Key Takeaways

  • Strayos uses drone photogrammetry and ML/genetic-algorithm optimization trained on historical blast outcomes to recommend burden, spacing, and timing before you drill.
  • O-Pitblast and Deswik.Blast are established commercial alternatives with strong simulation support for the same design step.
  • Orica BlastIQ is the cloud platform connecting drill data through to blast execution, standardizing how design intent gets carried into the field.
  • Explosive loading itself (physically charging the holes) has the least AI penetration of the group — current systems mostly add QA/QC checks rather than optimization.
  • Fragmentation and vibration prediction from these tools is trained on your own historical blast data, so accuracy improves the more blasts you feed back into the system.

TL;DR

Feed drone-captured bench topography and your historical blast performance data into an AI blast-design platform (Strayos, O-Pitblast, or Deswik.Blast) to get an optimized pattern before drilling, then execute and track it through a connected platform like Orica BlastIQ.

How Do I Optimize Blast Design and Execution With AI?

Blast design has traditionally leaned on empirical formulas and an experienced engineer’s judgment — burden, spacing, and powder factor set from rules of thumb, then adjusted after the fact based on how fragmentation and vibration actually turned out. The AI-native platforms invert that: Strayos builds a 3D digital twin of the bench from drone photogrammetry and smart-drill data, then applies machine learning and genetic-algorithm optimization — trained on your site’s historical blast outcomes — to recommend burden/spacing, differential energy loading, and timing design that predicts fragmentation, material movement, and vibration/airblast before you drill a single hole.

O-Pitblast (via its O-Pitsurface product) and Deswik.Blast cover similar ground with strong simulation and vibration-prediction capability, and represent the more established commercial end of blast-design software — less “trained on your data” AI-native optimization, more configurable simulation with decades of empirical models behind it. Which you pick often comes down to what’s already integrated with your mine-planning stack, since blast design doesn’t happen in isolation from bench sequencing and short-term planning.

Once a design is finalized, execution runs through a connected platform — Orica BlastIQ is the dominant cloud-based option, storing and sharing blast-related data across the drill-to-blast workflow so the field crew is working from the same design the engineer optimized, and post-blast results (fragmentation, vibration monitoring) flow back in to improve the next design cycle. The physical act of loading explosive into the hole is where AI penetration drops off — current systems (Orica’s electronic detonator/WebGen line, for instance) add QA/QC checks and logging rather than optimization; there’s not much to “optimize” once the design is set and the crew is executing it.

The honest caveat across all of this: these tools are only as good as the historical blast data you feed them. A site with a few years of consistent fragmentation and vibration monitoring will get real value from the ML recommendations; a site starting from scratch is initially getting a well-built simulation tool more than a trained optimizer.

Try It With Geocluster

Connecting drone survey data, drill logs, and historical blast performance into a coherent dataset that an optimization tool can actually learn from is a real research problem before it’s a blast-design problem — the kind of work Geocluster is built to support. If you’re building out a blast-optimization feedback loop, Geocluster can help you assemble the historical dataset behind it.