AI for drilling and blasting turns a steady stream of field data into decisions made during the shift, not in next week’s report. Drill rigs log every hole, probes measure the rock, cameras size the broken muck and sensors watch the water and walls around the pit. This category covers those operations and the monitoring programs that keep the pit safe to work in.
What AI Does in Drilling and Blasting
AI in this group does three jobs. It automates machines, such as drill rigs that position and drill each hole without an operator in the cab. It measures the rock and the result, from blast-hole logs to fragmentation and ore grade at the shovel. Finally, it watches groundwater, slopes, tailings and water quality, flagging abnormal trends before they become incidents.
Where to Start
Each page below answers one “how do I do this with AI?” question.
- Drill and log the bench. Start with autonomous drilling for precise holes, then log them with the BLASTDOG probe.
- Measure the blast and the ore. Use fragmentation analysis to size the broken rock, and shovel grade sensing to route each truck by measured grade.
- Watch the pit walls. Set up slope stability monitoring with in-ground sensors and a well-planned hydrogeologic monitoring network.
- Model the water. Build your groundwater model with scripted hydrological modeling tools.
- Keep the model honest. Speed up hydrological model calibration with PEST++ and ML surrogates.
- Protect water and tailings. Run tailings management on one data platform, and add AI to water quality monitoring.
What’s Mature and What’s Emerging
BLASTDOG, image-based fragmentation analysis and shovel grade sensing are commercially deployed at multiple mines. Wireless sensor platforms for slope and groundwater monitoring are proven too. By contrast, full-fleet autonomous drilling is still expanding. ML-assisted calibration, hydrogeophysical data fusion and AI for water quality remain emerging, so test them on a pilot first.