Contribution of artificial intelligence to mineral reserves modeling
| dc.contributor.author | DERKAOUI Abderrahmane Saad | |
| dc.contributor.author | CHENITI Hamza (Encadrant) | |
| dc.date.accessioned | 2024-12-09T12:38:33Z | |
| dc.date.available | 2024-12-09T12:38:33Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The responsible extraction and utilization of natural resources from minerals to petroleum hinge on a critical first step accurately assessing the quantity and quality of these resources within the Earth. Reserve modeling, where geological data is analyzed and interpreted to predict the distribution and properties of these valuable materials, plays a central role in economic planning, investment decisions, and sustainable resource management. Traditional methods like Kriging face challenges in complex scenarios characterized by non-normal data distributions and spatial heterogeneity. To address these challenges, the integration of advanced machine learning techniques has emerged as a transformative approach, improving predictive accuracy and providing greater flexibility in mineral reserve estimation. | |
| dc.identifier.uri | http://172.16.16.5:4000/handle/123456789/321 | |
| dc.language.iso | en | |
| dc.publisher | ECOLE NATIONALE SUPERIEURE DE TECHNOLOGIE ET D’INGENIERIE - ANNABA | |
| dc.title | Contribution of artificial intelligence to mineral reserves modeling | |
| dc.type | Thesis |