Anomaly detection in Fourier transform infrared spectroscopy of geological specimens using variational autoencoders

Fourier Transform infrared spectroscopy (FTIR) is an emerging cost effective and rapid mineralogical characterization technique being applied in the geosciences. Detecting anomalous FTIR spectra is especially relevant to the geoscience domain, as it may indicate abrupt changes in geology or mineralogical composition of the rock sample being examined. Given a large volume of data, detecting anomalies that exhibit significant and abrupt spatial and compositional variability is a time-consuming and challenging task. This paper explores the use of an unsupervised variational autoencoder (VAE) for determining anomalies that may exist within a set of FTIR spectra collected from reverse circulation (RC) drill chip samples spanning several iron ore deposits from the Pilbara region in Western Australia. Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) were measured from 1,579 two-metre composite samples. Our results showed that the VAE was effective in separating anomalous spectra from spectra typical of unmineralized banded iron formation by leveraging the probabilistic latent representation of the spectra in as few as two latent dimensions. To validate our results, detected anomalous samples were compared with their respective geochemical assays to analyse their mineralogical differences, which may have led to the anomalous spectra. In the iron ore sample data used in this study, the observed spectral anomalies were shown to have elevated concentrations of Al2O3 and TiO2 wt.% while being several standard deviations below the mean Fe2O3 wt.% indicating mineralogies rich in shale as opposed to iron oxide rich mineralogies. While the paper demonstrates the efficacy of the VAE in anomaly detection, it can also be effective in assuring the quality of the FTIR data as a pre-processing step, which is critically important for machine learning applications.