Paper submitted to IMAGE 2026, by Long Chen, Chun-Hao Liu, Suren Gunturu, Sujitha Martin, Arunabha Datta, Vidya Sagar Ravipati (AWS Generative AI Innovation Center), Ben Lasscock, Brian Michell (TGS).
Abstract
Energy companies hold millions of legacy seismic files in SEG-Y format with inconsistent, fragmented metadata that prevents automated processing and AI integration. We present a multi-agent AI system that automates end-to-end meta-data reconstruction for large-scale SEG-Y migration to MDIO v1.0, a modern self-describing seismic format. Our system addresses three coupled challenges: (1) seismic product type classification across a canonical taxonomy spanning 2D/3D, pre-stack/post-stack, and marine/land/OBN acquisition modes, (2) header field extraction from free-form text and binary headers, and (3) schema mapping to standardized MDIO coordinates and dimensions. Built on Claude Sonnet 4 with no fine-tuning, our agent-based pipeline achieves 95% accuracy in template classification, 99.17% in field extraction, 98.16% in schema mapping, and 96.83% end-to-end accuracy, surpassing human baseline performance while processing files at 90 seconds per file. Evaluated on expert-labeled samples spanning diverse acquisition types and header conventions, the system demonstrates production-ready performance for migrating TGS’s archive of 1.4+ million SEG-Y files. This approach transforms a previously manual, expert-dependent process into a scalable, automated workflow, enabling AI-ready seismic data management at cloud scale.
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