Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
The authors here tackle the problem that too much seismic data is acquired worldwide to be evaluated in a timely fashion. Seydoux and colleagues develop a machine learning framework that can detect and cluster seismic signals in continuous seismic records.
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a51cdd1b270c4f8196f7d797e88c4698 |
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| Sumario: | The authors here tackle the problem that too much seismic data is acquired worldwide to be evaluated in a timely fashion. Seydoux and colleagues develop a machine learning framework that can detect and cluster seismic signals in continuous seismic records. |
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