Remote Characterization of Dominant Wavelengths From Surface Folding on Lava Flows Using Lidar and Discrete Fourier Transform Analyses. (8th August 2019) - Deardorff, Nicholas; Booth, Adam; Cashman, Katharine
Abstract: Surface folding is common in lava flows of all compositions and is believed to be due to changes in viscosity and flow velocity between the cooling crust and the more fluid flow interior. However, our understanding of the relationship between surface folding and flow rheology is incomplete. In this study we analyze digital terrain models of eight lava flows ranging in composition from basaltic andesite to rhyolite using a discrete Fourier transform analysis to quantitatively determine dominant surface fold wavelengths. Our discrete Fourier transform analyses show that each lava flow has multiple fold generations and that dominant wavelengths are more closely related to calculated effective viscosity than to lava composition. At our Oregon sites, average dominant wavelengths generally increase with viscosity ( r 2 =0.68), and the correlation improves ( r 2 =0.87) when expanded by including previously measured fold wavelengths and viscosities from the global database. However, there are a few exceptions to this positive trend where a few lava flows have lower or higher than expected dominant fold wavelengths, which we infer are due to secondary factors such as differences in eruption conditions (eruption rate, temperature, etc.). Additionally, over a 5 order of magnitude range in viscosity, there is significant overlap between the ranges of fold wavelengths, particularly from 10 to 20m, for lavas from basaltic andesite to rhyolite, making it difficult to determine a numeric correlation between surface folds and lava rheology that would allow remote characterization of lava. Plain Language Summary: Folding of the surface crust of lava flows is common and is believed to be due to the interior of the lava flow flowing more easily and quickly than the lava crust, which is much stiffer and more brittle. However, our understanding of the relationship between surface folding and how lava flows and deforms is incomplete. In this study we analyze three‐dimensional digital models of the surfaces of lava flows over a range in composition. Our analyses look at patterns of periodicities within the lave surfaces, which can detect differences in roughness between lava flows as well as the size and distance between surface folds. Our findings show that typically lavas that are thick and sticky, flowing less easily, have larger fold wavelengths (distances between folds) and lavas that flow more easily have smaller fold wavelengths. This type of analysis provides a versatile tool for characterizing lava flows without ever visiting the location physically and has significant implications for possible future analyses of volcanic terrains on Earth in hard to reach locations and on other planets and moons. Key Points: Discrete Fourier transform (DFT) analyses can be used to extract dominant wavelengths of lava flow surface folds from digital terrain models Average dominant wavelengths determined through discrete Fourier transform (DFT) analyses generally increase with viscosity DFT power spectral densities correlate with lava profile roughness, and dominant fold wavelength is closely related to lava surface roughness