A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale
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.
Keywords
Cite
@article{arxiv.2505.18874,
title = {A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale},
author = {Yiyu Ni and Marine A. Denolle and Amanda M. Thomas and Alex Hamilton and Jannes Münchmeyer and Yinzhi Wang and Loïc Bachelot and Chad Trabant and David Mencin},
journal= {arXiv preprint arXiv:2505.18874},
year = {2025}
}