Implementation and comparative analysis of supervised machine learning methods for domain modeling and grade estimation techniques

In the face of dwindling economic mineral resources, this study addresses the critical need for reliable characterization of the mineral resources for improved extraction methods and safer, more profitable mining operations. Current resource modeling techniques are time-consuming, and prone to errors due to manual interpretation of detailed data leading to economic viability issues due to uncertainties associated with the estimates. Geostatistics, while a predominant method in resource modeling, depends on assumptions that may not always hold true, and common practices, such as variogram interpretations, and data filtering, can introduce errors early in the modeling process. Supervised Machine learning (ML) offers a promising alternative, capable of handling complex data sets for domain analysis and grade estimation. This research introduces novel geospatial estimation methods, validated through a case study on geological domains using Supervised Machine Learning methods. A comprehensive comparison analysis of various modeling and grade estimation methods is presented, highlighting the potential of supervised ML algorithms as an alternative to traditional geostatistical methods. However, the study also acknowledges the limitations of ML, emphasizing the importance of geographic validation and visualization, apart from statistical analysis, in ensuring methodological rigor. Recommendations for future work include enhancing ML algorithms through specific data feeding, improving sample relationships with variable orientation data, anisotropy, and anisotropy ratio, and integrating geochemical data to enhance predictability. The thesis serves as a foundational guide for future resource estimation endeavors using not only ML algorithms but also geostatistical methods, underscoring the necessity of methodological rigor and validation in both geostatistical and machine-learning applications.

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Mining Engineering

Grantor dc:publisher

Colorado School of Mines. Arthur Lakes Library

Year dc:date.issued

2023

Author dc:creator

• ASLAN, ERKAN UGUR

Advisor dc:contributor.advisor

• Dagdelen, Kadri

Committee members dc:contributor.committeemember

• Miller, Hugh B.
• Enders, M. Stephen

• domain modeling
• geological modeling
• geostatistics
• grade estimation
• machine learning
• resource estimation

Statement dc:rights

• Copyright of the original work is retained by the author.

Language dc:language.iso

eng, English

Identifier

T 9696

OAI identifier oai:identifier

oai:repository.mines.edu:11124/179130