California Earthquake Dataset for Machine Learning and Cloud Computing
Weiqiang Zhu, Haoyu Wang, Bo Rong, Ellen Yu, Stephane Zuzlewski,, Gabrielle Tepp, Taka'aki Taira, Julien Marty, Allen Husker, Richard M Allen

TL;DR
This paper introduces a comprehensive, open-access California Earthquake Dataset (CEED) that consolidates seismic data from multiple sources, supporting machine learning and seismic research with detailed event parameters from 2000 to 2024.
Contribution
The paper presents a unified, organized earthquake dataset integrating multiple data sources and parameters, facilitating advanced deep learning applications and seismic research.
Findings
Dataset includes over two decades of seismic data.
Supports development of deep learning models for earthquake analysis.
Enables research into earthquake processes and seismic risks.
Abstract
The San Andreas Fault system, known for its frequent seismic activity, provides an extensive dataset for earthquake studies. The region's well-instrumented seismic networks have been crucial in advancing research on earthquake statistics, physics, and subsurface Earth structures. In recent years, earthquake data from California has become increasingly valuable for deep learning applications, such as Generalized Phase Detection (GPD) for phase detection and polarity determination, and PhaseNet for phase arrival-time picking. The continuous accumulation of data, particularly those manually labeled by human analysts, serves as an essential resource for advancing both regional and global deep learning models. To support the continued development of machine learning and data mining studies, we have compiled a unified California Earthquake Event Dataset (CEED) that integrates seismic records…
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Taxonomy
TopicsSeismology and Earthquake Studies · Earthquake Detection and Analysis
