Image and point data fusion for enhanced discrimination of ore and waste in mining

Sensor technologies provide relevant information on the key geological attributes in mining. The integration of data from multiple sources is advantageous in making use of the synergy among the outputs for the enhanced characterisation of materials. Sensors produce various types of data. Thus, the fusion of these data requires innovative data...

Fusion of Mid-Wave Infrared and Long-Wave Infrared Reflectance Spectra for Quantitative Analysis of Minerals

Accurate quantitative mineralogical data has significant implications in mining operations. However, quantitative analysis of minerals is challenging for most of the sensor outputs. Thus, it requires advances in data analytics. In this work, data fusion approaches for integrating datasets pertaining to the mid-wave infrared (MWIR) and long-wave...

Data Fusion for the Prediction of Elemental Concentrations in Polymetallic Sulphide Ore Using Mid-Wave Infrared and Long-Wave Infrared Reflectance Data

The increasing availability of complex multivariate data yielded by sensor technologies permits qualitative and quantitative data analysis for material characterization. Multivariate data are hard to understand by visual inspection and intuition. Thus, data-driven models are required to derive study-specific insights from large datasets. In the...

Chemometric Analysis of Mid-Wave Infrared Spectral Reflectance Data for Sulphide Ore Discrimination

Despite significant recent advancements in the sensor technologies, the use of sensors for raw material characterization in the mining industry remains limited. The aim of the present study was to assess the utility of applying the mid-wave infrared (MWIR) reflectance data acquired by the use of a handheld Fourier-transform infrared...