Stope Optimisation and AI-Assisted Workflows for Stochastic Analysis: A Case Study

Traditional stope optimisation approaches provide valuable guidance for mine planning, but they often rely on a limited number of deterministic scenarios. As mining professionals seek greater confidence in strategic planning decisions, new approaches are emerging that combine optimisation frameworks with AI-assisted workflows to better understand uncertainty and risk.

About this event

In this webinar, Micromine (an AusIMM Industry Partner) will present a practical framework for incorporating stope optimisation and AI-assisted workflows into stochastic analysis of underground metalliferous deposits. Using a de-identified underground mining case study, the presenters will demonstrate how probabilistic assessment methods can support more informed decision-making and improve the efficiency of evaluating mining scenarios.

What you'll learn

Participants will gain insights into:

• Stope optimisation methodologies and frameworks
• Accelerating the creation of stope arrangements
• Using AI-assisted workflows to identify risk-aware operating ranges
• Applying probabilistic assessment techniques to planning decisions
• Evaluating uncertainty using Monte Carlo analysis

Speaker/s

Stefano Guiulfo

Technical Product Manager
Micromine

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A mining engineer with over 15 years of experience across underground operations, consulting, and digital mining solutions. Today, I lead a global business unit focused on bringing innovative mining technology to market, aligning product strategy with customer needs, and driving commercial growth.

Maurice Nyilimana

Principal Mining Consultant
Micromine

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Mining Engineer with over 14 years of industry experience in effective Mine Planning, Stope Design, Drill & Blast, Scheduling, Ring design, administration, training, project management, Risk Management, Mine Emergency Response and Underground Supervision. Has completed a Bachelor of Mining Engineering from the University of Adelaide.