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

TL;DR

This paper introduces a comprehensive, cloud-based database of 4.3 billion seismic phase picks from global continuous data, enabling advanced seismic research and demonstrating scalable data mining at petabyte scale.

Contribution

It presents the first large-scale, open-access seismic phase database created using cloud computing and deep learning, showcasing a scalable workflow for seismic data analysis.

Findings

Revealed Omori-law aftershock decay patterns.

Identified seasonal variations in seismic noise.

Achieved rapid processing of petabyte-scale data in under three days.

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.