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

Abstract

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 change is used to ascertain cascaded triggering on these two structures.

Keywords

• Hualien earthquakes
• Unsupervised machine learning
• DBSCAN
• PCA
• Coastal rotation structure
• Central Range fault