A Machine Learning Classification Approach to Geotechnical Characterization Using Measure-While-Drilling Data
Bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis on account of expensive resource development drilling and mapping. The Measure-While-Drilling (MWD) system uses sensors to collect the drilling data from open-pit blast hole dr...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
2025
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| Online Access: | http://hdl.handle.net/20.500.11937/97503 |
| _version_ | 1848766284730204160 |
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| author | Goldstein, D. Aldrich, Chris Shao, Q. O’Connor, L. |
| author_facet | Goldstein, D. Aldrich, Chris Shao, Q. O’Connor, L. |
| author_sort | Goldstein, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis on account of expensive resource development drilling and mapping. The Measure-While-Drilling (MWD) system uses sensors to collect the drilling data from open-pit blast hole drill rigs. Historically, the focus of MWD studies was on penetration rates to identify rock formations during drilling. This study explores the effectiveness of Artificial Intelligence (AI) classification models using MWD data to predict geotechnical categories, including stratigraphic unit, rock/soil strength, rock type, Geological Strength Index, and weathering properties. Feature importance algorithms, Minimum Redundancy Maximum Relevance and ReliefF, identified all MWD responses as influential, leading to their inclusion in Machine Learning (ML) models. ML algorithms tested included Decision Trees, Support Vector Machines (SVMs), Naive Bayes, Random Forests (RFs), K-Nearest Neighbors (KNNs), Linear Discriminant Analysis. KNN, SVMs, and RFs achieved up to 97% accuracy, outperforming other models. Prediction performance varied with class distribution, with balanced datasets showing wider accuracy ranges and skewed datasets achieving higher accuracies. The findings demonstrate a robust framework for applying AI to real-time orebody characterization, offering valuable insights for geotechnical engineers and geologists in improving orebody prediction and analysis |
| first_indexed | 2025-11-14T11:48:42Z |
| format | Journal Article |
| id | curtin-20.500.11937-97503 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:48:42Z |
| publishDate | 2025 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-975032025-04-16T03:43:51Z A Machine Learning Classification Approach to Geotechnical Characterization Using Measure-While-Drilling Data Goldstein, D. Aldrich, Chris Shao, Q. O’Connor, L. Bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis on account of expensive resource development drilling and mapping. The Measure-While-Drilling (MWD) system uses sensors to collect the drilling data from open-pit blast hole drill rigs. Historically, the focus of MWD studies was on penetration rates to identify rock formations during drilling. This study explores the effectiveness of Artificial Intelligence (AI) classification models using MWD data to predict geotechnical categories, including stratigraphic unit, rock/soil strength, rock type, Geological Strength Index, and weathering properties. Feature importance algorithms, Minimum Redundancy Maximum Relevance and ReliefF, identified all MWD responses as influential, leading to their inclusion in Machine Learning (ML) models. ML algorithms tested included Decision Trees, Support Vector Machines (SVMs), Naive Bayes, Random Forests (RFs), K-Nearest Neighbors (KNNs), Linear Discriminant Analysis. KNN, SVMs, and RFs achieved up to 97% accuracy, outperforming other models. Prediction performance varied with class distribution, with balanced datasets showing wider accuracy ranges and skewed datasets achieving higher accuracies. The findings demonstrate a robust framework for applying AI to real-time orebody characterization, offering valuable insights for geotechnical engineers and geologists in improving orebody prediction and analysis 2025 Journal Article http://hdl.handle.net/20.500.11937/97503 10.3390/geosciences15030093 unknown |
| spellingShingle | Goldstein, D. Aldrich, Chris Shao, Q. O’Connor, L. A Machine Learning Classification Approach to Geotechnical Characterization Using Measure-While-Drilling Data |
| title | A Machine Learning Classification Approach to Geotechnical Characterization Using Measure-While-Drilling Data |
| title_full | A Machine Learning Classification Approach to Geotechnical Characterization Using Measure-While-Drilling Data |
| title_fullStr | A Machine Learning Classification Approach to Geotechnical Characterization Using Measure-While-Drilling Data |
| title_full_unstemmed | A Machine Learning Classification Approach to Geotechnical Characterization Using Measure-While-Drilling Data |
| title_short | A Machine Learning Classification Approach to Geotechnical Characterization Using Measure-While-Drilling Data |
| title_sort | machine learning classification approach to geotechnical characterization using measure-while-drilling data |
| url | http://hdl.handle.net/20.500.11937/97503 |