Seismology modeling agent: A smart assistant for geophysical researchers

TL;DR

This paper introduces an AI-powered interactive workflow for seismic wave simulation that simplifies complex processes, making geophysical research more accessible and efficient through a novel Model Context Protocol (MCP) server suite.

Contribution

It presents the first application of MCP technology to computational seismology, enabling intent-driven interactions and reducing manual operations in SPECFEM workflows.

Findings

Workflow operates seamlessly in autonomous and interactive modes.

High-fidelity results consistent with standard baselines.

Significantly lowers entry barriers for seismic simulation.

Abstract

To address the steep learning curve and reliance on complex manual file editing and command-line operations in the traditional workflow of the mainstream open-source seismic wave simulation software SPECFEM, this paper proposes an intelligent, interactive workflow powered by Large Language Models (LLMs). We introduce the first Model Context Protocol (MCP) server suite for SPECFEM (supporting 2D, 3D Cartesian, and 3D Globe versions), which decomposes the entire simulation process into discrete, agent-executable tools spanning from parameter generation and mesh partitioning to solver execution and visualization. This approach enables a paradigm shift from file-driven to intent-driven conversational interactions. The framework supports both fully automated execution and human-in-the-loop collaboration, allowing researchers to guide simulation strategies in real time and retain scientific decision-making authority while significantly reducing tedious low-level operations. Validated through multiple case studies, the workflow operates seamlessly in both autonomous and interactive modes, yielding high-fidelity results consistent with standard baselines. As the first application of MCP technology to computational seismology, this study significantly lowers the entry barrier, enhances reproducibility, and offers a promising avenue for advancing computational geophysics toward AI-assisted and automated scientific research. The complete source code is available at https://github.com/RenYukun1563/specfem-mcp.