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AI Prospectors: Transforming Economic Geology with Machine Intelligence

نویسندگان :

Parinesa Moshefi¹

1- دانشگاه آزاد اسلامی واحد مراغه

کلمات کلیدی :

Artificial Intelligence،Economic Geology،Mineral Exploration،Machine Learning

چکیده :

The integration of Artificial Intelligence (AI) into economic geology has accelerated over the past decade, bringing data-driven methods to mineral prospectivity mapping, resource estimation, remote-sensing interpretation, and automated core/logging workflows. This paper provides a journal-style review of AI techniques (machine learning, deep learning, and hybrid approaches) applied to economic geology, summarizes representative case studies, discusses methodological challenges (data bias, interpretability, and uncertainty quantification), and outlines future directions including explainable AI, multi-source data fusion, and real-time decision support. The integration of AI into economic geology represents a paradigm shift in the way mineral resources are explored, modeled, and managed. The paper concludes by outlining the future perspectives of AI as a transformative tool for sustainable resource management.