From Metadata to Embeddings: Enabling Agentic AI for Subsurface Intelligence

B. Lasscock; D. Arunabha; L. Chen; M. Gajula; K. Gonzalez; C. Liu; B. Michell; S. Namasivayam; V.S. Ravipati; A. Sansal; M. Sujitha; G. Suren; A. Valenciano

doi:10.3997/1365-2397.fb2026015

ISSN: 0263-5046
E-ISSN: 1365-2397

From Metadata to Embeddings: Enabling Agentic AI for Subsurface Intelligence
Authors B. Lasscock¹, D. Arunabha², L. Chen², M. Gajula¹, K. Gonzalez¹, C. Liu², B. Michell¹, S. Namasivayam¹, V.S. Ravipati², A. Sansal¹, M. Sujitha², G. Suren² and A. Valenciano¹
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¹ TGS ASA ² AWS Generative AI Innovation Center
Publisher: European Association of Geoscientists & Engineers
Source: First Break, Volume 44, Issue 2, Feb 2026, p. 71 - 78
DOI: https://doi.org/10.3997/1365-2397.fb2026015
- Published online: 01 Feb 2026

Abstract

This article presents a practical framework for AI-assisted subsurface data access based on explicit data representations, agent-based workflows, and efficient information retrieval. We demonstrate large-scale conversion of SEG-Y archives into self-describing MDIO v1 datasets and present a case study on agent-driven reconstruction of seismic metadata from legacy text headers. A second case study evaluates embedding-based retrieval across acquisition and processing reports, showing that vector quantisation and graph-based indexing enable low-latency, relevance-driven search. These capabilities are integrated into an interactive, multi-agent system that supports natural-language analysis and coordinated access to structured and unstructured subsurface information.

EAGE Publications BV

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2026-02-16

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References

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From Metadata to Embeddings: Enabling Agentic AI for Subsurface Intelligence

First Break 44, 71 (2026); https://doi.org/10.3997/1365-2397.fb2026015

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From Metadata to Embeddings: Enabling Agentic AI for Subsurface Intelligence

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