TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology
Organizations: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. · Shanghai Artificial Intelligence Laboratory, Shanghai, China. · School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China. · Department of Earth and Planetary Sciences,McGill University, Montreal, Canada. · Department of Earth and Planetary Sciences, Harvard University, Cambridge, United States of America. · Institute of Earthquake Forecasting, China Earthquake Administration, Beijing, China.
Abstract
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.
Figures & tables
| Levels | Description | Number of Tasks |
| Level 1: Atomic task capability | Performs fundamental seismological operations with procedural correctness and execution stability, including seismic data acquisition, format conversion, preprocessing, signal feature extraction, and scientific visualization. Emphasizes accurate tool invocation, parameter handling, and computational consistency in routine seismic data processing workflows. | 74 |
| Level 2: Multi-step analytical capability | Solves workflow-level seismological problems requiring coordinated multi-tool orchestration and dependency management. Integrates sequential toolchains, heuristic branching strategies, large-scale batch scanning, and parameter-space exploration under physical constraints. Evaluates adaptive pathway selection, intermediate state management, and long-chain analytical coherence. | 30 |
| Level 3: Integrated scientific analysis capability | Conducts end-to-end scientific investigations under realistic research scenarios. Integrates cross-module data processing, multi-perspective analysis, hypothesis formulation, and evidence synthesis to assemble evidence-supported, testable interpretations. Assesses workflow coherence, cross-task integration, and interpretation boundaries in complex seismological studies. | 2 |
| Score | Quantitative Accuracy | Scientific Correctness | Visualization and Reporting Quality |
| 5 points | Numerical outputs match reference solutions within predefined numerical tolerances. | All results are scientifically valid, logically consistent, and fully aligned with task objectives. No conceptual or methodological errors. | Figures are accurate, clearly labeled (axes, units, scales), and publication-ready. Visual design enhances interpretability. Text and figures are fully consistent. |
| 4 points | Minor numerical deviations from reference results but within acceptable tolerance. No impact on overall conclusions. | Results are scientifically correct with only minor interpretative omissions or limited analytical depth. | Figures are correct and interpretable, but minor issues exist (e.g., formatting inconsistencies, limited annotations). Overall presentation is clear. |
| 3 points | Moderate deviation from reference values, but core trends or magnitudes remain correct. | Partial correctness: main scientific conclusion is identifiable, but secondary errors or incomplete interpretation exist. | Figures are understandable but contain noticeable labeling issues, limited clarity, or insufficient explanation. |
| 2 points | Significant numerical errors or unstable results that affect reliability. | Results contain conceptual misunderstandings or incorrect scientific interpretations. | Figures are poorly constructed or partially incorrect, limiting interpretability. |
| 1 point | Numerical results are incorrect or fail to match reference benchmarks. | Scientific conclusions are invalid or inconsistent with task requirements. | No meaningful figures or coherent result presentation provided. |
| Provenance layer | Information retained | Audit function |
| Input boundary | Scientific question; data sources; spatial and temporal selections; permitted contextual information; stage at which external evidence entered | Establishes what information was available at each stage |
| Planning | Physical constraints; candidate physical interpretations; selected and retained analysis branches | Links the scientific question to the analyses without prescribing a conclusion |
| Execution | Tool choices; executable scripts; parameter settings; input–output paths; intermediate products | Reconstructs the computational path from observations to analytical products |
| Diagnostics and revision | Computational and physical checks; failed steps; diagnostic flags; permitted revisions; intervention status and source, when applicable | Distinguishes accepted outputs from unresolved failures and records how the workflow changed |
| Evidence extraction | Source data product; method and parameters; diagnostic status; interpretation-relevant observation | Links each evidence statement to a checked analytical output |
| Synthesis | Supporting, inconsistent or unresolved relations; evidence-supported interpretation; viable alternatives; external validation targets | Links the physical interpretation to the evidence while preserving its boundaries |
| Benchmark group | Benchmark category | Capability tested | Operation pattern | Tasks (n) |
| L1-A | Data access and retrieval | Data retrieval | Single-step | 7 |
| L1-B | Data representation and formatting | Data formatting | Single-step | 12 |
| L1-C | Signal processing | Signal processing | Single-step | 22 |
| L1-D | Statistics and feature extraction | Statistical feature extraction | Single-step | 18 |
| L1-E | Scientific visualization | Scientific plotting | Single-step | 15 |
| Level 1 subtotal | 74 | |||
| Task ID | Benchmark category | Representative task | Operation pattern | Complexity |
| L1-A-1 | Data access and retrieval | Download an event catalog from an FDSN server | Single-step | Low |
| L1-B-1 | Data representation and formatting | Convert ASCII waveform data to MiniSEED | Single-step | Low |
| L1-C-1 | Signal processing | Apply an AIC picker to a seismic trace | Single-step | Low |
| L1-D-1 | Statistics and feature extraction | Compute an array response function | Single-step | Low |
| L1-E-1 | Scientific visualization | Plot a focal-mechanism beachball from a QuakeML event | Single-step | Low |
| L2-A-1 | Sequential chaining | Estimate back azimuth from STEAD waveforms using BazNet | Multi-step | Medium |
| Model | Model–task runs | Success rate | Statistical analysis rating | Visualization rating | Mean correction rounds | Mean score |
| Claude Sonnet 4 | 104 | 98.08% | 4.49 | 4.88 | 2.28 | 4.79 |
| Gemini 3 Flash preview | 104 | 100.00% | 4.64 | 4.86 | 2.26 | 4.82 |
| GPT-5.1 | 104 | 100.00% | 4.72 | 4.83 | 1.64 | 4.85 |
| Task ID | Case study | Scientific question | Workflow inputs and information boundary | Required report |
| L3-001 | Ridgecrest | How was seismicity organized between the 6.4 foreshock and the 7.1 mainshock? | Initial observations: continuous waveforms and station metadata; auxiliary geospatial data: mapped surface-rupture traces; quality control and alignment: reference catalogs | Evidence-supported physical interpretation and viable alternatives, followed by retrospective comparison with published interpretations after the analysis was complete |
| L3-002 | Sanriku | What evidence constrains shallow-interface activation before the April 2026 7.7 rupture? | Initial observations: JMA event catalog and phase-arrival records; analysis-stage data: event waveforms for repeating-earthquake analysis; synthesis-stage constraints: historical rupture models and convergence estimates; external validation: independent S-net strain observations | Evidence-supported physical interpretation, viable alternatives and external validation targets |
| Dimension | Assessment criterion | Common failure mode | Assessment evidence |
| Planning | Scientific question decomposed into executable, physically relevant analyses | Missing analysis branch or unsupported assumption | Workflow plan and task decomposition |
| Implementation | Executable scripts and tool calls linked to expected products | Broken execution, unstable parameters or missing quality control | Code, logs and output products |
| Synthesis | Report separates observations from interpretation and records viable alternatives and external validation targets | Overclaiming, unsupported interpretation or unacknowledged uncertainty | Structured report and expert review |
| Processing output | Count (n) | Workflow use |
| Selected three-component stations | 47 | Waveform processing and phase picking station set |
| Stations used for association and relocation | 30 | Stations nearest to the 6.4 epicenter |
| Quality-controlled station-day waveform inputs | 853 | Waveform inputs retained after station-day quality control |
| arrivals retained after PhaseNet filtering | 1,454,311 | Thresholded PhaseNet picks before 30-station association |
| arrivals retained after PhaseNet filtering | 1,641,210 | Thresholded PhaseNet picks before 30-station association |
| Picks assigned to associated events | 2,120,090 | Event-level picks after station and association filtering |
| Stage | Setting | Value |
| Phase picking | PhaseNet probability threshold | ; |
| Phase picking | Waveform sampling rate | 100 Hz |
| Association | Association model | GaMMA Bayesian Gaussian-mixture model |
| Association | DBSCAN pre-clustering | Enabled; km; minimum samples, 3 |
| Association | Oversampling factor | 3 |
| Association | Amplitude term | Not used |
| Catalog | Events | Median | Use in TRACE audit | |||
| Ross et al. high-resolution catalog, [ 35 ] | 4,138 | 2,499 | 605 | 111 | 1.15 | High-resolution reference |
| USGS/ANSS ComCat (USGS/CI) [ 45 ] | 3,484 | 2,686 | 704 | 145 | 1.37 | Operational reference |
| TRACE associated catalog before relocation | 6,351 | 3,907 | 817 | 163 | 1.14 | Unrelocated TRACE reference |
| TRACE HypoDD relocated catalog | 4,714 | 3,205 | 734 | 151 | 1.23 | Final catalog used for diagnostic tests |