Average VNIR reflectance: A rapid, sample-free method to estimate glass content and crystallinity of fresh basaltic lava

Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2022.115084]
1University of Idaho, Department of Geological Sciences, Moscow, ID 83844, USA
2NASA Ames Research Center/ Bay Area Environmental Research Institute, Moffett Field, CA 94035, USA
3SETI Institute, Moutain View, CA, USA
4Arizona State University, Tempe, AZ, USA
5Terracon, Olathe, KS, USA
Copyright Elsevier

The microcrystalline texture in basaltic lava, scoria, and spatter can vary widely from pure glass to holocrystalline due to complex cooling histories after eruption. How quickly a molten rock cools is a function of the environmental surroundings, including water, ice, sustained heat source, and atmospheric conditions. Thus, petrologic texture serves as an indicator of cooling history captured in the rock record. As basalt is a common component of terrestrial bodies across the solar system, relating the abundance of crystalline components to spectral character would allow for a more thorough understanding of the cooling history and emplacement conditions on planetary surfaces. Visible/near-infrared (VNIR) reflectance spectroscopy has been used to examine the absorptions associated with volcanic glass, however, the non-linearity of absorption features in this spectral region requires complex spectral unmixing modeling to achieve modal percentages of minerals. Here we present evidence that average reflectance from 500 to 1000 nm (referred to as R500–1000) of solid surface samples is indicative of the crystal texture and degree of glassiness of basaltic rocks. Several factors, such as sample surface roughness, lichen cover, coatings, weathering, and chemical composition can affect the R500–1000 of a sample. However, our data indicate that these factors can be sufficiently controlled during sample selection to attribute relative glassiness values to basaltic surfaces. This quick and straightforward method requires no sample preparation or modeling and is demonstrated with training data from sixteen rocks from five basaltic flow fields with differing mineralogy, surface qualities, and geochemistry across Idaho and Oregon, USA. We further test our relationship with two published datasets of synthetic and natural basalts, as well as a subset of our own data collected with our methods to examine the sensitivities of the correlation. This method has the potential to broadly identify glassier basaltic lavas across planetary surfaces. This could be applied toward understanding lava eruption temperatures, cooling rates, magma petrogenesis, paleoclimate reconstruction, and astrobiology due to the involvement of water in quenching of lava.


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