Carl runs the OSNACA Project at the University of Western Australia. It is an open source project where all types of different hypogene ore samples from around the world have been analysed for a standard suite of elements at Bureau Veritas. Consistent 65-element data for over a thousand samples have been used to create a model that quantifies multielement metal signatures for each sample. One product from the model is a simplified "Magmato-Hydrothermal Space" where the distribution of varying metal signatures can be mapped (figure opposite). All other existing descriptions of metal signature are qualitative; e.g., Au-As-Sb-Ag-Te orogenic gold mineralisation and cannot be mapped with respect to each other. Industry applications of the OSNACA transform, thus far, are twofold.
To download the publicly available data and for other resources related to the project, go to:
https://www.cet.edu.au/project/the-osnaca-project/ or come and see Carl to discuss how the OSNACA transform can be used to advance your project.
Northern Star kindly made data for a section through the Golden Mile public, but redacted Au. Without Au to define grade (and be part of the signature), a weighted sum of pathfinder elements was used as a proxy for grade. The OSNACA transform was then calculated for each mineralised sample.
The averaged profiles above are for five populations that were determined by K-means clustering, an algorithm that isolates groups of proximal (similar) samples in multi-dimensional space. Populations are named according to distinctive features on their profile and their "grade" according to their absoulte pathfinder
element abundance (see definition of "grade" below).
Each population has a reasonably consistent set of individual profiles, particularly when compared to other populations below.
In the absence of Au data, a proxy for grade has been calculated which is the sum of squares for each element score PRIOR to scaling. In the final output, OSNACA scores are scaled so that the coordinates for each sample are independent of grade.
High-grade and low-grade are named according to their position on this plot, high grade standing out clearly from the rest.
The green and blue populations can be predicted to have a sericite-paragonite-chlorite alteration assemblage, whereas the other groups will have a sericite-paragonite-albite assemblage. In that second group, the red and pink populations are more biased towards the sericite node.
A cross section shows that each metal signature population defines a discrete spatial domain. The Lowest Grade population is almost entirely distal and on the western part of the section, whereas the Base Metal population defines a central, steep west-dipping, barrier and a distal zone to the east. The low-grade signature defines scattered mineralisation east of the base metal barrier, whereas High Grade and Ag occupy a more densly mineralised domain immediately west of the base metal barrier.
A more precise understanding of metal signature is clearly helpful for navigating around a mineral system and predicting where the highest grade domains are likely to be. Each mineral system modelled so far defines precise domains within the ore body that are not readily apparent in grade models. They have all defined a "master signature" that has a median grade up to five times higher than the others.
Adding a quantifiable metal signature model to grade, structural, stratigraphic, alteration and other available models will advance a project in three ways:
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