2023/11/23 by Jannes Münchmeyer, Münchmeyer, Jannes, Sophie Giffard‐Roisin +11 · 2 citations
Computer Science · Earth and Planetary Sciences · #Earthquake Detection and Analysis #FOS: Physical sciences #Geophysics (physics.geo-ph) #Seismology and Earthquake Studies #earthquake and tectonic studies
paper · pdf · doi:10.48550/arxiv.2311.13971
openalex publication_date 2023/11/23 · openalex created_date 2023/11/28 · openalex updated_date 2026/08/01
Documenting the interplay between slow deformation and seismic ruptures is essential to understand the physics of earthquakes nucleation. However, slow deformation is often difficult to detect and characterize. The most pervasive seismic markers of slow slip are low-frequency earthquakes (LFEs) that allow resolving deformations at minute-scale. Detecting LFEs is hard, due to their emergent onsets and low signal-to-noise ratios, usually requiring region-specific template matching approaches. These approaches suffer from low flexibility and might miss LFEs as they are constrained to sources identified a priori. Here, we develop a deep learning-based workflow for LFE detection, modeled after classical earthquake detection with phase picking, phase association, and location. Across three regions with known LFE activity, we detect LFEs from both previously cataloged sources and newly identified sources. Furthermore, the approach is transferable across regions, enabling systematic studies of LFEs in regions without known LFE activity.