  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.
