With the increasing development and utilization of natural resources, the importance of rock property characterization is becoming increasingly prominent. Artificial intelligence (AI) technology, with its rapid and accurate identification and analysis capabilities, is driving the evolution of a new generation of intelligent rock property analysis technologies. This paper systematically reviews the application and development trends of AI in rock property analysis. Key topics include: using AI methods to identify and analyze rock structure, composition, and texture; introducing commonly used AI models, analytical metrics, and public datasets in this field to help researchers more comprehensively evaluate model performance and match appropriate rock data; and summarizing AI solutions, future challenges, and coping strategies in four key areas of rock property analysis. This study emphasizes that the current application of AI methods to rock property analysis still faces challenges such as data quality, model generalization, and interpretability. To address these challenges, this paper proposes constructive suggestions, including the development of industry standards for intelligent rock analysis, the integration of petrological theory and fluid dynamics equations, and the adoption of weakly supervised learning strategies, in order to overcome existing technical bottlenecks.
Jiang et al. (Wed,) studied this question.
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