GSA PLANETARY GEOLOGY DIVISION G.K. GILBERT LECTURE: THE FUTURE OF SPECTROSCOPY
Critical for these new ML approaches will be creation of improved reference datasets that augment and mitigate some of the inadequacies of existing databases, which may have impure and/or poorly characterized samples that can confound automated approaches. Moreover, there are very few adequate spectral data on mixtures. Remedying these shortcomings will require major effort but the potential benefits are enormous. ML approaches including manifold alignment can assist with uniting disparate data and identifying/removing useless outliers.
Several types of spectroscopy have been shown to benefit from ML approaches. Baseline/continuum removal can now be automated and optimized for any data set or application. Fe X-ray absorption studies have long used the pre-edge feature in the FeKα absorption edge for Fe3+/ΣFe measurements, yet ML-identified features in the main edge produce far more quantitative results. Automated whole spectrum matching approaches dramatically improve the accuracy of mineral identification in Raman spectroscopy over single peak matching methods. Multivariate analysis is required for predicting laser-induced breakdown spectra in geologic materials. Finally, ML derivations of olivine and pyroxene compositions/ratios from reflectance outperform existing approaches.