GeoMind: An Agentic Workflow for Lithology Classification with Reasoned Tool Invocation

2026-04-23

AI Summary

This study addresses the limitations of conventional well-log lithology classification methods, which often reduce the task to a static, single-step discrimination process, neglecting geological reasoning and the integration of multi-source evidence, thereby yielding unrealistic results. To overcome this, we propose GeoMind—the first framework to incorporate agent-based workflows into lithology classification—formulating the task as a multi-stage, sequential reasoning process. GeoMind coordinates perception, reasoning, and analysis modules under the dynamic orchestration of an adaptive global planner. Crucially, it introduces a fine-grained process supervision mechanism that optimizes intermediate reasoning steps to ensure logical consistency and geological plausibility in decision trajectories. Experimental results demonstrate that GeoMind significantly outperforms strong existing baselines across four standard well-log datasets while delivering transparent and traceable decision pathways.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Reasoning under Uncertainty: Sequential Decision Making
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Lithology classification in well logs is a fundamental geoscience data mining task that aims to infer rock types from multi dimensional geophysical sequences. Despite recent progress, existing approaches typically formulate the problem as a static, single-step discriminative mapping. This static paradigm limits evidence-based diagnostic reasoning against geological standards, often yielding predictions that are detached from geological reality due to a lack of domain priors. In this work, we propose GeoMind, a tool-augmented agentic framework that models lithology classification as a sequential reasoning process. GeoMind organizes its toolkit into perception, reasoning, and analysis modules, which respectively translate raw logs into semantic trends, infer lithology hypotheses from multi-source evidence, and verify predictions against stratigraphic constraints. A global planner adaptively coordinates these modules based on input characteristics, enabling geologically plausible and evidence-grounded decisions. To guarantee the logical consistency of GeoMind, we introduce a fine-grained process supervision strategy. Unlike standard methods that focus solely on final outcomes, our approach optimizes intermediate reasoning steps, ensuring the validity of decision trajectories and alignment to geological constraints. Experiments on four benchmark well-log datasets demonstrate that GeoMind consistently outperforms strong baselines in classification performance while providing transparent and traceable decision-making processes.

Problem

• lithology classification
• well logs
• geological reasoning
• domain priors
• evidence-based diagnosis

Innovation

• agentic workflow
• tool-augmented reasoning
• lithology classification
• process supervision
• geological constraints