Fresh Thinking by Snowden Optiro

In this episode of Fresh Thinking by Snowden Optiro, Ian Glacken and Susan Havlin explore localised conditional simulation (LCS) and its role in modern resource estimation workflows.
Building on earlier discussions around estimation methods, recoverable resources, and conditional simulation, this episode brings everything together to explain how LCS converts multiple simulation realisations into a single, practical block model—while preserving realistic grade variability. 
Key topics include: 
What localized conditional simulation is and why it matters
How localization transforms multiple realizations into a single model
The role of the E-type estimate in ranking and distribution
Differences between LCS and traditional ordinary kriging
How LCS supports recoverable resource estimation at the SMU scale
The origins of localization and its relationship to methods like uniform conditioning and MIK

In this episode of Fresh Thinking by ‪@SnowdenOptiro‬, Susan Havlin and Ian Glacken explore the fundamentals of conditional simulation and its role in modelling grade variability and uncertainty in mineral resource estimation.
They discuss how simulation differs from traditional methods like kriging and uniform conditioning, why capturing uncertainty is critical for modern mining decisions, and how multiple realizations can provide valuable insight into upside and downside risk.
 The conversation also touches on practical challenges, including reporting to the market and integrating uncertainty into mine planning workflows.

In this episode of Fresh Thinking by @SnowdenOptiro, Susan Havlin speaks with Tarrant Elkington about the mine planning implications of simulation.
The conversation explores:
Why resource models are fundamental to mine planning and project evaluation
The impact of grade variability and smoothing on mine planning outcomes
How LCS compares with conventional estimation methods such as Ordinary Kriging
The operational implications of dilution, ore loss and mining selectivity
How simulation-based approaches can support risk-aware and stochastic mine planning
Why LCS can integrate into existing mine planning workflows
This episode is the sixth in our seven-part series exploring recoverable resource estimation, simulation and uncertainty in mineral resource modelling.

In this episode of Fresh Thinking by ‪@SnowdenOptiro‬, Susan Havlin and Ian Glacken discuss uncertainty in mineral resource estimation and how Localised Conditional Simulation (LCS) provides a more practical framework for analysing geological risk and variability. The conversation explores:
Conditional simulation and uncertainty
Resource classification
Simulation versus kriging
Risk-qualified mine planning
Why preserving variability matters
Listen now to learn how modern geostatistical approaches are shaping the future of resource estimation.

In this episode of Fresh Thinking by @SnowdenOptiro , we explore recoverable resource methods and how they help bridge the gap between resource models and what is actually mined.
We discuss key approaches used across the industry, including Uniform Conditioning (UC) and Multiple Indicator Kriging (MIK), outlining how they address grade smoothing and improve predictions at the mining scale. We also cover the strengths and limitations of these methods, particularly their reliance on variograms and their inability to capture uncertainty.
This episode sets the stage for the next discussion on conditional simulation, where uncertainty and variability can be modeled more effectively.
Topics covered:
What are recoverable resources?
Why traditional estimation methods fall short
Uniform Conditioning (UC) explained
Multiple Indicator Kriging (MIK) explained
Strengths and limitations of recoverable resource methods Subscribe for more insights on geostatistics, resource estimation, and mining best practices.
Subscribe to Fresh Thinking by Snowden Optiro for more insights on geostatistics, resource estimation, and mining best practices.