Key Takeaways

  • Shovel-face, automated, and blasthole/RC sampling are all being upgraded with sensor packages that reduce manual handling and speed up grade turnaround.
  • Motion Metrics ShovelMetrics and MineSense’s shovel sensor line use AI/computer vision to estimate fragmentation and grade at the dig face in real time.
  • Scott Automation’s Rocklabs line (AMS Prep, RoboPrep Elite) automates crushing, splitting, and pulverizing so cross-belt or lab autosamplers can feed inline XRF without manual prep.
  • IMDEX BLASTDOG specifically instruments blasthole/RC sampling, capturing sensor-while-drilling data as the hole is drilled.
  • Blasthole/RC sampling AI is the least mature of the three (Emerging) — most of the intelligence currently sits in the downstream XRF/assay step rather than the sampling mechanism itself.

TL;DR

Replace manual scoop-and-bag sampling with sensor-equipped shovels (grade/fragmentation AI), robotic sample-prep lines feeding inline XRF, and instrumented blasthole rigs like BLASTDOG — cutting the lag between digging ore and knowing what’s in it.

How Do I Automate Ore Sampling With AI?

Sampling sits at the front of every grade-control and geometallurgy pipeline, and it’s traditionally been the slowest link — a shovel operator digs, a sample gets bagged, and results come back hours or days later from the lab. The current generation of tools attacks each stage of that chain separately.

At the dig face, shovel-mounted sensor systems — Motion Metrics’ ShovelMetrics (now part of Weir) and MineSense’s shovel sensor products — use 3D cameras and computer vision to continually estimate fragmentation and flag oversized material directly from the bucket, without stopping the dig cycle. This is a genuinely real-time AI application: the model runs on every pass, not on a periodic sample.

For the sample-preparation step, Scott Automation’s Rocklabs line — products like the AMS Prep Line and RoboPrep Elite — robotically crush, split, dose, and pulverize samples with RFID tracking, feeding directly into cross-belt autosamplers with inline XRF analyzers. The “AI” contribution here is less about the mechanical automation and more about the XRF analyzers themselves increasingly using dynamic, matrix-aware calibration to hold accuracy across variable ore types without manual recalibration.

Blasthole and RC sampling has a more specific instrumented tool: IMDEX BLASTDOG, an eight-arm calliper and multi-sensor probe that rides down the blasthole on a semi-autonomous tracked platform, capturing physical hole measurements (diameter, voids, structure) as sensor-while-drilling data rather than relying purely on the recovered sample. This is the least mature of the three sampling methods in terms of AI maturity — standard RC rig cone splitters are still the default, and BLASTDOG-style instrumentation is a newer addition layered on top rather than a full replacement.

Across all three, the practical move is the same: wherever you can put a sensor closer to the point of extraction (shovel bucket, sample-prep line, drill string) instead of waiting for a physical sample to reach a lab, you shorten the grade-control decision loop.

Try It With Geocluster

Sensor-based sampling generates a lot of streaming, semi-structured data that needs to be reconciled against lab assay results to be trusted — exactly the kind of research and validation work Geocluster is designed for. If you’re evaluating or deploying sensor-based sampling, Geocluster can help you build the reconciliation pipeline behind it.