Detecting slow slip events in the Cascadia subduction zone from GNSS time series using deep learning-Reference-Cited by-同舟云学术

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Detecting slow slip events in the Cascadia subduction zone from GNSS time series using deep learning

Published:2024-07-18 Issue:4 Volume:28 Page:
ISSN:1080-5370
Container-title:GPS Solutions
language:en
Short-container-title:GPS Solut

Author:

Wang Ji,Chen Kejie,Zhu Hai,Hu Shunqiang,Wei Guoguang,Cui Wenfeng,Xia Lei

Funder

Guangdong Natural Science Fund 2023 - General Program

the Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology

the National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10291-024-01701-y.pdf

Reference38 articles.

1. Bletery Q, Nocquet JM (2020) Slip bursts during coalescence of slow slip events in Cascadia. Nat Commun 11(1):2159. https://doi.org/10.1038/s41467-020-15494-4

2. Carr Agnew D (2013) Realistic simulations of geodetic network data: the Fakenet package. Seismol Res Lett 84(3):426–432. https://doi.org/10.1785/0220120185

3. Choudrey RA (2002) Variational methods for Bayesian independent component analysis. (Ph.D. Thesis), University of Oxford Oxford, UK.

4. Choudrey RA, Roberts SJ (2003) Variational mixture of Bayesian independent component analyzers. Neural Comput 15(1):213–252. https://doi.org/10.1162/089976603321043766

5. Costantino G, Socquet A, Giffard-Roisin S, Dalla Mura M, Radiguet M, and Marsan D (2022) Detection of small slow slip events from GNSS data using deep learning in the cascadia subduction zone. Paper presented at the AGU Fall Meeting Abstracts.

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