Key Takeaways

  • MineSense ShovelSense mounts high-speed XRF sensors on shovel/excavator buckets to scan material for grade in real time, bucket by bucket.
  • The system’s ML models are trained per ore body, so grade estimates adapt to your specific deposit’s mineralogy rather than using a generic calibration.
  • MineSense BeltSense is the conveyor-belt equivalent — same sensing concept applied downstream of the shovel.
  • Real-time grade data feeds directly into automated truck-diversion decisions, routing ore to mill, stockpile, or waste without waiting for lab assays.
  • This is a mature, commercially deployed technology (ai_relevant: Yes) — not experimental.

TL;DR

Fit shovel buckets with high-speed XRF sensors (MineSense ShovelSense) that scan every dig pass and feed ore-body-specific ML grade models directly into your dispatch system, so trucks get routed by measured grade instead of block-model estimate alone.

How Do I Monitor Ore Grade at the Shovel With AI?

The core problem this solves: your block model tells you the expected grade of a mining block before you dig it, but actual grade varies within that block, and by the time a lab assay comes back from a physical sample, the ore has often already been dug, hauled, and dumped. MineSense’s ShovelSense closes that gap by mounting a high-speed XRF sensor array directly on the shovel or excavator, scanning the bucket’s contents as it swings — every pass, not just a periodic sample.

The sensor readings alone aren’t grade — they’re raw elemental signal that needs to be translated into a grade estimate, and that’s where the ML layer comes in: MineSense trains its classification/regression models per ore body, using your site’s own assay and mineralogy data as ground truth, so the system learns your deposit’s specific relationship between XRF signal and true grade rather than applying a generic factory calibration. That per-site training step matters — a model trained on one deposit’s gangue mineralogy won’t transfer cleanly to a geochemically different one.

BeltSense is the same underlying technology applied further downstream, on the conveyor belt rather than the shovel bucket — giving you a second real-time grade checkpoint after material has already been loaded and hauled, which is useful for catching misrouted material or validating the shovel-stage estimate. Used together, ShovelSense and BeltSense give you grade visibility at two points in the material flow, sequentially confirming and refining the routing decision.

The practical payoff is automated truck diversion: instead of a dispatcher routing trucks by block-model estimate and hoping, the real-time grade reading feeds directly into the fleet dispatch system, sending each truckload to mill, low-grade stockpile, or waste based on what was actually measured. This is one of the more mature AI deployments in this entire poster — it’s commercially deployed at multiple operating mines, not a research prototype.

Try It With Geocluster

Turning a stream of shovel-mounted sensor readings into a trustworthy, deposit-specific grade model requires careful reconciliation against your assay database — exactly the kind of applied research Geocluster is built to support. If you’re evaluating real-time grade sensing for your operation, Geocluster can help you validate the model against your own data before you trust it in production.