Gregory Zhang (Senior Geology Consultant) to talk about what convolution actually means in a geostatistical and geological context, and how it shows up in tools ranging from moving averages to convolutional neural networks (CNNs).

Building on part one of the conversation, they focus on practical use, common pitfalls, and how to combine modern machine learning approaches with classic geostatistics without losing geological control.

Key moments
00:02 – What convolution means in day-to-day geology and resource work
00:51 – Moving averages, detrending and kriging in practice
01:30 – Using external data like magnetics and structure with kriging
02:09 – Common pitfalls: edge effects, over-smoothing and stationarity
05:14 – Letting geological structure guide smoothing
05:17 – A worked example using folded geology and copper grades
06:11 – Where CNNs fit, and how they compare to traditional geostatistics
07:28 – Tools and software for convolution, kriging and CNN workflows
09:05 – When not to use moving averages or CNNs
10:39 – Practical sanity checks after detrending
11:05 – A clear framework for combining geostatistics and machine learning If you work in resource estimation, geostatistics, or applied geology, this episode is packed with practical insights you can take straight back to your own workflows.

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