ECOLOGICALLY ORIENTED MINERAL EXPLORATION: THE SYNERGY OF REMOTE SENSING AND MACHINE LEARNING

Abstract

The article explores the prospects of integrating remote sensing (RS) and machine learning (ML) in mineral exploration. The combination of these advanced technologies opens new horizons for geological research, enabling more efficient and accurate identification of mineralization, mapping of lithological units, and analysis of structural features and alteration zones. The paper examines current methods of processing remote sensing data using various machine learning algorithms, including deep neural networks and clustering methods. Special emphasis is placed on practical examples demonstrating the successful application of RS and ML synergy in geological exploration. The main challenges and future research prospects in this area are also discussed, highlighting the potential to significantly enhance the efficiency and sustainability of mineral exploration processes.