Seismic Features Predict Ground Motions During Repeating Caldera Collapse Sequence

• Published in: Geophysical research letters 2024-06, Vol.51 (11), p.n/a
• Main Authors: Johnson, Christopher W., Johnson, Paul A.
• Format: Article
• Language: English
• Subjects: Acoustic emission; Acoustics; Calderas; Collapse; Earthquake damage; Earthquakes; Emissions; Fault lines; Faults; Friction; GEOSCIENCES; GNSS; Ground motion; ground motions; Laboratories; Learning algorithms; Machine learning; Physical properties; predicting fault time to failure; Regression models; seismic; Seismic activity; Signatures; Slip; Subduction; Subduction (geology); Subduction zones; Volcanoes
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2024GL108288

Applying machine learning to continuous acoustic emissions, signals previously deemed noise, from laboratory faults and slowly slipping subduction‐zone faults, demonstrates hidden signatures are emitted that describe physical details, including fault displacement and friction. However, no evidence currently exists to demonstrate that similar hidden signals occur during seismogenic stick‐slip on earthquake faults—the damaging earthquakes of most societal interest. We show that continuous seismic emissions emitted during the 2018 multi‐month caldera collapse sequence at the Kı̄lauea volcano in Hawai'i contain hidden signatures characterizing the earthquake cycle. Multi‐spectral data features extracted from 30 s intervals of the continuous seismic emission are used to train a gradient boosted tree regression model to predict the GNSS‐derived contemporaneous surface displacement and time‐to‐failure of the upcoming collapse event. This striking result suggests that at least some faults emit such signals and provide a potential path to characterizing the instantaneous and future behavior of earthquake faults. Plain Language Summary Applications of machine learning have revealed that continuous acoustic emissions from laboratory earthquake experiments contain continuous, hidden signatures that describe the fault slip. In these laboratory studies, the acoustic emissions signals that were previously deemed to be dominantly noise, are found to be rich with details that can describe physical properties such as the fault displacement, friction, and fault thickness. By applying similar machine learning approaches, it was discovered that signatures of surface displacement exist in the seismic emissions from slowly slipping subduction zone faults. However, there has yet to be similar evidence observed during seismogenic stick‐slip on earthquake faults–the damaging earthquakes of most societal interest. Here we study a repeating caldera collapse sequence with short enough repeat times to mimic the experiments performed in the laboratory. We find that seismic emissions from seismogenic fault slip associated with the 2018 caldera collapse at the Kı̄lauea volcano in Hawai'i, also contain hidden signatures informing of instantaneous surface displacement associated with fault slip at depth, as well as time‐to‐failure of the upcoming slip event. These observations suggest that at least some seismogenic faults emit such signals, and provide a potential path to characterizing the