Remote Characterization of Dominant Wavelengths From Surface Folding on Lava Flows Using Lidar and Discrete Fourier Transform Analyses

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.