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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1029/2019GL085870
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|a eng
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|a Rouet-Leduc, Bertrand
|e verfasserin
|4 aut
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|a Probing Slow Earthquakes With Deep Learning
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|c 2020
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 29.03.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a ©2020. The Authors.
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|a Slow earthquakes may trigger failure on neighboring locked faults that are stressed sufficiently to break, and slow slip patterns may evolve before a nearby great earthquake. However, even in the clearest cases such as Cascadia, slow earthquakes and associated tremor have only been observed in intermittent and discrete bursts. By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi-continuous tremor, providing a proxy that quantifies the slow slip rate. Furthermore, the model trained in Cascadia recognizes tremor in other subduction zones and also along the San Andreas Fault at Parkfield, suggesting a universality of waveform characteristics and source processes, as posited from experiments and theory
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|a Journal Article
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|a Cascadia
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|a deep learning
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| 650 |
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|a machine learning
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| 650 |
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|a slow earthquakes
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| 650 |
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|a tectonic tremor
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1 |
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|a Hulbert, Claudia
|e verfasserin
|4 aut
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| 700 |
1 |
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|a McBrearty, Ian W
|e verfasserin
|4 aut
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| 700 |
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|a Johnson, Paul A
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Geophysical research letters
|d 1984
|g 47(2020), 4 vom: 28. Feb., Seite e2019GL085870
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|x 0094-8276
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|g volume:47
|g year:2020
|g number:4
|g day:28
|g month:02
|g pages:e2019GL085870
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|u http://dx.doi.org/10.1029/2019GL085870
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