Topographic and Acoustic Estimates of Grain-Scale Roughness from High-Resolution Multibeam Echo-Sounder: Examples from the Colorado River in Marble and Grand Canyons.

Thursday, 18 December 2014: 4:15 PM
Daniel Buscombe and Paul E Grams, USGS Grand Canyon Monitoring and Research Center, Flagstaff, AZ, United States
High-frequency (several hundred kilohertz) multibeam echo-sounder (MBES) systems have the potential to provide complete coverage of large areas (km2) of the bed, rapidly (mins to hrs), at high resolution (cm2), and with high positional accuracy (cm). Here, we explore the use of MBES data to estimate grain-scale roughness of submerged riverbed sediment. There are two broad approaches: 1) using digital elevation models constructed from depth soundings, and 2) using acoustic backscatter. We discuss the relative merits of both approaches using examples from data collected on the Colorado River in Marble and Grand Canyons, Arizona, USA.

The primary advantage of acoustic backscatter over topography from soundings, for the purposes of sediment classification, is the potential to distinguish between sediment at a higher resolution. This is because soundings are point measurements, whereas a recorded backscatter magnitude is the integral of backscattered sound from all scatterers in the insonified area. In addition, this acoustic return contains information about both the roughness and the hardness/impedance of the sediment. The statistics of backscatter magnitudes alone are found to be a poor discriminator between sediment types perhaps because, using our 400 kHz system, the scattering regime changes from Rayleigh (sound scattering by particles smaller than the sound wavelength) for fine sand, to geometric (scattering by larger-than-sound-wavelength particles) for substrates coarser than sand. However, simple measures derived from backscatter power spectra (namely, the variance, integral lengthscale, and the intercept and slope from a power-law form - see Figure) are found to distinguish between patches of sand, gravel, cobbles and boulders. Using this dependence, we present a new data-driven approach to classify grain-scale roughness, developed by comparing the spectral properties of backscatter with bed-sediment observations using geo-referenced underwater video.