Ep 146: How Machine Learning Can Close the Gap Between Grade Control and Resources. Part 2

Ian Glacken (Executive Consultant geology) and Dr Gregory Zhang (Senior Consultant Geology) explore how machine learning and convolutional neural networks (CNNs) can be used to bridge the gap between grade control data and resource estimation, and why treating resource models as static can hold operations back.

Key discussion points:

⏱ 00:00 Introduction and context for using CNNs with grade control data
⏱ 00:55 Why machine learning must be an ongoing, iterative process
⏱ 03:18 Handling multivariate data and complex geological relationships
⏱ 04:00 Practical considerations: data quality, alignment, and validation
⏱ 06:02 Trust, interpretability, and keeping geologists in the loop
⏱ 07:19 Using lithology and categorical data in CNN models
⏱ 08:58 How an operation can get started with these techniques
⏱ 11:58 Final thoughts on machine learning as a decision-support tool

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