models, and pre-trained neural networks for picking P- and S-waves in seismograms, hierarchial clustering, identifying non-traditional volcanic earthquakes (e.g., long-period, degassing events, bubble collapse, and assigning magnitudes).
Detailed example
Catalogs of earthquakes
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
These methods will automatically detect and classify earthquakes in large data sets without human review. In the case of identifying small earthquakes, the method has detected 4.5x more earthquakes than human review.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Seismic waveforms and spectrograms