Geoscience-Aware AI for Interpretable Seismic Interpretation of Mass Transport Deposits Using Knowledge Graphs and Large Language Models

Seismic interpretation of mass transport deposits (MTDs) relies heavily on expert knowledge and conceptual reasoning yet remains difficult to formalize and scale. While recent artificial intelligence (AI) methods have shown strong capabilities in seismic pattern recognition, most approaches operate as black boxes and remain poorly aligned with the interpretative frameworks used by geoscientists, limiting transparency and trust.

 

This study proposes a geoscience-aware hybrid intelligence framework that integrates expert knowledge graphs (KGs) with large language models (LLMs) to support interpretable seismic interpretation of MTDs. The approach builds upon the conceptual methodology of Le Bouteiller et al. (2019), which organizes MTD interpretation through causal relationships linking environmental controls, mass transport properties, and observable seismic descriptors across trigger, transport, and post-deposition phases.

 

The KG provides a structured reference for interpretation that constrains vocabulary, causal direction, and temporal logic. Our workflow reads scientific papers, identifies relevant descriptors and processes, checks them with LLMs, and evaluates how well they support interpretation. In this setup, seismic descriptors give different levels of support (weak to strong) for geological processes, like how experts reason under uncertainty.

Preliminary results show that ~68% of expert defined concepts are recovered in the inferred graph, with a semantic validation score of 0.73, indicating good conceptual alignment. However, descriptor matching based on textual similarity remains difficult, with average scores around 0.41. This gap highlights the difference between semantic agreement (conceptually correct) and textual agreement (exact wording), mainly due to synonymy and variable phrasing in the literature. We plan to address this by using domain-specific LLMs and ontology-based synonym expansion to improve semantic matching in future iterations