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 and detects earthquakes. We use these sub-networks in conjunction with a shift-and-stack module based on back-projection that introduces kinematic constraints on arrival times, allowing the 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.

References in corpus (10)

• YOLOv4: Optimal Speed and Accuracy of Object Detection
• Decoupled Weight Decay Regularization
• SGDR: Stochastic Gradient Descent with Warm Restarts
• word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
• Generalized Seismic Phase Detection with Deep Learning
• P-wave arrival picking and first-motion polarity determination with deep learning
• PhaseLink: A Deep Learning Approach to Seismic Phase Association
• Locating earthquakes with a network of seismic stations via a deep learning method
• Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7.9 Wenchuan earthquake
• Beyond Correlation: A Path-Invariant Measure for Seismogram Similarity

Cited by in corpus (4)

• Earthquake Phase Association with Graph Neural Networks
• Rapid Seismic Waveform Modeling and Inversion with Neural Operators
• Generalized Neural Networks for Real-Time Earthquake Early Warning
• Seismic-phase detection using multiple deep learning models for global and local representations of waveforms