Applying unsupervised machine-learning algorithms and MUSIC back-projection to characterize 2018–2022 Hualien earthquake sequence

Applying unsupervised machine-learning algorithms and MUSIC back-projection to characterize 2018–2022 Hualien earthquake sequence

Key points 1. We used unsupervised machine-learning algorithms DBSCAN and PCA to study the 2018–2022 Hualien earthquake sequence. 2. A deep westward-dipping and a shallow rotation structure system are revealed from earthquake clusters close to the northernmost Longitudinal Valley. 3. Coulomb stress...

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Bibliographic Details
Main Authors: Pei-Ru Jian, Yu Wang
Format: Article
Language:English
Published: Springer 2022-09-01
Series:Terrestrial, Atmospheric and Oceanic Sciences
Subjects:
Hualien earthquakes
Unsupervised machine learning
DBSCAN
PCA
Coastal rotation structure
Central Range fault
Online Access:https://doi.org/10.1007/s44195-022-00026-y

Internet

https://doi.org/10.1007/s44195-022-00026-y

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