Objective Faced with the increasing difficulty in exploring deep and concealed ore deposits, traditional methods have shown their limitations. Therefore, systematically evaluating the pathways and potential of an AI−driven paradigm shift in mineral exploration is of great practical significance.
Methods This paper systematically sorts out the development context and key technologies of AI in the field of geological prospecting, and reveals the in−depth coupling relationship between technological evolution and the demands of geological prospecting. It elaborates on the application mechanisms and practical effects of AI in key scenarios such as geological mapping, mineralization prediction, mineral identification, and geophysical inversion.
Results Despite its broad application prospects, artificial intelligence still faces key technical bottlenecks at present, such as difficulties in fusing multi−source heterogeneous geological data, insufficient generalization ability of prospecting models, and the construction of geological large models.
Conclusions This paper argues that realizing data standardization, developing knowledge−guided intelligent prospecting models, and constructing large mineral geological models are the keys to breaking through current bottlenecks. The collaborative research of these three directions will substantially promote the paradigm shift of geological work from "experience−driven" to an intelligent decision−making paradigm driven by "data and knowledge integration".
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