地震自动编目技术的进展与反思：在智能处理时代

周一剑; 周仕勇

doi:10.3799/dqkx.2025.177

Volume 51 Issue 2

Feb. 2026

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Article Navigation > Earth Science > 2026 > 51(2): 690-702

Zhou Yijian, Zhou Shiyong, 2026. Rethinking the Advances and Challenges of Contemporary Auto-Cataloging Workflows: In the AI Processing Era. Earth Science, 51(2): 690-702. doi: 10.3799/dqkx.2025.177

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Zhou Yijian, Zhou Shiyong, 2026. Rethinking the Advances and Challenges of Contemporary Auto-Cataloging Workflows: In the AI Processing Era. Earth Science, 51(2): 690-702. doi: 10.3799/dqkx.2025.177

Zhou Yijian, Zhou Shiyong, 2026. Rethinking the Advances and Challenges of Contemporary Auto-Cataloging Workflows: In the AI Processing Era. Earth Science, 51(2): 690-702. doi: 10.3799/dqkx.2025.177

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Rethinking the Advances and Challenges of Contemporary Auto-Cataloging Workflows: In the AI Processing Era

doi: 10.3799/dqkx.2025.177

Zhou Yijian^{1
,
,},
Zhou Shiyong^{2
,
,}

1.
Division of Geological and Planetary Sciences, California Institute of Technology, CA 91125, USA
2.
Department of Artificial Intelligence and Data Sciences, Xinhua University, Guangzhou 510520, China

Received Date: 2025-07-15
Publish Date: 2026-02-25

Abstract

Abstract

With AI-based automatic cataloging techniques increasingly becoming the mainstream, the limited generalization ability of pre-trained models has also emerged as a widely recognized issue. Through a review of several recent studies and a set of simple tests, this paper seeks to highlight this technological bottleneck and to outline perspectives on future development. On the one hand, the evaluation framework for AI models is in urgent need of updating: the prevailing assessment approaches based on manual annotations exhibit inherent limitations and often lack practical relevance for specific user applications. On the other hand, research on the relationship between training data and model performance remains at an early stage. Although different strategies have been proposed to address generalization issues, systematic discussions of this complex problem are still lacking. This paper aims to provide directional recommendations on potential pathways to overcome these challenges, with the hope of offering useful insights to researchers engaged in AI-based earthquake cataloging.
- AI algorithms,
- earthquake cataloging,
- generalization,
- evaluation metrics,
- training datasets

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References(65)

References

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