In this episode of Fresh Thinking, Ian Glacken and Dr Gregory Zhang explore why mineral resource models and grade control models often fail to reconcile and how convolutional neural networks (CNNs) can help bridge that gap. This is Part 1 of a 2 part series.

The discussion focuses on early-stage resource estimation, sparse drilling data, and how machine learning can learn from dense grade control information to improve confidence, reduce bias, and support better decision-making. Importantly, this approach is positioned as a supplement to sound geology and geostatistics, not a replacement.

Speakers Ian Glacken – Executive Consultant Geology - Snowden Optiro Dr Gregory Zhang – Senior Consultant Geology - Snowden Optiro

Key moments in the episode
00:22 Why resource models and grade control often don't match
02:55 Systematic bias and the limits of traditional reconciliation
04:22 Using CNNs to learn relationships between sparse and dense data
06:02 Treating resource models as 3D images for machine learning
07:18 What CNNs can actually predict in a mining context
10:07 Using grade control areas as calibration zones
12:43 Why AI should supplement, not replace, good geology

This is Part 1 of a two-part conversation on applying machine learning responsibly in mineral resource estimation.

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If you would like to contact Ian or Gregory: contact@snowdenoptiro.com

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Snowden Optiro:
Snowden Optiro is a resources consulting and advisory group that provides independent advice, consulting and training to mining and exploration companies, their advisors and investors. We help mine developers to advance their projects, mining companies to improve their operations and their professionals, and investors to de-risk their investments by the provision of quality advice, training and software in the field of Mineral Resources and Mineral/Ore Reserves.

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