Gregory C. Beroza
5 papers in the PaperMetrix corpus
Papers by this author
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USGS scientists open to change
2016 · Science
The recent News Feature “Hurdling obstacles” (E. R. Shell, 8 July, p. 116) provided an insightful summary of Marcia McNutt's career. However, we reject the idea that “USGS [U.S. Geological Survey] scientists… were known for …
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STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI
2019 · IEEE Access
Seismology is a data rich and data-driven science. Application of machine learning for gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The availability of a large amount of seismic data …
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Earthquake Location and Magnitude Estimation with Graph Neural Networks
2022 · arXiv (Cornell University)
We solve the traditional problems of earthquake location and magnitude estimation through a supervised learning approach, where we train a Graph Neural Network to predict estimates directly from input pick data, and each input allows …
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QuakeFlow: a scalable machine-learning-based earthquake monitoring workflow with cloud computing
2022 · Geophysical Journal International
SUMMARY Earthquake monitoring workflows are designed to detect earthquake signals and to determine source characteristics from continuous waveform data. Recent developments in deep learning seismology have been used to improve tasks within earthquake monitoring workflows …
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A Robust and Rapid Grid-Based Machine Learning Approach for Inside and Off-Network Earthquakes Classification in Dynamically Changing Seismic Networks
2024 · Seismological Research Letters
Abstract Earthquake location and magnitude estimation are critical for seismic monitoring and emergency response. However, accurately determining the location and the magnitude of off-network earthquakes remains challenging. Seismic stations receive signals from various sources, and …