Advancing Mineral Prospectivity Mapping (MPM) Using Machine Learning (ML) and Data Science Techniques

Mineral Prospectivity Mapping (MPM) has undergone significant transformation with the integration of Machine Learning (ML) and data science methodologies, enhancing the efficiency and accuracy of mineral exploration. This paper discusses the diverse data types utilized in MPM, including geological, geochemical, geophysical, and remote sensing data, which collectively provide a comprehensive understanding of mineralization processes. Advanced ML techniques, such as Random Forest, Support Vector Machines, and deep learning algorithms, are employed to automate pattern recognition and predictive modeling, allowing for the identification of prospective mineral deposits with greater precision. The study highlights the importance of addressing data imbalance and employing hybrid models to optimize predictive performance. Furthermore, the application of graph deep learning techniques demonstrates improved mapping effectiveness by capturing spatial relationships in exploration data. As the demand for critical minerals continues to rise, the advancements in MPM through data-driven approaches are essential for sustainable resource development, ultimately reducing exploration risks and costs while enhancing the success rates of mineral exploration efforts.

کلیدواژه ها:

Mineral Prospectivity Mapping (MPM) ، Machine Learning (ML)- Data Science Techniques ، Mineral Exploration ، Deep Learning (DL)