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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 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

Ketersediaan
#
Perpustakaan BIG (Eksternal Harddisk) 550
415
Tersedia
Informasi Detail
Judul Seri
Geosciences
No. Panggil
550
Penerbit
Switzerland : MPDI.,
Deskripsi Fisik
17 hlm PDF, 1.559 KB
Bahasa
Inggris
ISBN/ISSN
2076-3263
Klasifikasi
550
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
online resource
Edisi
Vol.15, Issue 3, March 2025
Subjek
Measure-While-Drilling (MWD)
geotechnical engineering properties
rock mass prediction
feature importance
Geological Strength Index
Info Detail Spesifik
Geosciences
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • 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 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
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