5 ways an exploration prospectivity model can fool you.
Challenge leakage, spatial optimism, invented negatives, unfamiliar conditions and scores that hide weak evidence.
MindSync insights
Better questions.
Clearer decisions.
Practical thinking for evidence-led exploration. Five perspectives on preparing the evidence, using machine learning responsibly, challenging a model and deciding what comes next.
01 / Featured insight05
Methods, validation and limits
for exploration decisions.
Machine learning · 6 min read
Machine learning for mineral exploration: methods, validation and practical limits.
What machine learning can contribute to an exploration decision, and how to keep geology, validation and uncertainty in control.
Read the guide →Further thinking
Prepare · Interpret · DecideRead insight ↗
How to prepare exploration data for an independent evidence review.
Prepare a handover with traceable sources, coordinates, coverage, QA/QC and known gaps.
What a defensible target-generation study can and cannot establish.
Separate useful review priorities from probabilities, discoveries and drilling decisions.
How geologists can keep uncertainty visible in exploration decisions.
Carry observations, conflicting evidence and missing support into the next investigation.
From perspective to practice
Inspect the evidence
behind the thinking.
Explore the public study records, including the results, disagreements and limits. These insights are professional perspectives, not project-specific advice.