Rock Sample Surface Preparation Influences Thermal Infrared Spectra

Abstract

High-resolution laboratory-based thermal infrared spectroscopy is an up-and-coming tool in the field of geological remote sensing. Its spatial resolution allows for detailed analyses at centimeter to sub-millimeter scales. However, this increase in resolution creates challenges with sample characteristics, such as grain size, surface roughness, and porosity, which can influence the spectral signature. This research explores the effect of rock sample surface preparation on the thermal infrared spectral signatures. We applied three surface preparation methods (split, saw, and polish) to determine how the resulting differences in surface roughness affects both the spectral shape as well as the spectral contrast. The selected samples are a pure quartz sandstone, a quartz sandstone containing a small percentage of kaolinite, and an intermediate-grained gabbro. To avoid instrument or measurement type biases we conducted measurements on three TIR instruments, resulting in directional hemispherical reflectance spectra, emissivity spectra and bi-directional reflectance images. Surface imaging and analyses were performed with scanning electron microscopy and profilometer measurements. We demonstrate that surface preparation affects the TIR spectral signatures influencing both the spectral contrast, as well as the spectral shape. The results show that polished surfaces predominantly display a high spectral contrast while the sawed and split surfaces display up to 25% lower reflectance values. Furthermore, the sawed and split surfaces display spectral signature shape differences at specific wavelengths, which we link to mineral transmission features, surface orientation effects, and multiple reflections in fine-grained minerals. Hence, the influence of rock surface preparation should be taken in consideration to avoid an inaccurate geological interpretation.

Standoff Detection of Oil and Powder Mixtures at 12 Meters Using a Tunable Quantum Cascade Laser-Based System with a Close Focus Telescope and Uncooled Infrared Detector

“…The spectral structure, relative band magnitudes and wavenumber offsets of reflectance and transmittance features presented in Fig. 4 are consistent with spectra reported by Rost and coworkers 27 and Salisbury and coworkers. 28,29 Figure 5 shows the real and imaginary parts of the refractive index for quartz taken from the measurements of Papova et al 30 The strong resonance peak between 1111.1 and 1052.6 cm À1 is associated with the antisymmetric stretching vibration of Si-O-Si bridges.…”

Surface roughness of asteroid (162173) Ryugu and comet 67P/Churyumov–Gerasimenko inferred fromin situobservations

“…Generally it is assumed that planetary surfaces are covered with a particulate medium that depending on the particle size and shape influences the reflectance spectra of the observed surface (Hiroi & Peters 1991). However, with increasing spatial resolution of the spectral data the porosity and roughness of the surface material can influence the spectral contrast, spectral shape and the absolute reflectance/emission due to additional volume scattering and cavity effects (Rost et al 2018) and should be taken into account in order to avoid a misinterpretation. In addition, the roughness on scales that cause shadowing effects or small scale self-heating have to be considered when interpreting near-infrared and thermal measurements (Davidsson et al 2009).…”

Modelling the Transmission Component in TIR Reflectance Spectra of Sandstones to Understand the Effect of Surface Roughness and Clinging Fines

“…In this section we give a short description of the characteristics of the two sandstones used for this research, followed by a description of the spectral reflectance measurements and results from Rost et al. (2018, Figure 2) and the transmission measurements and the scanning electron microscopy images obtained for this research. At the end of this section, we present our model.…”

“…In the context of laboratory‐based geological spectroscopy, several researchers showed that surface roughness of rocks influences thermal infrared (TIR) spectral signatures, both in spectral shape and in amplitude (Hardgrove et al., 2016; Kirkland et al., 2002, 2003; Osterloo et al., 2012; Rost et al., 2018; Salisbury & Wald, 1992). The shape and amplitude (Figure 1) in thermal infrared (TIR) spectra play a critical role in determining the mineral composition of rock samples and drill cores.…”