Machine learning accurately predicts slow slip in earthquakes
Abstract
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.
- Publication Date:
- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1657151
- Resource Type:
- Multimedia
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; MACHINE LEARNING; SLOW SLIP; SEISMIC SIGNALS; ACOUSTIC DATA; SOUND WAVES; SOUND PATTERN; MEGAQUAKE
Citation Formats
. Machine learning accurately predicts slow slip in earthquakes. United States: N. p., 2019.
Web.
. Machine learning accurately predicts slow slip in earthquakes. United States.
. Mon .
"Machine learning accurately predicts slow slip in earthquakes". United States. https://www.osti.gov/servlets/purl/1657151.
@article{osti_1657151,
title = {Machine learning accurately predicts slow slip in earthquakes},
author = {},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Mar 18 00:00:00 EDT 2019},
month = {Mon Mar 18 00:00:00 EDT 2019}
}