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