Version 1
: Received: 2 March 2024 / Approved: 2 March 2024 / Online: 4 March 2024 (09:29:05 CET)
How to cite:
Goldstein, D.; Aldrich, C.; O'Connor, L. A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data. Preprints2024, 2024030079. https://doi.org/10.20944/preprints202403.0079.v1
Goldstein, D.; Aldrich, C.; O'Connor, L. A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data. Preprints 2024, 2024030079. https://doi.org/10.20944/preprints202403.0079.v1
Goldstein, D.; Aldrich, C.; O'Connor, L. A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data. Preprints2024, 2024030079. https://doi.org/10.20944/preprints202403.0079.v1
APA Style
Goldstein, D., Aldrich, C., & O'Connor, L. (2024). A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data. Preprints. https://doi.org/10.20944/preprints202403.0079.v1
Chicago/Turabian Style
Goldstein, D., Chris Aldrich and Louisa O'Connor. 2024 "A Review of Orebody Knowledge Enhancement using Machine Learning on Open Pit Mine Measure-While-Drilling Data" Preprints. https://doi.org/10.20944/preprints202403.0079.v1
Abstract
Measure While Drilling (MWD) refers to a comprehensive array of sensors employed to gather performance data throughout the process of rock drilling in mining blast holes. Multiple MWD metrics are collected by researchers depending on the setup of MWD sensors. Trends in MWD data can be correlated with pre-excavation subsurface conditions. The low-cost, extensive MWD data acquisition resolution of blast holes compared to expensive and sparsely collected exploration drill holes make the former one a desirable method for characterizing pre-excavation subsurface conditions. Nevertheless, the application of MWD technology in open pit mining is constrained by a substantial amount of data that requiring manual analysis within a short time. To address this limitation, computerized data acquisition and interpretation of MWD variables has been used to broadly determine open pit mining lithological boundaries. The results of artificial intelligence algorithms, such as Neural Networks, and Gaussian Processes, to recognize subsurface conditions, such as rock type, from MWD data have consistently returned >90% prediction accuracy. However, methods of evaluating the importance of each MWD feature, such as Principal Component Analysis, has not been optimally used. This review focuses on the progression of intelligent MWD analysis using Machine Learning methods in open-pit mining, to determine the geotechnical, geological, and geochemical subsurface characteristics before open-pit mining excavation.
Keywords
Measure While Drilling (MWD); Open-pit mining; Subsurface characterization; Machine Learning (ML); Data acquisition; Rock properties; Geochemical analysis; Artificial Intelligence (AI); Predictive modeling; Feature Importance
Subject
Engineering, Mining and Mineral Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
