Remote bitumen content estimation of Athabasca oil sand from hyperspectral infrared reflectance spectra using Gaussian singlets and derivative of Gaussian wavelets

Modeling of the total bitumen content, TBC, in Athabasca oil sands was undertaken on the basis of its hyperspectral reflectance spectra. Spectra (8 cm−1 resolution) were obtained that covered both the short-wave infrared and thermal infrared (TIR: 3.00–30.00 μm). Two methods, Gaussian fitting and wavelet analyses, were investigated to identify useful bitumen features as well as the best removal of the baseline. We aim to obtain the best determination of the TBC for a suitable suite of test and validation oil sands samples. The Gaussian model relied explicitly on features at 2.282 and 2.532 μm though these were only two of 10 features simultaneously fit with a quadratic baseline to the range of 2.230–2.603 μm of the spectra. The wavelet model relied on bitumen features selected at 2.274, 2.396 and 3.725 μm that could be isolated from the baseline and noise. Both models yielded similar dispersion in their estimates of TBC (±∼1–2%) while the wavelet model proved to be more robust when applied to the validation and blind data suites. We also considered the effects of using the L2 optimization (classical least-squares) and L1 optimization (minimization of largest outlier) schemes for both models. Both schemes produced similar results for the model suite of samples for TBC but the L1 was superior when applied to the validation and blind data suites. The wavelet model using the L1 optimization appeared to be quite robust producing estimates of TBC (±∼1.0–1.7%).