TRACE：一种用于地震学自主物理推理的多智能体系统
TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology
AI总结：
TRACE通过结合大语言模型规划与正式地震学约束，从原始观测中推导出可审计的物理机制，解决了地震序列物理机制推断的挑战，推动了地球科学从专家依赖分析向知识引导的自主发现发展。
摘要：
从地物理观测推断支配地震序列的物理机制仍是一个具有挑战性的任务，特别是在构造环境不同的情况下，相似的地震模式可能反映不同的底层过程。当前的地震学处理和解释严重依赖专家参数选择和各种地震学产品的合成，限制了可重复性和跨环境的知识泛化。本文提出了TRACE（Trans-perspective Reasoning and Automated Comprehensive Evaluator），一种多智能体系统，结合大语言模型规划与正式地震学约束，从原始观测中推导出可审计、基于物理的机制推断。应用于2019年Ridgecrest序列时，TRACE自动识别了由应力扰动引起的延迟触发，解决了Mw 6.4和Mw 7.1主震之间的级联相互作用。对于2025年Santorini-Kolumbo火山喷发，系统识别出由结构引导的侵入模型，区分了通过断层通道的间歇性迁移与均质地壳破坏预期的连续传播。通过提供从地震现象中推导物理见解的一般化基础设施，TRACE推动了该领域从专家依赖分析向知识引导的自主发现发展。
英文摘要：
Modern seismic networks resolve earthquake sequences in unprecedented detail, yet explaining how large earthquakes emerge from evolving fault systems remains difficult. We introduce TRACE, a seismology-guided artificial intelligence agent that plans and executes workflows while preserving auditable evidence chains from observations to physical interpretation. We evaluated TRACE through 104 benchmark tasks and two complementary earthquake sequences. For the well-studied 2019 Ridgecrest sequence, TRACE constructed a high-resolution catalog from continuous waveforms and retrospectively recovered delayed cascading activation between the Mw 6.4 and Mw 7.1 earthquakes without a prescribed target interpretation. In the less-understood 2025-2026 Sanriku sequence off northeastern Japan, TRACE developed a testable interpretation of progressive destabilization within a segmented megathrust. Its synthesis linked coupled seismic-aseismic activation around the MJ 6.9 sequence and subsequent persistent, spatially segmented shallow-interface activity to a megathrust patch that lay between regions of past large coseismic slip and later hosted the MJ 7.7 rupture. These results open a path from seismic observations to testable physical insight.
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