California Earthquake Dataset for Machine Learning and Cloud Computing

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 from the Northern California Earthquake Data Center (NCEDC) and the Southern California Earthquake Data Center (SCEDC). The dataset includes both automatically and manually determined parameters such as earthquake origin time, source location, P/S phase arrivals, first-motion polarities, and ground motion intensity measurements. The dataset is organized in an event-based format organized by year spanning from 2000 to 2024, facilitating cross-referencing with event catalogs and enabling continuous updates in future years. This comprehensive open-access dataset is designed to support diverse applications including developing deep learning models, creating enhanced catalog products, and research into earthquake processes, fault zone structures, and seismic risks.