Remote characterization of dominant wavelengths of surface folds on lava flows using Lidar and Discrete Fourier Transform analyses

Monday, 15 December 2014
Nick Deardorff, Indiana University of Pennsylvania Main Campus, Indiana, PA, United States and Katherine Cashman, University of Bristol, Bristol, United Kingdom
The crust of lava flows (of all compositions) is commonly folded into arcuate ridges, bent such that the convex ridges point down flow. In theory, the geometry of flow surface folds can be used to constrain the thickness and viscosity of the folding layer (from the fold wavelength) and the compressional stress (from the fold amplitude). Crustal thickness is controlled primarily by lava composition and extent of cooling. Therefore, lavas of more evolved compositions (higher silica content) have thicker crusts, which should generate surface folds with larger wavelengths.

We have determined the characteristic scale of surface folds using 1000m along-channel segments from Lidar-derived 3D Digital Terrain Models over a range of lava compositions (53-72 wt% SiO2). All profiles were analyzed by discrete Fourier transform (DFT) analysis in Matlab, used to determine the spatial scale of periodic surface features. The DFT periodograms produce 1D arrays of spectral density over a range of spatial frequencies, which describe the amplitude and spatial scale (wavelength) of lava surface topography. The DFT analysis allows for unbiased measurements of dominant surface fold wavelengths as well as identification of primary and secondary folds (i.e. folds within folds). Measurements of multiple fold generations are not possible from satellite images or in the field on intermediate to high silica blocky lavas.

In our analyses, strong signals of surface periodicities were found at multiple frequencies for all lava flows, indicating multiple generations of surface folds. Additionally, mafic to intermediate lavas (<60 wt% SiO2) show a positive correlation between dominant fold wavelengths and wt% silica. This correlation breaks down with high silica lavas (>65 wt% SiO2) which have a much larger range in dominant wavelength (10 - >100m). The deviation in expected dominant wavelengths for high silica flows is likely explained by effective viscosity, which is strongly influenced by lava crystallinity. Crystallization both adds solid particles to the lava, increasing the effective viscosity, and increases the silica content (and viscosity) of the remaining matrix melt. Therefore, dominant surface fold wavelengths likely increase with increasing effective viscosity, rather than more evolved compositions.