TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology

TL;DR

TRACE is a multi-agent system that combines language models and seismological constraints to autonomously infer physically grounded mechanisms from seismic data, improving reproducibility and generalizability in earthquake and volcanic analysis.

Contribution

The paper introduces TRACE, a novel multi-agent system integrating language models with seismological constraints for autonomous physical reasoning in seismology.

Findings

Successfully identified stress-perturbation-induced delayed triggering in 2019 Ridgecrest sequence.

Distinguished episodic fault migration from continuous propagation in 2025 Santorini eruption.

Provides a generalizable infrastructure for physical insights from seismic data.

Abstract

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.