Characterization of bedform morphology generated under combined flows and currents using wavelet analysis

Cataño-Lopera, Yovanni A.; Abad, J. D.; García, Marcelo H.

doi:10.1016/j.oceaneng.2009.01.014

Cited by 35 publications

(23 citation statements)

References 62 publications

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“…The present method could be advanced using Fourier filtering to find the frequency band most strongly related to individual substrates. Alternatively, wavelet analysis on fields [ Buscombe , ] of backscatter and bathymetry [ Cataño‐Lopera et al , ] could reveal both the strength of the periodic components in the data as well as the locations associated with these frequency bands. The choice of alternative classification techniques could also be beneficial, as long as classification results can still be expressed and understood in physical units rather than statistical quantities, and uncertainties can be quantified in a straightforward manner.…”

Section: Discussionmentioning

confidence: 99%

Characterizing riverbed sediment using high-frequency acoustics: 2. Scattering signatures of Colorado River bed sediment in Marble and Grand Canyons

Buscombe

Grams

Kaplinski

2014

J. Geophys. Res. Earth Surf.

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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.

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Section: Discussionmentioning

confidence: 99%

Characterizing riverbed sediment using high-frequency acoustics: 2. Scattering signatures of Colorado River bed sediment in Marble and Grand Canyons

Buscombe

Grams

Kaplinski

2014

J. Geophys. Res. Earth Surf.

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“…Results from both studies illustrate the potential of signal processing methods for characterizing the morphology, size, and spatial variability of bedforms objectively. Moreover, Cataño-Lopera et al (2009) point out that this type of analysis can be used to compare bedform characteristics generated from computer models with those generated in flumes or under natural conditions. Objective methods that can be used to gauge model output are essential for refining the representativeness of the latter (Baas and Nield, 2010).…”

Section: Challengesmentioning

confidence: 99%

Remote sensing and spatial analysis of aeolian sand dunes: A review and outlook

Hugenholtz

Levin

Barchyn

et al. 2012

Earth-Science Reviews

177

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“…[8] Wavelet transforms were developed to overcome the limitations of Fourier transforms and have been applied to fluid mechanics in the isolation of coherent structures in turbulent flows [Farge, 1992], in analyzing the temporal variability of coherent convective storm structures [Kumar and Foufoula-Georgiou, 1993], within investigation of longterm land temperature/climate series [Baulinas, 1997], in analyzing oxygen isotopic ratios from marine sediments [Prokoph and Veizer, 1999], and in analyzing the local curvature of meanders [Abad, 2009]. Some recent applications of 1-D wavelets in sedimentology encompass temporal variations within streamflow and sediment loads [Rossi et al, 2009], characterization of bed form morphology [Catano-Lopera et al, 2009;Singh et al, 2011], sediment concentration distributions [Felix et al, 2005], the recognition of patterns in seabed morphology [Little, 1994], analysis of riverbed roughness [Nyander et al, 2003], and investigation of flow structure over alluvial sand dunes [Shugar et al, 2010]. Herein we demonstrate that this technique identifies the various scales of bed forms present within a series and significantly improves the quantification of form roughness at different bed form scales.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of bed form scales using robust spline filters and wavelet transforms: Methods and application to synthetic signals and bed forms of the Río Paraná, Argentina

et al. 2013

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[1] There is no standard nomenclature and procedure to systematically identify the scale and magnitude of bed forms such as bars, dunes, and ripples that are commonly present in many sedimentary environments. This paper proposes a standardization of the nomenclature and symbolic representation of bed forms and details the combined application of robust spline filters and continuous wavelet transforms to discriminate these morphodynamic features, allowing the quantitative recognition of bed form hierarchies. Herein the proposed methodology for bed form discrimination is first applied to synthetic bed form profiles, which are sampled at a Nyquist ratio interval of 2.5-50 and a signal-to-noise ratio interval of 1-20 and subsequently applied to a detailed 3-D bed topography from the Río Paraná, Argentina, which exhibits large-scale dunes with superimposed, smaller bed forms. After discriminating the synthetic bed form signals into three-bed form hierarchies that represent bars, dunes, and ripples, the accuracy of the methodology is quantified by estimating the reproducibility, the cross correlation, and the standard deviation ratio of the actual and retrieved signals. For the case of the field measurements, the proposed method is used to discriminate small and large dunes and subsequently obtain and statistically analyze the common morphological descriptors such as wavelength, slope, and amplitude of both stoss and lee sides of these different size bed forms. Analysis of the synthetic signals demonstrates that the Morlet wavelet function is the most efficient in retrieving smaller periodicities such as ripples and smaller dunes and that the proposed methodology effectively discriminates waves of different periods for Nyquist ratios higher than 25 and signal-to-noise ratios higher than 5. The analysis of bed forms in the Río Paraná reveals that, in most cases, a Gamma probability distribution, with a positive skewness, best describes the dimensionless wavelength and amplitude for both the lee and stoss sides of large dunes. For the case of smaller superimposed dunes, the dimensionless wavelength shows a discrete behavior that is governed by the sampling frequency of the data, and the dimensionless amplitude better fits the Gamma probability distribution, again with a positive skewness. This paper thus provides a robust methodology for systematically identifying the scales and magnitudes of bed forms in a range of environments.

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Characterization of bedform morphology generated under combined flows and currents using wavelet analysis

Cited by 35 publications

References 62 publications

Characterizing riverbed sediment using high-frequency acoustics: 2. Scattering signatures of Colorado River bed sediment in Marble and Grand Canyons

Characterizing riverbed sediment using high-frequency acoustics: 2. Scattering signatures of Colorado River bed sediment in Marble and Grand Canyons

Remote sensing and spatial analysis of aeolian sand dunes: A review and outlook

Discrimination of bed form scales using robust spline filters and wavelet transforms: Methods and application to synthetic signals and bed forms of the Río Paraná, Argentina

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