Agentic AI for Seismic Data Processing: Automating Outcomes, Not Workflows

Seismic data processing is challenged by limited domain expertise and manual, labor-intensive workflows, which restrict scalability and consistency. This work introduces an agentic artificial intelligence framework that automates seismic noise attenuation outcomes by embedding expert judgment within three autonomous agents: one for diagnostics, one for remediation, and one for quality control. Unlike previous approaches that focus on scripting workflows or enhancing tool usage, this solution operationalizes domain expertise, enabling agents to collaborate and deliver noise-attenuated seismic data. The system employs image similarity and feature embeddings to diagnose, remediate, and validate seismic data in a self-regulating quality loop. This proof-of-concept demonstrates practical decision-making and establishes the feasibility of fully agentic seismic processing pipelines. By automating outcomes rather than tasks, this approach addresses critical bottlenecks and provides a foundation for future development of transformative subsurface workflows.