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The application of deep learning to extract
geological information from drill core imagery
Liam Webb, Mark Grujic and Dr. Duy Tin Truong
Question: Can we replicate every day geological tasks using
commonly acquired core photography and a neural network?
• Mask – R-CNN
• Pre-processing of imagery
• RQD
• Unsupervised Lithological Domaining
• How does it all fit?
Problem: Every core photo is different – anything we build
needs to be flexible and adaptable.
Solution: Mask R-CNN
1980 – CNN – Kunihiko Fukushima
2013 - R-CNN - Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra
Malik
2015 - Fast R-CNN - Ross Girshick
2015 - Faster R-CNN - Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
2017 - Mask R-CNN - Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross
Girshick
October 2019 - Detectron II - Facebook
https://www.pyimagesearch.com/2018/11/19/mask-r-cnn-with-opencv/
1.3
-0.8 2.2
0.2
-2.2 1.7
0.2 0.6
2048 per image
1.3
-0.8 2.2
0.2
-2.2 1.7
0.2 0.6
2048 per image
Input Image:
Box and Row cropping
Empty space
Empty space
Core Blocks
Optical Character Recognition
Optical Character Recognition
Depth Registration
RQD
179 – 182m
170-172m
1.3
-0.8 2.2
0.2
-2.2 1.7
0.2 0.6
Unsupervised Lithological
Classification
• Deep learning models can potentially be used to
replicate any visual task done manually by a geologist.
• If properly trained, the performance should be
equivalent or superior to a human, mostly due to an
increase in consistency and auditability.
• Models can be updated to reflect new geological
concepts and backwards propagated to historical data.
• These algorithms can be applied to imagery
immediately after acquisition and provide rapid
results, meaning that they could be integrated into
existing workflows without disruption.
• Results can be integrated with other data sources (lab
at rig XRF, hyperspectral scanning, laser profiling etc)
to build more complex products.
Implementation

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The future of mining and mining geology - advances in technology, AI and automation.

  • 1. The application of deep learning to extract geological information from drill core imagery Liam Webb, Mark Grujic and Dr. Duy Tin Truong
  • 2. Question: Can we replicate every day geological tasks using commonly acquired core photography and a neural network?
  • 3. • Mask – R-CNN • Pre-processing of imagery • RQD • Unsupervised Lithological Domaining • How does it all fit?
  • 4. Problem: Every core photo is different – anything we build needs to be flexible and adaptable.
  • 5. Solution: Mask R-CNN 1980 – CNN – Kunihiko Fukushima 2013 - R-CNN - Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik 2015 - Fast R-CNN - Ross Girshick 2015 - Faster R-CNN - Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun 2017 - Mask R-CNN - Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick October 2019 - Detectron II - Facebook https://www.pyimagesearch.com/2018/11/19/mask-r-cnn-with-opencv/
  • 6. 1.3 -0.8 2.2 0.2 -2.2 1.7 0.2 0.6 2048 per image 1.3 -0.8 2.2 0.2 -2.2 1.7 0.2 0.6 2048 per image
  • 8. Box and Row cropping
  • 12.
  • 16. RQD
  • 17.
  • 19. 1.3 -0.8 2.2 0.2 -2.2 1.7 0.2 0.6 Unsupervised Lithological Classification
  • 20.
  • 21.
  • 22.
  • 23. • Deep learning models can potentially be used to replicate any visual task done manually by a geologist. • If properly trained, the performance should be equivalent or superior to a human, mostly due to an increase in consistency and auditability. • Models can be updated to reflect new geological concepts and backwards propagated to historical data. • These algorithms can be applied to imagery immediately after acquisition and provide rapid results, meaning that they could be integrated into existing workflows without disruption. • Results can be integrated with other data sources (lab at rig XRF, hyperspectral scanning, laser profiling etc) to build more complex products. Implementation