Geological domaining is a crucial step in the resource modelling process. However, domains can be challenging to identify when grade populations are overly intricate.
Sibanye-Stillwater faced one such challenge for a PGM (platinum group metals) deposit with complex low-grade and very high-grade zones and several faults. They generated two domains that successfully separated the two grade populations and improved the block model’s quality.
Sibanye-Stillwater is a global mining and metals processing group with a diverse portfolio of projects and investments spanning multiple countries.
Established in 2013, they currently operate in Argentina, Australia, Finland, France, India, South Africa, the USA, and Zimbabwe. Specialising in a variety of commodities, Sibanye-Stillwater’s portfolio includes gold, platinum, copper, zinc, lead, and lithium.
Sibanye-Stillwater is working on a PGM orebody extension project close to its Stillwater and East Boulder mines in Montana.
The project presents extremely high-grades that are distributed sporadically throughout the deposit interspersed with lower-grade material and intersected by fault lines. These factors contribute to the complexity of establishing distinct domains.
Isatis.neo offers a powerful sample clustering tool based on geostatistical hierarchical clustering.
It automatically groups borehole samples into homogeneous classes based on several continuous and categorical variables. Users can select the number of classes they want data to be grouped into, the variables used for the classification, and the weights to assign them.
Antonio Umpire, who is familiar with Isatis.neo and its predecessor, Isatis, utilised the sample clustering tool to categorise samples into a specific number of domains.
This approach enabled him to quickly and accurately pinpoint two distinct groups of samples: with low values and with extremely high values.
The Isatis.neo clustering tool automatically groups samples according to their similarity in different variables. In this case, four classes were used for the clustering analysis. Clusters are displayed as a dendrogram.
2D cross-section showing the domaining in four classes, defined by Isatis.neo’s automated sample clustering. The west part clearly identifies the lower grades.
Domaining using the “undiluted horizontal width” variable validates domaining computed only with PTPD data.
Antonio applied a similar principle in another mine block to determine optimised capping values for two grade populations, which also had a complex distribution This technique enabled him to efficiently and objectively separate samples with normal grades from those with extremely high grades and define a suitable capping value for each population.
Antonio Umpire – Unit Manager Group Resource Estimation & Reporting
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