Automated geological logging from FTIR reflectance spectra utilising geochemical and historical logging constraints

In iron ore exploration, mineral samples are obtained from a drill hole and their compositions logged as proportions of material types, which encapsulate mineralogical and textural attributes. This logging is used to inform the properties, i.e. mineralogical, grade, and handling, of the orebody during mining and processing. Logging has historically been recorded manually, and as such is subjective and time consuming. We have developed a system that uses objective datasets, namely Fourier Transform Infrared reflectance spectra, geochemical assays, and a priori geological constraints, within a machine learning system to objectively log these mineral samples. The system is trained on samples logged by expert geologists in a laboratory environment. The resultant objective logging optimally satisfies constraints including matching the theoretical chemistry (calculated from the logging) to the laboratory assays, and satisfying the sample's known geological context. We present comparisons of the logged material type proportions recorded by expert geologists in a laboratory environment with the proportions produced by the objective logging system, and also against geologists’ field logging proportions of the same samples. These comparisons demonstrated that the objective logging system's proportions closely matched the geochemically validated expert-logged proportions for important individual material types, and over larger mineralogical groupings, and groups of material types of different textural hardness. Additionally, the system outperformed field logging, relative to laboratory-based logging, according to r² and RMSE metrics. These results demonstrate an improvement over geologists’ field logging, while removing the subjectivity inherent in the human logging process.