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Państwowa służba geologiczna planuje wykorzystać sztuczną inteligencję i uczenie maszynowe do analizy danych i poszukiwania surowców krytycznych na Dolnym Śląsku

Karcz Przemysław

Przegląd Geologiczny

2025

Vol. 73, nr 8

744--745

Artificial intelligence (AI) and machine learning (ML) are becoming increasingly common tools in geological data analysis, offering new opportunities in data processing, prediction, and decision support. This article presents a proposal for implementing AI and ML solutions within the operations of the Polish Geological Sur-vey (PSG), which has collected vast geological data resources over more than 100 years. The project, initiated by the Lower Silesian Branch of PSG, aims to integrate dispersed data and use it for exArtificial intelligence (AI) and machine learning (ML) are becoming increasingly common tools in geological data analysis, offering new opportunities in data processing, prediction, and decision support. This article presents a proposal for implementing AI and ML solutions within the operations of the Polish Geological Sur-vey (PSG), which has collected vast geological data resources over more than 100 years. The project, initiated by the Lower Silesian Branch of PSG, aims to integrate dispersed data and use it for exploration purposes, including the identification of new prospective areas for resources such as critical raw materials. The article also discusses existing examples of AI applications in geology -particularly in the oil and gas sector - and highlights potential risks related to data quality, model interpretability, and acceptance within the expert community. It emphasizes that the success of AI implementation depends on close collaboration between specialists and technical teams, as well as on phased approach to deployment. The paper aligns with current trends in the digital transformation of geosciences and serves as a starting point for further research based on advanced technologies.ploration purposes, including the identification of new prospective areas for resources such as critical raw materials. The article also discusses existing examples of AI applications in geology -particularly in the oil and gas sector - and highlights potential risks related to data quality, model interpretability, and acceptance within the expert community. It emphasizes that the success of AI implementation depends on close collaboration between specialists and technical teams, as well as on phased approach to deployment. The paper aligns with current trends in the digital transformation of geosciences and serves as a starting point for further research based on advanced technologies.