Abstract Understanding how slow slip events (SSEs) influence fault behavior is essential for characterizing the fault slip spectrum and its role in earthquake generation. Here, we show that deep learning applied to strainmeter data can detect short-duration SSEs on the San Andreas Fault near Parkfield, enabling an SSE catalog. SSEs are coherently observed across instruments, with evidence from nearby creepmeters. Location analysis indicates shallow depths and slip consistent with right-lateral motion. They follow a cubic moment–duration scaling law, similar to earthquakes and consistent with both subduction zone observations, and linear scaling as an upper bound. Low-frequency earthquakes increase following SSEs, suggesting that slow aseismic slip modulates seismicity. Detecting these SSEs fills an observational gap in slow earthquake studies and highlights their broader relevance. These findings support a continuum between aseismic and seismic slip, where transient deformation in creeping segments perturbs stress in adjacent locked areas, potentially promoting seismic activity.
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