Compositional Characterization of Glassy Volcanic Material From VSWIR and MIR Spectra Using Partial Least Squares Regression Models

Abstract

AbstractThe glass phase in volcanic rocks presents a challenge to obtaining compositional data from visible and short‐wave‐infrared (VSWIR) and mid‐infrared (MIR) spectral data of remote surfaces due to its amorphous structure and variable composition. Nonetheless, glass is a common phase in volcanic materials because it forms via the rapid quench of magma and can constitute up to the entirety of a volcanic deposit. Use of partial least squares regression (PLS) to predict glass contents creates models that are insensitive to viewing geometry and sample conditions such as grain size and spectrally inactive compositional variables, enhancing the ability to detect glasses with remote sensing. PLS models are used here to predict crystallinity and oxide composition of samples from VSWIR and MIR spectral data using training spectra from natural volcanic rocks and geologically relevant synthetic samples. Three spectral resolutions of VSWIR and MIR spectra (1, 10, and 100 nm/band, and 1.9, 19, and 190 cm−1/band, respectively) were tested to assess the effects of collection configuration on different spectrometers. PLS models trained on 1 nm and 1.9 cm−1 data sets have the lowest uncertainties of glass modal abundance for VSWIR and MIR, respectively. MIR models predicting sample wt. % SiO2 and FeO, and VSWIR models of wt. % FeO provide accurate estimates (e.g., RMSE‐P of 3.4 wt. % FeO) at all spectral resolutions. Results are based on training data sets skewed to mafic compositions, which affects model accuracies.