Blast Design Optimization and Safety Copilot

Optimizes underground drill-and-blast patterns using design and muckpile image analysis to improve fragmentation, reduce oversize and fines, and provides blast safety prediction with clearance-zone alerting for safer execution.

The Problem

“Blast Design Optimization and Safety Copilot for Underground Mining”

Organizations face these key challenges:

1

Persistent fragmentation variability in deep-level longhole stoping

2

Oversized rocks causing grizzly blockages and production interruptions

3

Excessive fines reducing downstream efficiency

4

Blast design tuning depends heavily on individual engineer experience

Impact When Solved

Reduce oversize events that block 300 mm x 300 mm grizzliesLower fines generation that harms downstream recovery and handling efficiencyImprove fragmentation consistency across stopes, shifts, and crewsShorten blast design review and optimization cycle time

The Shift

Before AI~85% Manual

Human Does

  • •Review blast designs and past blast results to adjust burden, spacing, charging, and timing
  • •Inspect muckpiles visually and judge oversize and fines issues after each blast
  • •Coordinate pre-blast safety checks, radio confirmations, and exclusion-zone clearance
  • •Decide blast readiness and authorize firing based on SOPs and crew input

Automation

    With AI~75% Automated

    Human Does

    • •Approve or reject recommended blast pattern changes for each ring or stope
    • •Investigate exceptions, conflicting signals, or unusual geology before finalizing plans
    • •Confirm final blast readiness, handle escalations, and authorize firing

    AI Handles

    • •Analyze blast designs, operating history, geology, and muckpile images to estimate fragmentation outcomes
    • •Recommend burden, spacing, charge, and timing adjustments to reduce oversize and fines
    • •Monitor pre-blast conditions and score blast safety risk against readiness thresholds
    • •Issue clearance-zone alerts and explain go or no-go status in simple operational language

    Operating Intelligence

    How it works

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence91%
    ArchetypeRecommend & Decide
    Shape6-step converge
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

    Real-World Use Cases

    Underground drill-and-blast pattern optimisation with Aegis and muckpile image analysis

    The mine used software to study photos of blasted rock and simulate blast-hole layouts so it could change the drilling pattern and produce rock pieces that are not too big and not too fine.

    Physics-informed diagnostic optimisation using image-based measurement plus simulation of blast energy overlap.pilot/case-study level with a specific recommended drill pattern change, not broad autonomous deployment.
    10.0

    Blast safety prediction and clearance-zone alerting

    The system predicts whether a blast may violate safety limits and warns operators visually on maps or drone images before the blast happens.

    Predictive risk scoring and alertingdeployed capability highlighted as a platform benefit in the case study.
    9.5

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