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.

If you enjoyed this episode, please Subscribe for more mining-focused technical discussions across the mine value chain.

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

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Fresh Thinking by Snowden Optiro is rapidly becoming the best mining podcast globally, and is available on all major podcast platforms.

👍 Like, comment, and subscribe for more technical mining insights from our global consulting team.

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.

Explore more: https://snowdenoptiro.com/

In this episode of Fresh Thinking, Senior Consultant Jamie Oppelaar is joined by Dr Gregory Zhang, Senior Consultant at Snowden Optiro, to explore what convolution really means in a geostatistical and mine planning context.

Jamie and Greg walk through how it already shows up in everyday resource estimation workflows. From moving averages and detrending through to kriging residuals and convolutional neural networks, this episode focuses on practical application, not theory for theory's sake.

This is Part 1 of a two-part discussion, laying the foundations for how convolution links classical geostatistics with modern machine learning approaches.

Key topics and timestamps
00:01 – Introduction to convolution - geostatistics and machine learning
00:21 – Separating geological trend from local variability in grade data
02:26 – Detrending, residuals, and the link to universal kriging
03:15 – Moving averages as a defensible trend model using convolution
03:41 – What convolution actually is, explained practically
05:08 – Choosing window sizes using variograms and cross-validation
05:57 – Stationarity, variograms, and kriging residuals
07:19 – How convolution relates to convolutional neural networks (CNNs)
09:22 – A step-by-step workflow mine geologists can apply with existing data
13:19 – Summary of key ideas and what's coming in Part 2

If you enjoyed this episode, please Subscribe for more mining-focused technical discussions across the mine value chain.

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

Listen on the go: Fresh Thinking by Snowden Optiro is rapidly becoming the best mining podcast globally, and is available on all major podcast platforms.

👍 Like, comment, and subscribe for more technical mining insights from our global consulting team.

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.

Explore more: https://snowdenoptiro.com/