MINING SOFTWARE
beginning of the value chain and reconciled at the end : from geological modelling through to the execution of tasks and operations .
“ Many companies are starting to execute on such data-management strategies , allowing them to access and gain more insight into their data ,” he said . “ We ’ re increasingly seeing Edge devices capturing data and bringing it back to the office where it ’ s transformed into information for more proactive decisions .
“ IoT , cloud computing , AI , predictive analytics and , ultimately , automation are all playing a role in this shift towards the connected ecosystems strategy .”
Maptek ’ s Coloma sees network advancements as integral to the increased uptake of automation in the industry .
“ In the next five to 10 years , mining will see a massive consolidation of automated tasks , supported by on-demand data analytics and seamless data networks ,” he said . “ The uptake of 5G technology will facilitate adoption of this type of development and access to its benefits .”
Solutions such as Maptek Resource Tracking ( MRT ), the company ’ s live material tracking and reconciliation system , will take advantage of this increased bandwidth . MRT supports operational business improvement by delivering data in-shift where and when it can be acted on , according to Coloma .
He added : “ While there ’ s automation to increase speed in this process , there ’ s still the capacity for geologists to see the correlations with the resource model and further refine or correct the data association .”
Modular Mining ’ s solutions have been interacting with autonomous vehicles for close to a decade , with Komatsu ’ s FrontRunner AHS for mining haul trucks incorporating the DISPATCH FMS as the supervisory control component of the system .
Michael Lewis , Technical Director – Technology , Komatsu , said automation goes far beyond unmanned trucks , “ extending to a broad spectrum of automated functions and machine types , across five levels of full operator versus full equipment responsibility ”.
Modular added : “ Recognising that each mine has its own unique requirements and that customer
Geostatistical simulations allow the quantification of grade variability and the exploration of various scenarios from many different resource estimation standpoints , according to Geovariances .
Yet , despite the inherent ability to tackle risk analysis , a series of factors have prevented simulations being incorporated into the mainstream chain of information processing prevalent in the sector , the applied geostatistics company says .
“ The main technical block to the widescale use of simulations has undoubtedly been performance : it still takes a length of time to produce simulation realisations in sufficient numbers to allow meaningful risk analysis to be performed . This problem is made worse when the size of the dataset increases .”
To get over this problem , the latest version of Geovariances ’ geostatistical software , Isatis . neo , introduced a high-performing simulation methodology , SPDE .
This approach , unique to Isatis . neo , addresses multiple issues users face today . It helps integrate more and more data , boost productivity and deduce an answer quickly , the company says .
“ At the same time , this outcome is enriched with an assessment of the uncertainty that can be attached to that answer ,” it added .
The tests conducted at Geovariances using Isatis . neo have shown users can obtain simulation realisations up to 50 times faster in 2D and three times faster in 3D than running the standard Turning Bands method . The key ingredient to allow that quantum leap in performance is the solving of Stochastic Partial Differential Equations , hence the name given to the new algorithm ( SPDE ).
This algorithm came from a two-year research consortium Geovariances conducted in partnership with the Center for Geostatistics from MINES ParisTech and major mining companies including Anglo American , BHP , Eramet , Kinross , Newcrest and Orano .
“ With Isatis . neo , it is also possible to extract a representative subset of simulations selected among a more extensive set to evaluate projects ,” the company said . “ Indeed , even if the production and post-processing of multiple simulation realisations are now more than ever a practical solution to many resource estimation problems , the ability to still focus on a few realisations remains very appealing to all practitioners .”
Machine-learning algorithms have been implemented into Isatis . neo to speed up processes and deal with even bigger datasets . An example is the Sample Clustering functionality , which is used to define geological domains . This is an essential step in the mineral resource modelling process , according to the company .
Alongside this , Geovariances , in late 2020 , launched a new research consortium to accommodate bigger datasets by “ putting geostatistics in the cloud ”.
A probability map of exceeding a grade cutoff derived from geostatistical simulations in Geovariance ’ s Isatis . neo
“ The aim is to speed up results and free users from computer limits regarding performance and storage capacity ,” the company said .
Geovariances is starting with the simulation functions , which will be provided as “ microservices ” or “ libraries ” to be recalled from Isatis . neo or companies ’ own in-house applications .
Another advantage is that users will be able to connect multiple software solutions in a cloud environment without the need for their interfaces , the company added .
Geovariances is additionally working on developing robust implicit geological domain modelling , with an alternative already available in Isatis . neo being the multiple-points statistics ( MPS ) methodology .
“ MPS allows modelling complex relationships between facies and geological body shapes ,” the company explained .
The principle is to mimic a reference image . This image can be an analogue if the geological environment is known or derived from knowledge of a previous mining area . From the facies model , it is easy to then derive the estimation domains , the company says .
Geovariances recently applied MPS for a senior gold mining company with the objective of the study to assess resources and quantify risk in planned extensions .
The work resulted in a realistic 3D facies model built with data from several hundred drill holes and constrained by specific geological features such as shear lenses .
“ Geovariances is striving to make these rather complex techniques accessible ,” the company said . “ It is what led us to develop Isatis . neo , a userfriendly software solution .”
32 International Mining | FEBRUARY 2021