Machine learning accurately predicts slow slip in earthquakes
Multimedia
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OSTI ID:1657151
Machine-learning research has detected seismic signals accurately predicting the slow slipping of the Cascadia fault. It has also found similar signals predicting slow slip failure in Chile and New Zealand. Los Alamos National Laboratory researchers applied machine learning to analyze 12 years of historic Cascadia data. Cascadia’s constant tremors produce an acoustic signal, like sound waves. The key to Cascadia’s behavior was buried in that acoustic data.
- Research Organization:
- LANL (Los Alamos National Laboratory (LANL), Los Alamos, NM (United States))
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI ID:
- 1657151
- Country of Publication:
- United States
- Language:
- English
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Journal Article
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2020
· Geophysical Research Letters
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OSTI ID:1601684