2024-06-25 ロスアラモス国立研究所(LANL)
＜関連情報＞
- https://discover.lanl.gov/news/0625-ai-earthquakes/
- https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL108288
カルデラ崩壊が繰り返される際の地盤変動を地震の特徴から予測 Seismic Features Predict Ground Motions During Repeating Caldera Collapse Sequence
Christopher W. Johnson, Paul A. Johnson
Geophysical Research Letters Published: 10 June 2024
DOI:https://doi.org/10.1029/2024GL108288
Abstract
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.
Key Points
- Features extracted from 30 s of continuous seismic waveforms contain information about contemporaneous GNSS ground displacements
- A gradient boosted tree model predicts GNSS ground displacements and timing of the next caldera collapse event
- The observations suggest some seismogenic faults emit precursory signals, providing a potential path to characterize behavior of 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 instantaneous and future behavior of earthquake faults.
