GeoSIRR 1.0: Conversational Geological Cross-Section Modeling Using Large Language Models

Abstract. Geological cross-sections are a fundamental tool for subsurface interpretation, yet their construction remains a labor-intensive and largely manual process that relies on expert judgment and structured numerical inputs. While recent advances in artificial intelligence have enhanced specific geoscientific workflows, no existing method enables the direct generation and iterative refinement of geological cross-sections from unstructured natural language descriptions. In this paper, we present GeoSIRR 1.0 (Geological Section Interpretation, Reconstruction & Refinement), a novel modeling framework that leverages large language models (LLMs) to translate free-form geological narratives into structured, coordinate-based cross-section geometries. GeoSIRR introduces a domain-specific language (DSL) for representing geological bodies as topologically consistent polygons and integrates automated geometric and geological validation to ensure continuity, stratigraphic consistency, and structural plausibility. The framework supports both initial model generation and conversational refinement, allowing users to iteratively modify cross-sections using natural language commands while preserving existing geometry. We demonstrate the capabilities of GeoSIRR through multiple geological scenarios, including faulted sedimentary systems, intrusive bodies, and progradational deltaic sequences, and assess repeatability across multiple generation runs. Results show that GeoSIRR consistently produces geologically plausible cross-sections and effectively incorporates conceptual refinements with reduced generation time compared to initial model construction. By directly linking qualitative geological reasoning with quantitative geometric modeling, GeoSIRR provides a self-contained, dialogue-driven approach to cross-section construction that complements existing modeling tools and offers new opportunities for education, exploratory analysis, and rapid scenario development in subsurface geoscience.