Machine learning for drill core image analysis: A review

With the growing demand for raw materials, there is also an increased need in faster and more efficient processes of mineral exploration, especially locating possible materials to mine. Machine learning (ML) for supporting the labor-intensive and interpretive process of drill core logging becomes increasingly relevant to address these needs, since drill cores are the most direct and physically preserved record of subsurface geology available during mineral exploration. This paper reviews the current state of the art in ML-based drill core analysis, including its capabilities, limitations, and specific challenges related to generalization and practical deployment within geological workflows. The review focuses specifically on photographic images of drill core, which have been routinely used in mineral exploration for decades. This paper presents several major contributions: It offers a structured overview of current methods, organized around three key geological tasks, which are lithology prediction, geotechnical analysis, and mineralogical prediction. Additionally, it identifies potential research gaps and proposes directions for future work, concluding with an emphasis on advancing context-aware machine learning in drill core analysis through a human-in-the-loop approach. • Proposes a shift from automation to assistance via geological context-aware ML, with human-in-the-loop concepts and a multi-model framework. • Identifies generalization as a research gap with a six-category framework for analysis. • Emphasizes critical synthesis in ML–geology integration, highlighting scale alignment, performance interpretation, and benchmarking challenges. • Provides a reusable framework to organize diverse ML–geology studies by linking dataset type, task, and method.