Characterizing riverbed sediment using high‐frequency acoustics: 2. Scattering signatures of Colorado River bed sediment in Marble and Grand Canyons. Issue 12 (19th December 2014)

• Record Type: Journal Article
• Authors: Buscombe, D.; Grams, P. E.; Kaplinski, M. A.
• Is Part Of: Journal of geophysical research. Volume 119:Issue 12(2014)
• Page Start: 2692
• Page End: 2710
• Publication Date: 2014-12-19
• Subjects: Geomorphology -- Periodicals; 551.3
• DOI: 10.1002/2014JF003191
• Languages: English
• ISSNs: 2169-9003

Abstract

<abstract abstract-type="main" id="jgrf20336-abs-0001"> <title>Abstract</title> <p id="jgrf20336-para-0001">In this, the second of a pair of papers on the statistical signatures of riverbed sediment in high‐frequency acoustic backscatter, spatially explicit maps of the stochastic geometries (length and amplitude scales) of backscatter are related to patches of riverbed surfaces composed of known sediment types, as determined by georeferenced underwater video observations. Statistics of backscatter magnitudes alone are found to be poor discriminators between sediment types. However, the variance of the power spectrum and the intercept and slope from a power law spectral form (termed the spectral strength and exponent, respectively) successfully discriminate between sediment types. A decision tree approach was able to classify spatially heterogeneous patches of homogeneous sands, gravels (and sand‐gravel mixtures), and cobbles/boulders with 95, 88, and 91% accuracy, respectively. Application to sites outside the calibration and surveys made at calibration sites at different times were plausible based on observations from underwater video. Analysis of decision trees built with different training data sets suggested that the spectral exponent was consistently the most important variable in the classification. In the absence of theory concerning how spatially variable sediment surfaces scatter high‐frequency sound, the primary advantage of this data‐driven approach to classify bed sediment over alternatives is that spectral methods have well‐understood properties and make no assumptions about the distributional form of the fluctuating component of backscatter over small spatial scales.</p> </abstract>