AI for mine planning replaces slow manual iteration with search and prediction. Planning tools now test many schedules in parallel, and blast design platforms predict fragmentation before a hole is drilled. This category covers the planning side, from block model to production forecast, and the blasting side, from design to post-blast evaluation.
What AI Does in Mine Planning
Mine planning links decisions made years ahead to results measured every shift. AI helps in three ways. It models the deposit and the bench, from block attributes to drone-built digital twins. It optimizes schedules and blast patterns by exploring far more options than a planner can by hand. Finally, it learns from outcomes, feeding fragmentation, movement and production results back into the next plan.
Where to Start
Each page below answers one “how do I do this with AI?” question.
- Build the foundation. Start with the mining block model, the shared input for every planning decision.
- Plan the life of mine. Long-term mine planning with Maptek Evolution uses evolutionary search across many scheduling scenarios.
- Design the blast. Blast design optimization turns drone surveys and past blasts into better patterns.
- Time the blast. Blast sequencing digitizes delay timing in platforms like Orica BlastIQ and Deswik.Blast.
- Check the result. Blasting evaluation tracks ore movement and measures fragmentation from muckpile photos.
- Forecast production. Mine production forecasting starts by consolidating reports into one web platform.
What’s Mature and What’s Emerging
Evolutionary long-term scheduling is a shipped production capability. Blasting evaluation, with blast movement tracking and image-based fragmentation analysis, has years of field deployment. AI blast design platforms such as Strayos are commercially available. By contrast, AI optimization of blast sequencing is still emerging. The AI modules on top of block models, and predictive production forecasting, are also still maturing. Evaluate those on evidence rather than marketing.