Geological modelling tools, including Maptek GeologyCore and AI-assisted Maptek DomainMCF, were evaluated for up to four weeks, supported with documentation and technical assistance from Maptek’s global team.

First prize in the 2025 Maptek Geology Challenge was awarded to Dr. Jen Ellis, Superintendent of Mine Geology & Resource for Rio Tinto Kennecott, whose project showed that the machine learning capabilities of DomainMCF, combined with implicit modelling, can deliver robust decision-making for complex deposits.

Dr. Jen’s work focused on the Bingham Canyon deposit – one of the world’s largest and longest-lived open-pit mines. With more than 120 years of mining history, Bingham Canyon has produced a uniquely comprehensive geological dataset, including lithological, geochemical and structural data from decades of drilling and mapping, making it an ideal testing ground for advanced modelling techniques.

This project drew on an immense volume of information, incorporating over 6,000 drill holes and more than one million blast hole samples. Using DomainMCF, Jen demonstrated how machine learning can outperform traditional implicit methods for large datasets. The software’s ability to test multiple scenarios and identify lower-confidence areas revealed insights that would not be suited to a manual process at this scale.

Second prize in the 2025 Maptek Geology Challenge was awarded to Bahati Moshally, a master’s student at Clausthal University of Technology. His thesis focuses on characterising the primary dispersion halo of a transitional porphyry–epithermal gold deposit through spatial modelling of lithology, alteration, and the distribution of gold and associated hydrothermal elements. He entered the challenge to explore how a machine learning–based modelling approach would perform when applied to lithological and alteration domains, and to assess how these results compared with traditional geological interpretation and implicit modelling workflows.

The findings were that GeologyCore’s implicit modelling tools offered excellent control over object shapes, allowing alignment of the model with geological interpretation. It was Moshally’s view that Maptek offers “one of the most robust and modern approaches to geological modelling in the industry.”

Particularly impressive about this entry was that Moshally was using DomainMCF and GeologyCore software for the first time. This is not only testament to the participant’s skills and dedication but also speaks volumes as to the accessibility of the software tools on offer from Maptek.

Third place Josh Maurer of Carmeuse North America applied GeologyCore and DomainMCF on two distinct projects, concluding that even when provided with limited drilling information, DomainMCF was able to produce a realistic block model. “You can’t shy away from technology. If you do, you’re missing opportunities. Geology evolves, and you have to adapt. The more tools you have in your toolbox, the more efficient and accurate your workflows will be,” suggested Maurer.

The Maptek Geology Challenge is about evaluating new directions for geological modelling, using exciting, innovative technology, to solve the modelling problems presented today.

Global Customer Success Manager Henry Dillon highlighted the high calibre of entries in this year’s competition, “It has been another year of inspiring submissions to the Maptek Geology Challenge. It was especially exciting in this year’s challenge to see how people combined machine learning, implicit modelling and practical geology to improve decision-making on real deposits.”

Congratulations to Dr. Jen F. Ellis, Bahati Moshally and Joshua Maurer on their standout entries to the 2025 Maptek Geology Challenge. Maptek extends our thanks to all participants for their time, effort, and willingness to share ideas on geological techniques. It is through this collaboration and exchanging of knowledge that we strive to be Smarter Together.