Assimilation of different data types with machine learning methods for resource classification

Classification of mineral resources based on geological confidence level is mandated for disclosure by all of the international codes and standards. However, the definition of the “confidence level” is vague in all of these codes mostly because of the fact that every deposit has its own characteristics. This research attempts to establish a method that utilizes advanced statistical methods to quantify this confidence level that transparent and practicable enough so that it can be adopted by the industry. A practical approach to uncertainty quantification task could provide the partner company which is one of the pioneer service company in this field, with an alternative reliable service to provide its clients.

Faculty Supervisor:

Julian M Ortiz

Student:

Partner:

SRK Consulting (ON)

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Queen's University

Program: