INTEGRATION OF MACHINE LEARNING AND GEOSTATISTICS FOR DOMAINING: A DATA AUGMENTATION PRACTICE IN A TAILING STORAGE FACILITY

Abstract

Tailings Storage Facilities (TSFs) pose a multifaceted problem in mining owing to their environmental ramifications. One of the challenges in managing sulfidic TSFs is the presence of elevated sulfur (S) and iron (Fe) levels, which can lead to environmental contamination. This occurs through the generation of acid mine drainage (AMD), impacting surrounding soils, water, and vegetation. Traditional geostatistical techniques, however, struggle to accurately delineate compact and contiguous areas of these zones, often resulting in patchy and irregular clusters that are challenging to interpret and manage. This thesis introduces a novel approach that integrates machine learning (ML) and data augmentation with geostatistical simulations, incorporating variogram component filtering to delineate compact hazardous zones within the studied domains more effectively.