Tag Archives: DomainMCF

Multivariable modelling study wins Maptek Geology Challenge 2024

First prize in this year’s Maptek Geology Challenge has been awarded to Miguel Aliaga Oblitas, who showed how Maptek DomainMCF improved model consistency and reduced processing time in advanced multivariable geological modelling.

Senior Geomodeller with Newmont Mining Corporation, Oblitas receives a personal prize of $500 and a six-month DomainMCF subscription for his company.

Entries displayed a range of modelling techniques and applications, and judging was tight, resulting in a tie for second place awarded to Ed Lynch, Superintendent Exploration Geology, SIMEC, and Danielle Karbishev, Senior Resource Estimation Geologist, Fortescue.

Oblitas highlighted the chance to work with real data from an active mine and apply innovative methodologies to enhance geological modelling. His report defined the challenges of accurately predicting vein behaviour at depth, especially with limited data, and noted significant improvements in model accuracy and efficiency.

“The ability to integrate various geological inputs – such as lithology, structural data, mineralogical information and vein intensity – was invaluable,” Oblitas said. “The reduction in processing time from weeks to just a few hours greatly facilitated model updates and more timely decision making in exploration and resource estimation.”

Oblitas noted that the flexibility of the Domain Manager in GeologyCore, which enabled the creation of custom rules and rapid testing of different structural scenarios, was key to overcoming the deposit complexity.

“The integration of multivariable inputs, including lithological, vein intensity and mineralogical data, allowed for a more accurate and detailed representation of the deposit’s structural framework – something that was challenging to achieve with traditional methods,” Oblitas commented.

Danielle Karbichev jumped at the opportunity to trial GeologyCore and DomainMCF to increase efficiency for the Fortescue Resource Modelling and Estimation team and assess the potential applications of machine learning.

The biggest surprise for Karbishev was the ability of DomainMCF to rapidly generate grade estimations comparable to those produced via established estimation methods such as kriging, Maptek says.

“Geological volume model outputs and grade predictions drastically improved with more detailed geological input data, however purely data-driven models can also be used to identify trends and structures prior to interpretation and domaining,” Karbishev said. “More testing is required but it is clear that machine learning could revolutionise resource modelling and estimation as technology advances!”

In terms of GeologyCore, Karbishev found that the 3D drill hole visualisation options were particularly useful for validation of drill hole coding against modelled surfaces, as well as for stratigraphic domain interpretation using multi-element geochemistry and downhole geophysical data, Maptek says.

“Domain Manager allowed rapid flagging, processing and compositing of the drillhole database while Sample Manager enabled direct transfer of validated data points to DomainMCF for modelling,” Karbishev said. “DomainMCF provided almost instantaneous 3D block models, granting the ability to rapidly analyse vast databases and test alternate scenarios.”

Superintendent Exploration Geologist for SIMEC, Ed Lynch, was keen to apply DomainMCF to specific situations in the company’s hematite and magnetite operation, to test whether it could make things easier for geologists on site and unlock more time to go out and ‘kick the rocks’.

“The complexity of the geological setting we work in presents significant challenges to our geologists when it comes to 3D modelling and grade control,” Lynch explained. “DomainMCF was simple to use and incredibly fast. When given enough data it was able to produce similar results to more traditional human-driven modelling processes. I think it is particularly suited to grade control type modelling scenarios.”

Now in its fourth year, the Maptek Geology Challenge provides an opportunity to experiment with cutting-edge technology to create models directly from raw data, that are accurate and transformative for resource modelling and production applications.

The theme for 2024 – geological control for geological models – encouraged participants to combine their expertise with the power of machine learning to create models that accurately reflect geology.

Maptek provided geological modelling tools including GeologyCore and AI-assisted DomainMCF for up to four weeks, supported with documentation and technical assistance from our global team.

Steve Sullivan, Senior Geology Specialist and Technical Lead for DomainMCF, said that the Geology Challenge was founded to inspire geologists to engage with new approaches. The challenge provides a low-risk environment to test real data with the latest technology and explore avenues for improving existing practices.

“The winning entries were strong examples of tackling problems that are difficult to solve with traditional methods, and demonstrated the use of novel techniques to control their geology,” Sullivan said. “Recommendations for software improvements have already been passed on to our development teams.”

Maptek machine learning trial points to future of mineral deposit modelling

A trial of Maptek DomainMCF at an underground metals mine has concluded that machine learning will most likely become the preferred modelling method for mineral deposits, according to the software company.

DomainMCF is a platform that Maptek says will ‘put the geology back into geologists’, applying deep learning and big data computing methods to generate domain boundaries directly from drill hole sample data. Such rapid generation of resource models is a game changer for operations, according to Maptek.

In the trial of DomainMCF, geologists at the IGO Limited Nova-Bollinger underground mine in Western Australia trialled the solution’s machine-learning tools for modelling its resource.

Nova-Bollinger is 700 km due east of Perth, with the operation mining and processing nickel-copper-cobalt sulphide ores.

Traditional resource modelling is based on a drill hole database containing 99 lithological and 11 sulphide mineralisation logging codes. From this database the mine geologists use implicit modelling to interpret 22 different domains – 21 sulphide domains and one all-encompassing waste halo domain.

The block modelling process is undertaken annually by a team of geologists on site and in the Perth corporate office, taking several months to complete. The team trialled DomainMCF in parallel to the standard workflow as part of the 2020 resource update.

The required inputs for DomainMCF are a csv file comprising drill hole database or composite data, and an optional upper and lower surface to define the spatial extents of the region to be modelled. A block model parameter file details the origin of the block model, the 3D spatial extents and the block/sub-block dimensions.

Grade estimates are done on a 6 m x 6 m x 2 m block size and sub-blocks are permitted down to a quarter of the parent block size, according to Maptek.

During the trial, three primary tests were run using different versions of the drill hole file to explore the capabilities of the application and see how they compared to the existing workflow.

Test 1 provided DomainMCF with the drill hole composite file for the 22 different domains. Six chemical elements (Ni, Cu, Co, Fe, Mg and S) were provided to assist with the training phase of the machine-learning algorithm.

For Test 2, the data used in the first test was augmented with lithology coded data from the drill hole information outside the estimation boundary limits. The chemical variables were again used to help train the algorithm.

The purpose of this test was to determine if a combined sulphide and lithological model could be produced, and to see if giving DomainMCF additional information would impact the prediction of sulphide domains.

A hands-off approach was used in Test 3 to see how DomainMCF modelled a file containing only mineralisation codes and the grouped lithology used for Test 2. None of the domain codes from Test 1 were used.

Test 3 examined if the DomainMCF model was comparable with a manually-coded domain model and whether it was useful in the mineral resource estimate process.

IGO Senior Mine Geologist, Fletcher Pym, presented the trial results in a paper to the AusIMM International Mining Geology Conference 2022 in March.

“We were able to run Test 3, which was a relatively complicated model, in 45 minutes,” Pym said.

For Pym, Test 3 also showed that machine learning can produce very comprehensive models without the strong influence of a geologist.

Because machine learning made resource modelling much faster, senior staff had more time to focus on training less experienced core loggers. Improving the processes resulted in better quality drill hole logging, according to Maptek.

Pym added: “Machine learning will become particularly attractive if the process can not only model geological domains, but also return reliable grade estimates for mine planning across the full range of mineralisation styles.

DomainMCF model section

“Providing a well-understood confidence measure can assist in risk quantification of both geology and grade.”

The study, Maptek says, highlighted several advantages of machine learning:

  • The inputs required for machine-learning processing can be readily prepared in most resource modelling software;
    Machine learning modelling times are relatively short;
  • The pay-by-use business model is more cost-effective than maintaining implicit modelling software systems;
  • The machine learning model returns an objective measure of uncertainty in the geological model, which is likely to be useful in mineral resource classification and mining reconciliation work; and
  • Multiple different geological models can be prepared in parallel, meeting the JORC requirement to investigate ‘the effect, if any, of alternative interpretations on mineral resource estimation’.

Maptek Technical Lead for DomainMCF, Steve Sullivan, says he is excited at the potential of machine learning for revolutionising resource modelling.

“I’m amazed at the response – we are already seeing companies subscribe to DomainMCF for use in domain modelling for their 2022 resource reports,” he said.

“Machine learning works best when all the available data is presented, as shown in Test 3. The more data the better.

“The industry is struggling to find experienced personnel during the current mining boom, so embedding years of experience into smart systems helps get the job done on time and under budget.”

Maptek continues to work on proposed enhancements following feedback from industry trials of DomainMCF, with grade trend prediction added in the March 2022 release.

This is an edited version of an article that appeared in Maptek’s Forge newsletter.