Fresh Thinking by Snowden Optiro

In this episode of Fresh Thinking, Susan Havlin (Managing Consultant APAC) speaks with Dr Gregory Zhang (Senior Consultant APAC) about using Excel as a transparent learning environment for geostatistics.
Rather than relying on black-box software, they explore how building the logic step by step in a spreadsheet can strengthen intuition, improve data discipline and deepen technical confidence.
Whether you are a geologist, resource modeller or mining professional wanting to better understand what sits behind the software, this episode breaks down the thinking that underpins robust estimation.
🔎 In this episode:
00:01 – Why the Excel World Championship is more relevant to geologists than you might think 02:02 – Why Excel is a powerful tool for learning geostatistics
06:27 – Building variograms from scratch: pairs, distances and continuity
09:24 – Understanding kriging as a system of equations
12:03 – Simple validation routines to keep yourself honest
13:02 – Can Excel help you understand machine learning?
If you want to move beyond "clicking the kriging button" and truly understand the mechanics behind resource estimation, this conversation is for you.
If you would like to contact Susan and Greg: contact(at)snowdenoptiro.com
🎧 Fresh Thinking by Snowden Optiro is available as an audio podcast on all major podcast platforms, and in video format on Snowden Optiro's YouTube channel.
If you need further assistance in using Excel, we have 2 courses that might interest you:
1-day Essential Excel Skills for Geologists and our 1-day Advanced Excel Skills for Kriging & Simulation. Both courses are presented by Dr Gregory Zhang.
Link to training courses: https://snowdenoptiro.com/professional-development/
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.
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, Ian Glacken (Executive Consultant) and Dr Gregory Zhang (Senior Consultant) discuss one of the most important topics in modern resource modelling: uncertainty.
From the long-standing debate of simulation vs estimation, to the growing focus on geological uncertainty and hierarchical truncated plurigaussian simulation, this episode explores where the mining industry stands — and where it still needs to improve.
⏱ Key Topics
00:00 – Introduction: Simulation in mining and why uncertainty matters
01:18 – Simulation vs estimation: what's the real difference?
02:16 – The "space of uncertainty": grade, geology, parameters and planning
03:34 – Are we misleading stakeholders by ignoring uncertainty?
05:34 – Has grade uncertainty been solved? Two-point vs multipoint simulation
06:39 – Data uncertainty: blast holes, nugget effect and sampling quality
07:30 – Parameter uncertainty and spatial bootstrapping
09:43 – Geological uncertainty: why it's fundamentally different
10:44 – Plurigaussian and hierarchical truncated plurigaussian simulation (HTPGS)
12:51 – Industry adoption and emerging trends
13:08 – The "holy grail" of a comprehensive uncertainty model
14:47 – Digital twins and the future of integrated uncertainty modelling
👍If you enjoyed this discussion, please Subscribe to Fresh Thinking wherever you listen to podcasts, for more mining-focused technical discussions across the mine value chain.
If you would like to contact Ian Glacken and Dr Gregory Zhang: [email protected]
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. It is also available in video format on Snowden Optiro's YouTube channel: https://youtu.be/-DnskmSDnn4
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/
#Mining #ResourceModelling #Geostatistics #Simulation #Geology #MiningEngineering

In this episode of Fresh Thinking by Snowden Optiro, Susan Havlin (Managing Consultant) speaks with Dr Tarrant Elkington (Snowden Optiro General Manager) about extending generative AI into coding practical engineering solutions.
They discuss how mining engineers and scientists can use AI tools to prototype bespoke solutions, automate niche problems, and build simple applications without being expert programmers.
If you've ever thought about using AI, but didn't know where to start, this conversation is for you.
Key moments:
0:36 – Why generative AI and coding is relevant for mining professionals
2:41 – What "AI coding" actually involves and how it works
4:22 – Real mining examples: road design optimisation and waste dump planning
6:46 – Does it really work first time? Debugging and validation
8:24 – What you learn through the process
9:37 – When it won't work and why starting small matters
11:06 – Practical advice for engineers and scientists wanting to begin
Generative AI is changing how we work. The question is not whether to use it, but how to use it well. Listen now and see how you could start building your own tools.
If you enjoyed this episode, please Subscribe for more mining-focused technical discussions across the mine value chain.
If you would like to contact Tarrant Elkington or Susan Havlin: [email protected]
Listen on the go: Fresh Thinking by Snowden Optiro is rapidly becoming the best mining podcast globally, and is available on most audio podcast platforms and also as a video podcast on Snowden Optiro's YouTube channel: https://youtu.be/v3dQDfaU-vg
👍 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/

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.
👍 If you enjoyed this episode, please Subscribe for more mining-focused technical discussions across the mine value chain, from out global consulting team.
If you would like to contact Jamie or Gregory: [email protected]
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 including Snowden Optiro's YouTube channel. Link to video podcast: https://youtu.be/z5Gz1lYQ0Jg
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, 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: [email protected]
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/