Video

Improving Data Quality for Gold Miners with PhotonAssay™

Can poor sampling decisions cost you your project? In this critical discussion, Dr Simon Dominy, Dr Naomi Potter, and Dr James Tickner examine how data quality – especially assay precision and sampling strategy – directly impacts resource modelling, mine reconciliation, and financial outcomes. Drawing from decades of real-world studies and operations, this video explores how PhotonAssay™ can reduce the nugget effect, improve reconciliation, and deliver confidence in grade control and reserve models.

What you’ll learn

  • How assay misclassification can lead to ore/waste reversal and NPV erosion

  • Why most errors originate in sampling, not analysis

  • The role of heterogeneity studies, QC protocols, and duplicate analysis

  • How PhotonAssay™ handles large sample masses to reduce uncertainty

  • Real-world cases where PhotonAssay™ improved mine reconciliation outcomes

  • Why monthly QC reviews and mass-based measurement matter more than assumed

Chapters

  • 0:37 Where does assay error come from?
  • 1:46 What is the cost of bad data for mining operations?
  • 3:41 Where do sampling breakdowns happen?
  • 4:37 Should I crush or pulverise my samples?
  • 6:07 Am I mining the wrong rock?
  • 7:10 How does PhotonAssay™ support improved reconciliation?

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