Mineralogy identification through linear unmixing of blast hole geochemistry

Abstract

Validating and updating geological models is important in mining to reduce the errors between the predicted and mined values. In the Banded Iron Formation-hosted iron ore deposits in the Hamersley Ranges Western Australia, geological models are based on widely spaced exploration data, resulting in errors on smaller scales. Closely spaced chemical assays from production blast hole drilling become available for updating models, but do not routinely provide mineralogy. This study identifies mineralogy by applying linear unmixing methods to chemical assays. Linear spectral mixture analysis (LSMA), extended linear mixing model (ELMM) and spectral unmixing within a multi-task GP framework (SUGP) successfully identified the mineralogy in synthetic mixtures, with RMSEs of 13.75%, 11.78% and 11.97%. LSMA had consistently poorer results. SUGP was more accurate when using fewer endmember examples. ELMM was easier to train and required less processing power. Therefore either SUGP or ELMM should be chosen to identify the mineralogy depending on the application.