Detecting low-frequency earthquakes with deep learning

Subduction megathrusts are the largest earthquakes occuring worldwide. Yet the generation of large subduction earthquakes is still poorly understood. Recent research revealed that aseismic deformation in the form of slow slip events (SSEs) might play a key rule in the build-up of these events. However, SSEs are hard to observe directly, due to there slow nature. One way to identify and study aseismic deformation is through co-occuring signals, for example, low-frequency earthquakes (LFEs). Yet these events are again difficult to observe due to their low signal-to-noise ratio and emergent onsets.

In this project, we build machine learning models to identify low-frequency earthquakes. These models are more flexible and transferable than the commonly employed template matching techniques for LFE detection. We focus on deep learning based models, building upon their excellent performance for the picking and detection of regular seismicity. To train and evaluate these models we have compiled a collection of LFE datasets from multiple world region in a format tailored for machine learning. We integrate our LFE detector into the SeisBench library to allow easy application of the model in future studies.