Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab‐based studies have used acoustic emission data to predict time‐to‐failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physic...

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Detalles Bibliográficos
Autores principales: Shreedharan, Srisharan, Bolton, David Chas, Rivière, Jacques, Marone, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285915/
https://www.ncbi.nlm.nih.gov/pubmed/35865235
http://dx.doi.org/10.1029/2020JB021588

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285915/
https://www.ncbi.nlm.nih.gov/pubmed/35865235
http://dx.doi.org/10.1029/2020JB021588

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