Quantitative mineral identification

What’s in your rocks?

Quantifying common rock-forming minerals in routine mineral exploration has historically been challenging. While infrared spectral mineralogy has supported applied geoscience, its use has been largely qualitative and limited to hydrous mineral phases. To overcome the limitations of quantitative applications, ALS uses machine learning algorithms trained on an extensive library of geological materials. This approach enables accurate predictions of quantitative mineralogy using multi-band infrared spectra and high quality multi-element geochemical data.