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

TL;DR

GeoMind introduces an agentic, tool-augmented framework for lithology classification that models sequential reasoning, improving accuracy and interpretability over static methods by integrating geological constraints and evidence-based reasoning.

Contribution

It presents GeoMind, a novel framework that organizes perception, reasoning, and analysis modules for lithology classification, with a global planner and process supervision for logical consistency.

Findings

GeoMind outperforms baseline methods on four well-log datasets.

It provides transparent, traceable decision-making processes.

The framework ensures geological plausibility through intermediate supervision.

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.