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A Machine Learning Approach for Material Type Logging and Chemical Assaying from Autonomous Measure-While-Drilling (MWD) Data

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Abstract

Understanding the structure and mineralogical composition of a region is an essential step in mining, both during exploration (before mining) and in the mining process. During exploration, sparse but high-quality data are gathered to assess the overall orebody. During the mining process, boundary positions and material properties are refined as the mine progresses. This refinement is facilitated through drilling, material logging, and chemical assaying. Material type logging suffers from a high degree of variability due to factors such as the diversity in mineralization and geology, the subjective nature of human measurement even by experts, and human error in manually recording results. While laboratory-based chemical assaying is much more precise, it is time-consuming and costly and does not always capture or correlate boundary positions between all material types. This leads to significant challenges and financial implications for the industry, as the accuracy of production blasthole logging and assaying processes is essential for resource evaluation, planning, and execution of mine plans. To overcome these challenges, this work reports on a pilot study to automate the process of material logging and chemical assaying. A machine learning approach has been trained on features extracted from measurement-while-drilling (MWD) data, logged from autonomous drilling systems (ADS). MWD data facilitate the construction of profiles of physical drilling parameters as a function of hole depth. A hypothesis is formed to link these drilling parameters to the underlying mineral composition. The results of the pilot study discussed in this paper demonstrate the feasibility of this process, with correlation coefficients of up to 0.92 for chemical assays and 93% accuracy for material detection, depending on the material or assay type and their generalization across the different spatial regions. The results achieved are significant, showing opportunities to guide further drilling processes, provide chemistry data with downhole resolution, and continuously update mine plans as the mine progresses.

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Notes

  1. https://en.wikipedia.org/wiki/Pilbara.

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Acknowledgements

This work has been supported by the Australian Centre for Field Robotics and the Rio Tinto Centre for Mine Automation. The authors would also like to acknowledge the support of Anna Chlingaryan and Katherine Silversides in the manuscript review and editing process.

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Authors and Affiliations

  1. Rio Tinto Centre for Mine Automation, Australian Centre for Field Robotics, The University of Sydney, 8 Little Queen street, Chippendale, NSW, 2008, Australia

    Rami N. Khushaba, Arman Melkumyan & Andrew J. Hill

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  1. Rami N. Khushaba
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  2. Arman Melkumyan
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  3. Andrew J. Hill
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Correspondence to Rami N. Khushaba.

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Khushaba, R.N., Melkumyan, A. & Hill, A.J. A Machine Learning Approach for Material Type Logging and Chemical Assaying from Autonomous Measure-While-Drilling (MWD) Data. Math Geosci 54, 285–315 (2022). https://doi.org/10.1007/s11004-021-09970-w

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