A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale

2025/05/24 by Yiyu Ni, Marine A. Denolle, Marine Denolle +16 · 1 voice · 1 citation
Computer Science · Earth and Planetary Sciences · Physics and Astronomy · #FOS: Physical sciences #Geological Modeling and Analysis #Geophysics (physics.geo-ph) #Seismic Imaging and Inversion Techniques #Seismology and Earthquake Studies #physics.geo-ph

paper · pdf · doi:10.48550/arxiv.2505.18874

openalex publication_date 2025/05/24 · arxiv published 2025/05/24 · arxiv updated 2025/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present the first global-scale database of 4.3 billion P- and S-wave picks extracted from 1.3 PB continuous seismic data via a cloud-native workflow. Using cloud computing services on Amazon Web Services, we launched ~145,000 containerized jobs on continuous records from 47,354 stations spanning 2002-2025, completing in under three days. Phase arrivals were identified with a deep learning model, PhaseNet, through an open-source Python ecosystem for deep learning, SeisBench. To visualize and gain a global understanding of these picks, we present preliminary results about pick time series revealing Omori-law aftershock decay, seasonal variations linked to noise levels, and dense regional coverage that will enhance earthquake catalogs and machine-learning datasets. We provide all picks in a publicly queryable database, providing a powerful resource for researchers studying seismicity around the world. This report provides insights into the database and the underlying workflow, demonstrating the feasibility of petabyte-scale seismic data mining on the cloud and of providing intelligent data products to the community in an automated manner.

Citations

• Exploring the Potential of Carbon-Aware Execution for Scientific Workflows
• SeisMoLLM: Advancing Seismic Monitoring via Cross-modal Transfer with Pre-trained Large Language Model
• SeisLM: a Foundation Model for Seismic Waveforms
• Detection of Hidden Low-Frequency Earthquakes in Southern Vancouver Island with Deep Learning
• Deep learning detects uncataloged low-frequency earthquakes across regions
• PyOcto: A high-throughput seismic phase associator
• PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning
• QuakeFlow: A Scalable Machine-learning-based Earthquake Monitoring Workflow with Cloud Computing
• SeisBench -- A Toolbox for Machine Learning in Seismology
• SeisBench—A Toolbox for Machine Learning in Seismology
• CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
• PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method
• PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method
• Convolutional Neural Network for Earthquake Detection and Location
• ObsPy: A Python Toolbox for Seismology

Cited by

• Exploration of Machine Learning Methods to Seismic Event Discrimination in the Pacific Northwest

Discussions

• https://arxiv.org/abs/2505.18874 #physics.geo-ph Result Details [bsky, 0 points, 0 comments]

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