Rock type classification of drill core using continuous wavelet analysis applied to thermal infrared reflectance spectra

Abstract

This study investigates a core logging methodology to map rock type using thermal infrared reflectance (TIR) spectra (500–4000 cm–1 or 2.5–20.0 μm) for 74 samples encompassing 11 rock types exposed in various mines of the Sudbury Basin, Canada. A continuous wavelet transform (CWT) was used to represent the original reflectance spectra as a suite of wavelets, each capturing spectral features of different scales with the low-scale components containing mineral spectral features and the high-scale components capturing the overall continuum. Classification was driven by the use of endmember spectra and the spectral angle mapper (SAM). Modelling and validation suites were developed and the mapping accuracy evaluated iteratively for random data splits. The results were compared for reflectance and wavelets of low components of power and significance. We found that the variability amongst measurements observed for varying orientation of a sample or due to variable surface roughness can be greatly minimized with the use of low-scale components, thus improving rock type classification. The average accuracy computed for the 11 rock types is highest for the low-scale component of power (72%) data as opposed to the reflectance data (55%). The highest average accuracy per rock type is obtained using the low-scale components (average value of 82%) for seven rock units that are relatively texturally homogeneous and of uniform modal mineralogy. Lower accuracy values are observed for rock units that display pronounced textural heterogeneity at the scale of observation, or variability in modal mineralogy, or are spectrally similar to other rock types.