Leveraging Deep Learning in Global 24/7 Real-Time Earthquake Monitoring at the National Earthquake Information Center

Abstract Machine-learning algorithms continue to show promise in their application to seismic processing. The U.S. Geological Survey National Earthquake Information Center (NEIC) is exploring the adoption of these tools to aid in simultaneous local, regional, and global real-time earthquake monitoring. As a first step, we describe a simple framework to incorporate deep-learning tools into NEIC operations. Automatic seismic arrival detections made from standard picking methods (e.g., short-term average/long-term average STA/LTA) are fed to trained neural network models to improve automatic seismic-arrival (pick) timing and estimate seismic-arrival phase type and source-station distances. These additional data are used to improve the capabilities of the NEIC associator. We compile a dataset of 1.3 million seismic-phase arrivals that represent a globally distributed set of source-station paths covering a range of phase types, magnitudes, and source distances. We train three separate convolutional neural network models to predict arrival time onset, phase type, and distance. We validate the performance of the trained networks on a subset of our existing dataset and further extend validation by exploring the model performance when applied to NEIC automatic pick data feeds. We show that the information provided by these models can be useful in downstream event processing, specifically in seismic-phase association, resulting in reduced false associations and improved location estimates.