An End‐To‐End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning

Abstract Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. In this study, we introduce a new end‐to‐end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi‐station processing of seismic waveforms recorded over a seismic network. This end‐to‐end architecture consists of three sub‐networks: a backbone network that extracts features from raw waveforms, a phase picking sub‐network that picks P‐ and S‐wave arrivals based on these features, and an event detection sub‐network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub‐networks together with a shift‐and‐stack module based on back‐projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end‐to‐end approach can effectively pick P‐ and S‐wave arrivals and achieve earthquake detection accuracy rivaling that of other state‐of‐the‐art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.