用图检索增强生成技术，让地震目录自动变可查询知识图谱。
Automatic Knowledge Graph Construction and Query for Earthquake Catalogs
- 直接对原始表格数据构建可查询的知识图谱，无需人工整理。
- 在三个地震序列上验证，能准确回答复杂问题并识别机制错误。
- 适合地震学家快速分析灾后序列，降低主观判断偏差。
近年来，由于更高效的深度学习检测器和相位拾取器的应用，地震目录事件数量显著增加，但回答如‘该序列有何特征？’等开放性问题仍受限于固定时空窗口和主观专家判断。本文首次系统应用基于图的检索增强生成（GraphRAG）技术，直接处理三个独立地震目录——水库邻近群震、2019年里奇克雷斯构造序列和2021年玛多Mw7.4余震序列的原始表格记录。无需手动数据结构化，该流程为所有三组数据构建了结构完整、可查询的知识图谱。通过与目录衍生的真实答案及规则基参考图对比的严格评估，揭示了潜在失效模式；经四个地震学启发的提示修复后，所有目标伪造均被消除，并显著提升机制推理能力。向量式RAG基线对比凸显图层的独特价值：实现全目录摘要与时间阶段比较。此外，我们识别出两大关键陷阱需关注。因此，GraphRAG为地震目录提供了一种实用、可迁移、近乎零成本的查询接口，精心设计的提示确保结果始终准确可信。
原文摘要 · Abstract (English)
In recent years, the number of events in earthquake catalogs has significantly increased due to the utilization of more effective deep learning based detectors and phase pickers but answering open ended questions such as what characterizes this sequence? remains constrained by rigid spatiotemporal windowing and subjective expert interpretation. We present the first systematic application of graph based retrieval augmented generation GraphRAG directly to raw, tabular catalog records across three independently featured catalogs, a reservoir adjacent swarm, the 2019 Ridgecrest tectonic sequence, and the 2021 Maduo Mw7.4 aftershock sequence. Without the need for manual data structuring, the pipeline builds structurally complete, queryable knowledge graphs for all three. Rigorous evaluation individually verified against catalog derived ground truth and a rule based reference graph exposes failure modes, and four seismology informed prompt fixes eliminate all targeted fabrications while sharply improving mechanism reasoning. A vector RAG baseline demonstrates the graph layers distinctive value, catalog wide summarization and temporal stage comparison. In addition, we have identified two main pitfalls that need attention. GraphRAG thus offers a practical, transferable, near zero cost query interface for earthquake catalogs, where careful prompting ensures the results are consistently accurate and trustworthy.
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