[Un]supervised clustering of [non-]earthquake signals commonly recorded on regional seismic networks
Description
Surficial mass movements (SMMs), such as landslides and rockfalls, have seismic signatures distinct from other routinely-recorded seismic sources like earthquakes and explosions. This project aims to develop a classification scheme to differentiate between seismic signals generated by different sources, especially for those generated by vertical processes ("fall") versus those generated by horizontal processes ("slides").
Detailed example
Events will be automatically classified as seismogenic (e.g., eathquakes or explosions) surficial mass motions (e.g., falls or slides) using statistical metrics extracted from real-time seismic waveform data.
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
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
By automatically discriminating the different sources that generate observed seismic signals, we can more accurately catalog the events and respond to them in more timely, appropriate ways.
Controls / human review
ATO: No; PIA: Not published