Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking

By S. M. Mousavi, William Ellsworth, Weiqiang Zhu, L. Chuang, Gregory C. Beroza. Nature Communications, 2020

973 citations recorded by the source corpus.

Earthquake signal detection and seismic phase picking are challenging tasks in the processing of noisy data and the monitoring of microearthquakes. Here we present a global deep-learning model for simultaneous earthquake detection and phase picking. Performing these two related tasks in tandem improves model performance in each individual task by combining information in phases and in the full waveform of earthquake signals by using a hierarchical attention mechanism. We show that our model outperforms previous deep-learning and traditional phase-picking and detection algorithms. Applying our model to 5 weeks of continuous data recorded during 2000 Tottori earthquakes in Japan, we were able to detect and locate two times more earthquakes using only a portion (less than 1/3) of seismic stations. Our model picks P and S phases with precision close to manual picks by human analysts; however, its high efficiency and higher sensitivity can result in detecting and characterizing more and smaller events. The authors here present a deep learning model that simultaneously detects earthquake signals and measures seismic-phase arrival times. The model performs particularly well for cases with high background noise and the challenging task of picking the S wave arrival.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/309fd887dc776db182ba054c979b37738a2bee07

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Citations in the corpus

Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “Simultaneous earthquake detection and phase picking can further improve the accuracy of both tasks (Mousavi et al., 2020; Zhou et al., 2019).”
Earthquake Phase Association Using a Bayesian Gaussian Mixture Model (2021, background): “The phase picking step has been significantly improved by deep-learning-based pickers that learn from manual picks labeled by analysts to detect millions of phase picks from raw seismic waveforms (Mousavi et al., 2020; Ross et al., 2018; W. Zhu & Beroza, 2018).”
Deep-learning seismology (2022, methodology): “( 12 ), for example, used attention mechanisms in their detection and picker DNN.”
Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions (2021, background): “, 2019) to tackle problems such as earthquake detection, automatic phase picking (Mousavi et al., 2020; Ross et al., 2018; Yoon et al., 2015) and seismic signal denoising (Zhu et al.”
Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers (2021, background): “Earthquake transformer (EQTransformer) (Mousavi et al., 2020) is a model for joint event detection, phase detection and onset picking.”