Machine learning solutions

An open discussion webinar with our panel of experts

Enterprise mining solutions require us to create and integrate tools that target end-to-end technical processes spanning mining disciplines and stages.

Automated, real-time solutions which leverage machine learning and ‘internet of things’ concepts provide understanding around variances and exceptions as they occur. This then enables better and more timely decisions based on accurate, current operational information which in turn drives continual improvement.

Why Machine Learning

Discussion Forum

Host: Steve Sullivan
Panellists: Penny Stewart, Hugh Sanderson, Christie Myburgh

Join our expert panel as we explore the issues that arise around applying machine learning to mining applications.

We are all used to the idea that machines can do things that humans can’t - computers can do calculations quicker, robots don’t tire as easily, plotters don’t make silly mistakes and excavators are stronger than a pick and shovel.

We acknowledge the practical usefulness of machines but we want to retain control of the power to ‘think’. We learn from our successes, discard the false trails and slowly advance.

The good news is that machines can learn too. With machine learning, the paths that don’t work are discarded and the paths that work are reinforced very quickly, exponentially even. Our ‘thinking’ then comes into play and we can reset parameters before a new solution is presented for approval. The machine learns what we want, and does its thinking so that our thinking is more targeted.

This forum will be hosted by Steve Sullivan, Senior Technical Sales Specialist & Technical Lead at Maptek, joined by panellists Penny Stewart, Hugh Sanderson and Christie Myburgh.

Dr. Penny Stewart

Dr Penny Stewart

A mining engineer and founder of mining AI software company PETRA to extract value from mining data. PETRA is a world leading provider of machine learning and optimisation solutions to the global mining industry, including their flagship MAXTA™ digital twin value chain optimisation suite. In 2016, PETRA collaborated with Newcrest Mining to develop and deploy some of the mining industry's first machine learning algorithms.

Dr Hugh Sanderson

Dr Hugh Sanderson

Hugh has been writing software for over 30 years. He has extensive experience with the mining industry and with AI, data science and computer vision techniques. Now, as these disciplines converge, Hugh sees great opportunities in Australia and around the world to apply machine learning to the mining industry in very practical ways.

Christie Myburgh

Christie Myburgh

Christie has over 20 years of experience in mining and is the co-founder of tech start-up Atomorphis, which focuses on non-linear optimisation and complex systems analyses. He has been instrumental in the creation of optimisation/simulation solutions used across various industries such mining and aviation. Lately, Christie is focusing on solving large-scale practical problems by merging the fields of non-linear optimisation and complex systems analyses.

Maptek expertise

Maptek is now applying machine learning and augmented reality to accelerate tasks such as resource modelling, grade estimation, fragmentation analysis and production tracking. The Maptek Compute Framework enables faster, secure cloud processing and represents a radical improvement over intensive desktop processing and time-consuming data manipulation.

Maptek DomainMCF uses machine learning to generate domain boundaries directly from drillhole sample data for rapid creation of resource models.

Geologists feed in drilling data and obtain domain or grade models in dramatically less time than traditional resource modelling methods.

DomainMCF means projects can be modelled as often as you want. Results are available in minutes and comparable to classical techniques. Geologists remain in control of the process without the onerous preparation work. New data can be added and models regenerated quickly to reflect the current data.

Read more about DomainMCF

Industry partners

Machine learning outcomes are significantly improved when driven in collaboration with industry and technology partners. Maptek is committed to identifying technology partners to develop complementary technologies that enhance the value proposition for our customers. In this way we can share the vision for the future of mining and work more effectively to achieve better outcomes.

Integration between Maptek solutions and PETRA MAXTA targets operational improvement in areas such as ore recovery, blast optimisation and scheduling. The result is that planning is based on a better understanding of the real performance of downstream processes. MAXTA models of the mine performance can add value to the orebody knowledge base, enabling a mine to surpass targets every day.

Case Study: Digital twin models unlock mine value chain optimisation to improve performance


Join our expert panel to discuss Machine Learning and how it can benefit you