Ep 143 Convolution Without the Maths: a practical guide for Mine Geologists

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

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If you would like to contact Gregory or Jaimie: 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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