Mineral Identification

In order to determine minerology, geologists have traditionally used a variety of different features including colour, hardness, crystal habit, magnetic susceptibility, etc.

Our technology works in a similar manner, relying on data from multiple tools working in unison to determine mineralogy and key rock properties. Advanced artificial intelligence allows us to treat measurements of different types and scales as a single integrated data set, unlocking new capabilities beyond the state-of-the-art of each individual sensor and identifying minerals without a unique signature from any one sensor technology. Using a combination of high-resolution photography, hyperspectral, XRF, and other sensors we enable an understanding of the rocks greater than the sum of its parts.

Above is an image showing how we can hand-train AI. In this example, we identify biotite in core, and let our AI do the rest. This allows us to transcend from only a few pixels of hyperspectral information to millions of pixels in a library relatively quickly.