发表机构
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出TREMORS智能体框架，可将自然语言查询转换为结构化工作流，自动化跨多数据中心的地震数据检索，减轻用户负担并提升可复现性，为地震学研究提供支持。
AI 中文摘要
地震学越来越依赖跨多数据中心平台的大规模数据检索，但数据获取往往需要领域专业知识、用户承担繁重负担，且难以复现。随着数据档案不断增长，将科学意图转化为可跨多个知识库运行、生成高质量AI就绪数据的结构化工作流，正成为日益紧迫的挑战。本文提出TREMORS（地震仪的文本参考事件映射与输出渲染器），这是一种在受限执行图中利用大语言模型推理的智能体框架，用于自动化地震数据检索。TREMORS将自然语言查询转换为结构化中间模式，该模式通过受限LangGraph工作流驱动执行。示例工作流表明其支持基于事件和连续波形的两种采集模式。该框架通过可移植模式和模块化工作流组件设计，可扩展至异构多数据中心系统。本研究将智能体工作流定位为连接科学意图与分布式地震数据系统的催化剂，有望实现可复现、数据驱动的研究未来，同时减轻常规采集任务的负担。
英文摘要
Seismology increasingly depends upon large data retrieval across multi-datacenter platforms. Yet, data procurement often demands domain expertise, user burden, and is difficult to reproduce. As archives continue to grow, translating scientific intent into structured workflows that operate across multiple repositories and produce high quality, AI ready data is becoming an increasingly urgent challenge. We present TREMORS (Text Referenced Event Mapping and Output Renderer for Seismographs), an agentic framework that uses large language model reasoning within a constrained execution graph to automate seismic data retrieval. TREMORS translates natural language queries into a structured intermediate schema, which drives execution through a constrained LangGraph workflow. Example workflows demonstrate support for both event-based and continuous waveform acquisition. The framework is designed to extend across heterogeneous multi-datacenter systems through a portable schema and modular workflow components. This work positions agentic workflows as the catalyst that will connect scientific intent with distributed seismic data systems, enabling a future of reproducible, data-driven inquiry while reducing the burden of routine acquisition tasks.
