Open AccessAssessment of Ore Grade Estimation Methods for Structurally Controlled Vein Deposits - A Review
Author(s) -
Clara Akalanya Abuntori,
S Al-Hassan,
D. Mireku-Gyimah
Publication year - 2021
Publication title -
ghana mining journal
Language(s) - English
Resource type - Journals
eISSN - 2616-1478
pISSN - 0855-210X
DOI - 10.4314/gm.v21i1.4
Subject(s) - geostatistics , kriging , estimation , artificial neural network , geology , resource (disambiguation) , computer science , artificial intelligence , machine learning , statistics , mathematics , engineering , spatial variability , computer network , systems engineering
Resource estimation techniques have upgraded over the past couple of years, thereby improving resource estimates. The classical method of estimation is less used in ore grade estimation than geostatistics (kriging) which proved to provide more accurate estimates by its ability to account for the geology of the deposit and assess error. Geostatistics has therefore been said to be superior over the classical methods of estimation. However, due to the complexity of using geostatistics in resource estimation, its time-consuming nature, the susceptibility to errors due to human interference, the difficulty in applying it to deposits with few data points and the difficulty in using it to estimate complicated deposits paved the way for the application of Artificial Intelligence (AI) techniques to be applied in ore grade estimation. AI techniques have been employed in diverse ore deposit types for the past two decades and have proven to provide comparable or better results than those estimated with kriging. This research aimed to review and compare the most commonly used kriging methods and AI techniques in ore grade estimation of complex structurally controlled vein deposits. The review showed that AI techniques outperformed kriging methods in ore grade estimation of vein deposits.
Keywords: Artificial Intelligence, Neural Networks, Geostatistics, Kriging, Mineral Resource, Grade