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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.21152 |
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| _version_ | 1866908913703583744 |
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| author | Liu, Feng Xu, Jian Cui, Xin Wang, Xinghao Guo, Zijie Wang, Jiong Mousavi, S. Mostafa Gu, Xinyu Chen, Hao Fei, Ben Fang, Lihua Ling, Fenghua Li, Zefeng Bai, Lei |
| author_facet | Liu, Feng Xu, Jian Cui, Xin Wang, Xinghao Guo, Zijie Wang, Jiong Mousavi, S. Mostafa Gu, Xinyu Chen, Hao Fei, Ben Fang, Lihua Ling, Fenghua Li, Zefeng Bai, Lei |
| contents | Inferring physical mechanisms that govern earthquake sequences from geophysical observations remains a challenging task, particularly across tectonically distinct environments where similar seismic patterns can reflect different underlying processes. Current seismological processing and interpretation rely heavily on experts' choice of parameters and the synthesis of various seismological products, limiting reproducibility and the formation of generalizable knowledge across settings. Here we present TRACE (Trans-perspective Reasoning and Automated Comprehensive Evaluator), a multi-agent system that combines large language model planning with formal seismological constraints to derive auditable, physically grounded mechanistic inferences from raw observations. Applied to the 2019 Ridgecrest sequence, TRACE autonomously identifies stress-perturbation-induced delayed triggering, resolving the cascading interaction between the Mw 6.4 and Mw 7.1 mainshocks. For the 2025 Santorini-Kolumbo volcanic eruption, the system identifies a structurally guided intrusion model, distinguishing episodic migration via fault channels from the continuous propagation expected in homogeneous crustal failure. By providing a generalizable infrastructure for deriving physical insights from seismic phenomena, TRACE advances the field from expert-dependent analysis toward knowledge-guided autonomous discovery in Earth sciences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21152 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology Liu, Feng Xu, Jian Cui, Xin Wang, Xinghao Guo, Zijie Wang, Jiong Mousavi, S. Mostafa Gu, Xinyu Chen, Hao Fei, Ben Fang, Lihua Ling, Fenghua Li, Zefeng Bai, Lei Geophysics Artificial Intelligence Inferring physical mechanisms that govern earthquake sequences from geophysical observations remains a challenging task, particularly across tectonically distinct environments where similar seismic patterns can reflect different underlying processes. Current seismological processing and interpretation rely heavily on experts' choice of parameters and the synthesis of various seismological products, limiting reproducibility and the formation of generalizable knowledge across settings. Here we present TRACE (Trans-perspective Reasoning and Automated Comprehensive Evaluator), a multi-agent system that combines large language model planning with formal seismological constraints to derive auditable, physically grounded mechanistic inferences from raw observations. Applied to the 2019 Ridgecrest sequence, TRACE autonomously identifies stress-perturbation-induced delayed triggering, resolving the cascading interaction between the Mw 6.4 and Mw 7.1 mainshocks. For the 2025 Santorini-Kolumbo volcanic eruption, the system identifies a structurally guided intrusion model, distinguishing episodic migration via fault channels from the continuous propagation expected in homogeneous crustal failure. By providing a generalizable infrastructure for deriving physical insights from seismic phenomena, TRACE advances the field from expert-dependent analysis toward knowledge-guided autonomous discovery in Earth sciences. |
| title | TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology |
| topic | Geophysics Artificial Intelligence |
| url | https://arxiv.org/abs/2603.21152 |