Geospatial Intelligence in Mineral Exploration: Enhancing Mineral Prospectivity Mapping (MPM) with Python and Artificial intelligence (AI)

In recent years, the integration of artificial intelligence (AI) and machine learning with geospatial intelligence has revolutionized mineral exploration, particularly in the realm of mineral prospectivity mapping (MPM). This paper explores the methodologies and advancements in utilizing Python programming and AI algorithms to enhance predictive capabilities in identifying potential mineral deposits. By automating data processing and pattern recognition, these technologies significantly reduce the time and costs associated with exploration while improving accuracy. The study reviews various applications of AI, including deep learning and neural networks, and highlights the role of Geographic Information Systems (GIS) in overlaying diverse geological, geochemical, and remote sensing data. Despite the promising advancements, challenges such as data quality, computational intensity, and the complexity of geological systems persist. This paper aims to provide a comprehensive overview of the current state-of-the-art techniques in geospatial intelligence for mineral exploration, emphasizing the transformative impact of these technologies and outlining future directions for research and application in the field. Ultimately, the integration of AI and geospatial tools is poised to enhance decision-making processes and promote sustainable practices in mineral resource management.

Keywords:

Mineral Prospectivity Mapping (MPM) , Artificial intelligence (AI) , Python , Mineral Exploration , Geographic Information Systems (GIS)